Date: 2019-12-26 01:22:51 CET, cola version: 1.3.2
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All available functions which can be applied to this res_list object:
res_list
#> A 'ConsensusPartitionList' object with 24 methods.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows are extracted by 'SD, CV, MAD, ATC' methods.
#> Subgroups are detected by 'hclust, kmeans, skmeans, pam, mclust, NMF' method.
#> Number of partitions are tried for k = 2, 3, 4, 5, 6.
#> Performed in total 30000 partitions by row resampling.
#>
#> Following methods can be applied to this 'ConsensusPartitionList' object:
#> [1] "cola_report" "collect_classes" "collect_plots" "collect_stats"
#> [5] "colnames" "functional_enrichment" "get_anno_col" "get_anno"
#> [9] "get_classes" "get_matrix" "get_membership" "get_stats"
#> [13] "is_best_k" "is_stable_k" "ncol" "nrow"
#> [17] "rownames" "show" "suggest_best_k" "test_to_known_factors"
#> [21] "top_rows_heatmap" "top_rows_overlap"
#>
#> You can get result for a single method by, e.g. object["SD", "hclust"] or object["SD:hclust"]
#> or a subset of methods by object[c("SD", "CV")], c("hclust", "kmeans")]
The call of run_all_consensus_partition_methods() was:
#> run_all_consensus_partition_methods(data = mat, mc.cores = 4)
Dimension of the input matrix:
mat = get_matrix(res_list)
dim(mat)
#> [1] 18041 126
The density distribution for each sample is visualized as in one column in the following heatmap. The clustering is based on the distance which is the Kolmogorov-Smirnov statistic between two distributions.
library(ComplexHeatmap)
densityHeatmap(mat, ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 4)
Folowing table shows the best k (number of partitions) for each combination
of top-value methods and partition methods. Clicking on the method name in
the table goes to the section for a single combination of methods.
The cola vignette explains the definition of the metrics used for determining the best number of partitions.
suggest_best_k(res_list)
| The best k | 1-PAC | Mean silhouette | Concordance | Optional k | ||
|---|---|---|---|---|---|---|
| MAD:skmeans | 2 | 1.000 | 0.971 | 0.987 | ** | |
| SD:kmeans | 2 | 0.983 | 0.953 | 0.972 | ** | |
| MAD:kmeans | 2 | 0.983 | 0.948 | 0.977 | ** | |
| ATC:skmeans | 4 | 0.971 | 0.918 | 0.967 | ** | 2,3 |
| SD:NMF | 2 | 0.966 | 0.933 | 0.973 | ** | |
| SD:skmeans | 3 | 0.961 | 0.917 | 0.959 | ** | 2 |
| ATC:mclust | 6 | 0.947 | 0.931 | 0.962 | * | |
| ATC:pam | 6 | 0.911 | 0.831 | 0.924 | * | |
| CV:skmeans | 6 | 0.906 | 0.861 | 0.920 | * | 5 |
| MAD:NMF | 2 | 0.901 | 0.930 | 0.970 | * | |
| CV:NMF | 5 | 0.878 | 0.850 | 0.939 | ||
| MAD:pam | 2 | 0.830 | 0.865 | 0.939 | ||
| CV:mclust | 4 | 0.827 | 0.909 | 0.946 | ||
| CV:pam | 6 | 0.783 | 0.791 | 0.894 | ||
| ATC:NMF | 2 | 0.780 | 0.872 | 0.946 | ||
| SD:pam | 2 | 0.759 | 0.871 | 0.938 | ||
| ATC:hclust | 6 | 0.686 | 0.779 | 0.855 | ||
| ATC:kmeans | 2 | 0.612 | 0.897 | 0.929 | ||
| MAD:mclust | 4 | 0.550 | 0.696 | 0.832 | ||
| SD:mclust | 4 | 0.530 | 0.524 | 0.797 | ||
| SD:hclust | 5 | 0.489 | 0.520 | 0.648 | ||
| CV:kmeans | 2 | 0.325 | 0.755 | 0.835 | ||
| MAD:hclust | 3 | 0.292 | 0.469 | 0.684 | ||
| CV:hclust | 4 | 0.284 | 0.491 | 0.678 |
**: 1-PAC > 0.95, *: 1-PAC > 0.9
Cumulative distribution function curves of consensus matrix for all methods.
collect_plots(res_list, fun = plot_ecdf)
Consensus heatmaps for all methods. (What is a consensus heatmap?)
collect_plots(res_list, k = 2, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = consensus_heatmap, mc.cores = 4)
Membership heatmaps for all methods. (What is a membership heatmap?)
collect_plots(res_list, k = 2, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = membership_heatmap, mc.cores = 4)
Signature heatmaps for all methods. (What is a signature heatmap?)
Note in following heatmaps, rows are scaled.
collect_plots(res_list, k = 2, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 3, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 4, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 5, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 6, fun = get_signatures, mc.cores = 4)
The statistics used for measuring the stability of consensus partitioning. (How are they defined?)
get_stats(res_list, k = 2)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 2 0.966 0.933 0.973 0.459 0.547 0.547
#> CV:NMF 2 0.679 0.889 0.921 0.478 0.517 0.517
#> MAD:NMF 2 0.901 0.930 0.970 0.461 0.533 0.533
#> ATC:NMF 2 0.780 0.872 0.946 0.450 0.537 0.537
#> SD:skmeans 2 1.000 0.975 0.989 0.493 0.506 0.506
#> CV:skmeans 2 0.647 0.805 0.914 0.498 0.511 0.511
#> MAD:skmeans 2 1.000 0.971 0.987 0.492 0.506 0.506
#> ATC:skmeans 2 1.000 0.974 0.990 0.501 0.499 0.499
#> SD:mclust 2 0.258 0.516 0.740 0.339 0.511 0.511
#> CV:mclust 2 0.246 0.668 0.805 0.339 0.801 0.801
#> MAD:mclust 2 0.189 0.145 0.712 0.394 0.853 0.853
#> ATC:mclust 2 0.744 0.813 0.924 0.436 0.537 0.537
#> SD:kmeans 2 0.983 0.953 0.972 0.476 0.521 0.521
#> CV:kmeans 2 0.325 0.755 0.835 0.449 0.552 0.552
#> MAD:kmeans 2 0.983 0.948 0.977 0.474 0.529 0.529
#> ATC:kmeans 2 0.612 0.897 0.929 0.473 0.514 0.514
#> SD:pam 2 0.759 0.871 0.938 0.494 0.498 0.498
#> CV:pam 2 0.307 0.713 0.844 0.445 0.547 0.547
#> MAD:pam 2 0.830 0.865 0.939 0.495 0.497 0.497
#> ATC:pam 2 0.463 0.859 0.899 0.445 0.521 0.521
#> SD:hclust 2 0.560 0.796 0.906 0.403 0.618 0.618
#> CV:hclust 2 0.552 0.827 0.903 0.263 0.689 0.689
#> MAD:hclust 2 0.308 0.772 0.877 0.407 0.643 0.643
#> ATC:hclust 2 0.509 0.776 0.886 0.467 0.497 0.497
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.746 0.830 0.929 0.442 0.722 0.522
#> CV:NMF 3 0.650 0.838 0.898 0.381 0.706 0.490
#> MAD:NMF 3 0.682 0.772 0.903 0.449 0.736 0.532
#> ATC:NMF 3 0.705 0.830 0.927 0.314 0.713 0.526
#> SD:skmeans 3 0.961 0.917 0.959 0.345 0.752 0.545
#> CV:skmeans 3 0.800 0.848 0.913 0.343 0.676 0.445
#> MAD:skmeans 3 0.630 0.717 0.848 0.350 0.783 0.590
#> ATC:skmeans 3 0.900 0.864 0.937 0.316 0.768 0.566
#> SD:mclust 3 0.167 0.346 0.617 0.565 0.720 0.536
#> CV:mclust 3 0.538 0.858 0.889 0.623 0.635 0.555
#> MAD:mclust 3 0.280 0.463 0.733 0.398 0.476 0.410
#> ATC:mclust 3 0.585 0.720 0.829 0.346 0.656 0.464
#> SD:kmeans 3 0.468 0.625 0.776 0.347 0.704 0.485
#> CV:kmeans 3 0.537 0.807 0.818 0.387 0.724 0.527
#> MAD:kmeans 3 0.424 0.587 0.799 0.353 0.711 0.496
#> ATC:kmeans 3 0.544 0.671 0.790 0.331 0.712 0.511
#> SD:pam 3 0.439 0.678 0.805 0.250 0.821 0.661
#> CV:pam 3 0.324 0.563 0.796 0.287 0.756 0.602
#> MAD:pam 3 0.504 0.712 0.836 0.255 0.835 0.683
#> ATC:pam 3 0.669 0.710 0.823 0.361 0.809 0.659
#> SD:hclust 3 0.321 0.476 0.710 0.451 0.766 0.628
#> CV:hclust 3 0.187 0.466 0.761 0.756 0.850 0.786
#> MAD:hclust 3 0.292 0.469 0.684 0.457 0.682 0.515
#> ATC:hclust 3 0.458 0.678 0.722 0.260 0.918 0.839
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.664 0.746 0.851 0.0993 0.873 0.660
#> CV:NMF 4 0.663 0.651 0.812 0.1082 0.883 0.685
#> MAD:NMF 4 0.637 0.601 0.812 0.1033 0.847 0.593
#> ATC:NMF 4 0.555 0.615 0.800 0.2294 0.779 0.486
#> SD:skmeans 4 0.755 0.629 0.828 0.1255 0.898 0.706
#> CV:skmeans 4 0.768 0.754 0.863 0.1216 0.839 0.565
#> MAD:skmeans 4 0.651 0.529 0.769 0.1223 0.858 0.612
#> ATC:skmeans 4 0.971 0.918 0.967 0.1283 0.855 0.606
#> SD:mclust 4 0.530 0.524 0.797 0.3155 0.706 0.405
#> CV:mclust 4 0.827 0.909 0.946 0.2919 0.793 0.569
#> MAD:mclust 4 0.550 0.696 0.832 0.2947 0.741 0.433
#> ATC:mclust 4 0.569 0.666 0.793 0.2008 0.835 0.600
#> SD:kmeans 4 0.538 0.586 0.746 0.1287 0.813 0.530
#> CV:kmeans 4 0.568 0.712 0.813 0.1424 0.905 0.734
#> MAD:kmeans 4 0.495 0.479 0.680 0.1357 0.885 0.685
#> ATC:kmeans 4 0.567 0.422 0.626 0.1380 0.755 0.447
#> SD:pam 4 0.662 0.793 0.879 0.1406 0.883 0.705
#> CV:pam 4 0.439 0.672 0.820 0.1907 0.814 0.603
#> MAD:pam 4 0.716 0.768 0.881 0.1362 0.846 0.628
#> ATC:pam 4 0.850 0.850 0.943 0.1611 0.859 0.666
#> SD:hclust 4 0.383 0.446 0.612 0.1904 0.835 0.622
#> CV:hclust 4 0.284 0.491 0.678 0.2741 0.728 0.542
#> MAD:hclust 4 0.350 0.352 0.643 0.1770 0.696 0.395
#> ATC:hclust 4 0.480 0.671 0.756 0.1140 0.926 0.832
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.742 0.771 0.860 0.0866 0.885 0.620
#> CV:NMF 5 0.878 0.850 0.939 0.0803 0.830 0.489
#> MAD:NMF 5 0.646 0.574 0.748 0.0740 0.894 0.637
#> ATC:NMF 5 0.524 0.496 0.712 0.0576 0.815 0.461
#> SD:skmeans 5 0.782 0.790 0.868 0.0560 0.914 0.694
#> CV:skmeans 5 0.917 0.876 0.939 0.0544 0.930 0.732
#> MAD:skmeans 5 0.708 0.545 0.758 0.0585 0.950 0.813
#> ATC:skmeans 5 0.874 0.876 0.932 0.0602 0.899 0.640
#> SD:mclust 5 0.604 0.495 0.697 0.1102 0.876 0.600
#> CV:mclust 5 0.792 0.674 0.861 0.0633 0.966 0.881
#> MAD:mclust 5 0.586 0.486 0.717 0.0913 0.841 0.483
#> ATC:mclust 5 0.852 0.894 0.892 0.1013 0.917 0.705
#> SD:kmeans 5 0.601 0.620 0.706 0.0789 0.863 0.563
#> CV:kmeans 5 0.652 0.660 0.742 0.0762 0.869 0.579
#> MAD:kmeans 5 0.554 0.467 0.671 0.0759 0.813 0.454
#> ATC:kmeans 5 0.754 0.759 0.828 0.0816 0.841 0.505
#> SD:pam 5 0.709 0.683 0.861 0.1124 0.797 0.438
#> CV:pam 5 0.642 0.705 0.841 0.1089 0.912 0.732
#> MAD:pam 5 0.678 0.643 0.814 0.1152 0.819 0.472
#> ATC:pam 5 0.818 0.813 0.900 0.1097 0.858 0.565
#> SD:hclust 5 0.489 0.520 0.648 0.0837 0.834 0.518
#> CV:hclust 5 0.349 0.621 0.721 0.1067 0.829 0.593
#> MAD:hclust 5 0.429 0.427 0.628 0.0702 0.773 0.442
#> ATC:hclust 5 0.593 0.749 0.814 0.1228 0.873 0.654
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.786 0.689 0.819 0.0435 0.951 0.774
#> CV:NMF 6 0.801 0.756 0.870 0.0427 0.908 0.618
#> MAD:NMF 6 0.705 0.619 0.775 0.0427 0.922 0.675
#> ATC:NMF 6 0.562 0.472 0.696 0.0458 0.854 0.526
#> SD:skmeans 6 0.822 0.791 0.882 0.0488 0.935 0.719
#> CV:skmeans 6 0.906 0.861 0.920 0.0407 0.946 0.756
#> MAD:skmeans 6 0.792 0.684 0.827 0.0495 0.915 0.653
#> ATC:skmeans 6 0.833 0.755 0.835 0.0394 0.948 0.766
#> SD:mclust 6 0.620 0.483 0.709 0.0424 0.850 0.440
#> CV:mclust 6 0.710 0.579 0.740 0.0476 0.921 0.712
#> MAD:mclust 6 0.594 0.492 0.708 0.0386 0.897 0.582
#> ATC:mclust 6 0.947 0.931 0.962 0.0158 0.915 0.676
#> SD:kmeans 6 0.705 0.680 0.771 0.0484 0.946 0.757
#> CV:kmeans 6 0.683 0.549 0.757 0.0490 0.960 0.826
#> MAD:kmeans 6 0.662 0.587 0.734 0.0479 0.891 0.556
#> ATC:kmeans 6 0.774 0.776 0.839 0.0425 0.943 0.755
#> SD:pam 6 0.804 0.786 0.895 0.0544 0.886 0.555
#> CV:pam 6 0.783 0.791 0.894 0.0846 0.915 0.672
#> MAD:pam 6 0.778 0.742 0.870 0.0497 0.897 0.579
#> ATC:pam 6 0.911 0.831 0.924 0.0393 0.961 0.827
#> SD:hclust 6 0.526 0.485 0.621 0.0498 0.839 0.466
#> CV:hclust 6 0.467 0.575 0.748 0.0656 1.000 0.999
#> MAD:hclust 6 0.451 0.351 0.591 0.0429 0.880 0.593
#> ATC:hclust 6 0.686 0.779 0.855 0.0555 0.978 0.910
Following heatmap plots the partition for each combination of methods and the lightness correspond to the silhouette scores for samples in each method. On top the consensus subgroup is inferred from all methods by taking the mean silhouette scores as weight.
collect_stats(res_list, k = 2)
collect_stats(res_list, k = 3)
collect_stats(res_list, k = 4)
collect_stats(res_list, k = 5)
collect_stats(res_list, k = 6)
Collect partitions from all methods:
collect_classes(res_list, k = 2)
collect_classes(res_list, k = 3)
collect_classes(res_list, k = 4)
collect_classes(res_list, k = 5)
collect_classes(res_list, k = 6)
Overlap of top rows from different top-row methods:
top_rows_overlap(res_list, top_n = 1000, method = "euler")
top_rows_overlap(res_list, top_n = 2000, method = "euler")
top_rows_overlap(res_list, top_n = 3000, method = "euler")
top_rows_overlap(res_list, top_n = 4000, method = "euler")
top_rows_overlap(res_list, top_n = 5000, method = "euler")
Also visualize the correspondance of rankings between different top-row methods:
top_rows_overlap(res_list, top_n = 1000, method = "correspondance")
top_rows_overlap(res_list, top_n = 2000, method = "correspondance")
top_rows_overlap(res_list, top_n = 3000, method = "correspondance")
top_rows_overlap(res_list, top_n = 4000, method = "correspondance")
top_rows_overlap(res_list, top_n = 5000, method = "correspondance")
Heatmaps of the top rows:
top_rows_heatmap(res_list, top_n = 1000)
top_rows_heatmap(res_list, top_n = 2000)
top_rows_heatmap(res_list, top_n = 3000)
top_rows_heatmap(res_list, top_n = 4000)
top_rows_heatmap(res_list, top_n = 5000)
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "hclust"]
# you can also extract it by
# res = res_list["SD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.560 0.796 0.906 0.4026 0.618 0.618
#> 3 3 0.321 0.476 0.710 0.4508 0.766 0.628
#> 4 4 0.383 0.446 0.612 0.1904 0.835 0.622
#> 5 5 0.489 0.520 0.648 0.0837 0.834 0.518
#> 6 6 0.526 0.485 0.621 0.0498 0.839 0.466
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.0376 0.8943 0.996 0.004
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 1 0.9909 0.2702 0.556 0.444
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.1843 0.8802 0.028 0.972
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.6048 0.7904 0.148 0.852
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.1184 0.8948 0.984 0.016
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.7745 0.7266 0.772 0.228
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.7745 0.7266 0.772 0.228
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 1 0.9909 0.2702 0.556 0.444
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.1414 0.8950 0.980 0.020
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.8935 1.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.8267 0.6669 0.740 0.260
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.9323 0.4661 0.348 0.652
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 1 0.9922 0.2702 0.552 0.448
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0376 0.8939 0.996 0.004
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.1414 0.8950 0.980 0.020
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 1 0.7376 0.7522 0.792 0.208
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.8831 0.000 1.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0672 0.8937 0.992 0.008
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.4161 0.8451 0.084 0.916
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.2423 0.8911 0.960 0.040
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.8831 0.000 1.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.9970 0.0291 0.468 0.532
#> F325847E-F046-4B67-B01C-16919C401020 1 0.2778 0.8874 0.952 0.048
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.1633 0.8949 0.976 0.024
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.8831 0.000 1.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.2423 0.8911 0.960 0.040
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 1 0.7745 0.7304 0.772 0.228
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0376 0.8939 0.996 0.004
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.0376 0.8943 0.996 0.004
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.8935 1.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 1 0.9909 0.2702 0.556 0.444
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.3114 0.8850 0.944 0.056
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.1843 0.8812 0.028 0.972
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.8935 1.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.2236 0.8936 0.964 0.036
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.8935 1.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.2423 0.8911 0.960 0.040
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.2603 0.8900 0.956 0.044
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 1 0.9491 0.4761 0.632 0.368
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.1843 0.8802 0.028 0.972
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.2043 0.8937 0.968 0.032
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.2423 0.8911 0.960 0.040
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 1 0.7745 0.7304 0.772 0.228
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.2423 0.8911 0.960 0.040
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.0376 0.8939 0.996 0.004
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.1414 0.8942 0.980 0.020
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 1 0.9909 0.2702 0.556 0.444
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.2043 0.8937 0.968 0.032
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.6148 0.7974 0.848 0.152
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 1 0.8499 0.6495 0.724 0.276
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0376 0.8939 0.996 0.004
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.2948 0.8744 0.948 0.052
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.9881 0.2854 0.564 0.436
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.2236 0.8936 0.964 0.036
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.1843 0.8812 0.028 0.972
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.8935 1.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.2043 0.8933 0.968 0.032
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.8831 0.000 1.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.1184 0.8948 0.984 0.016
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.2236 0.8936 0.964 0.036
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.8935 1.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.1414 0.8942 0.980 0.020
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.8831 0.000 1.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0000 0.8935 1.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.1184 0.8948 0.984 0.016
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.8831 0.000 1.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.2236 0.8936 0.964 0.036
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0000 0.8935 1.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.2423 0.8911 0.960 0.040
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.5737 0.8026 0.136 0.864
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.8831 0.000 1.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.8935 1.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0376 0.8939 0.996 0.004
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.8499 0.6496 0.724 0.276
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.8207 0.6832 0.744 0.256
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.2423 0.8911 0.960 0.040
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0376 0.8939 0.996 0.004
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.8831 0.000 1.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.0000 0.8935 1.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.1184 0.8959 0.984 0.016
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.1633 0.8949 0.976 0.024
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.8935 1.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.2423 0.8911 0.960 0.040
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.3431 0.8808 0.936 0.064
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.2423 0.8911 0.960 0.040
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.8831 0.000 1.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.1414 0.8824 0.020 0.980
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.9850 0.2008 0.428 0.572
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.8935 1.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.8831 0.000 1.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.1414 0.8828 0.020 0.980
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0376 0.8939 0.996 0.004
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0376 0.8939 0.996 0.004
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.0938 0.8953 0.988 0.012
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.8935 1.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.0938 0.8953 0.988 0.012
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.6148 0.7974 0.848 0.152
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.1184 0.8948 0.984 0.016
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.6148 0.7974 0.848 0.152
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.8935 1.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.8831 0.000 1.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.8935 1.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.2423 0.8911 0.960 0.040
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 1 0.9909 0.2702 0.556 0.444
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.8831 0.000 1.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.6048 0.7904 0.148 0.852
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 1 0.9522 0.4647 0.628 0.372
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.8935 1.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.1843 0.8812 0.028 0.972
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.8935 1.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.1843 0.8812 0.028 0.972
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.1184 0.8948 0.984 0.016
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.2423 0.8911 0.960 0.040
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.1633 0.8949 0.976 0.024
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0938 0.8832 0.012 0.988
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.0376 0.8939 0.996 0.004
#> 2629FEE3-A203-4411-8A70-02A796C9505C 1 0.7745 0.7304 0.772 0.228
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.9754 0.2881 0.408 0.592
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.4939 0.8468 0.892 0.108
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.7950 0.7066 0.760 0.240
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.1843 0.8949 0.972 0.028
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.9552 0.3569 0.376 0.624
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.8713 0.5822 0.292 0.708
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.8831 0.000 1.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.2236 0.8936 0.964 0.036
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 1 0.9909 0.2702 0.556 0.444
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.5706 0.4548 0.680 0.000 0.320
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 3 0.9885 0.4791 0.260 0.368 0.372
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.1315 0.8512 0.020 0.972 0.008
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.4349 0.7326 0.128 0.852 0.020
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.5291 0.5399 0.732 0.000 0.268
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.9154 -0.2212 0.468 0.148 0.384
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.9154 -0.2212 0.468 0.148 0.384
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 3 0.9885 0.4791 0.260 0.368 0.372
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.6309 0.0295 0.500 0.000 0.500
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.1860 0.6244 0.948 0.000 0.052
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.5968 0.0980 0.364 0.000 0.636
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.8410 0.3248 0.216 0.620 0.164
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.9106 0.5236 0.180 0.284 0.536
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.3816 0.5883 0.852 0.000 0.148
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.6309 0.0295 0.500 0.000 0.500
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 1 0.8925 -0.1992 0.464 0.124 0.412
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.8586 0.000 1.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0892 0.6244 0.980 0.000 0.020
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.2939 0.8081 0.072 0.916 0.012
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.5650 0.4251 0.688 0.000 0.312
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0237 0.8585 0.000 0.996 0.004
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.6887 0.3563 0.060 0.236 0.704
#> F325847E-F046-4B67-B01C-16919C401020 1 0.6286 0.1037 0.536 0.000 0.464
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.6309 -0.0847 0.500 0.000 0.500
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0237 0.8585 0.000 0.996 0.004
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.5650 0.4251 0.688 0.000 0.312
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.8554 0.4411 0.324 0.116 0.560
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.2959 0.6148 0.900 0.000 0.100
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.5497 0.4889 0.708 0.000 0.292
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.1031 0.6243 0.976 0.000 0.024
#> A8E48877-F8AB-44DD-A18B-194D87C44931 3 0.9885 0.4791 0.260 0.368 0.372
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.6274 0.0580 0.456 0.000 0.544
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.3129 0.8365 0.008 0.904 0.088
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.1860 0.6244 0.948 0.000 0.052
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.5363 0.5382 0.724 0.000 0.276
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.1163 0.6215 0.972 0.000 0.028
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.5650 0.4251 0.688 0.000 0.312
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.6813 0.0310 0.468 0.012 0.520
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.9687 0.4924 0.248 0.296 0.456
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.1315 0.8512 0.020 0.972 0.008
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.6308 -0.0600 0.492 0.000 0.508
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.5650 0.4251 0.688 0.000 0.312
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.8554 0.4411 0.324 0.116 0.560
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.5650 0.4251 0.688 0.000 0.312
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.2959 0.6148 0.900 0.000 0.100
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.5517 0.5174 0.728 0.004 0.268
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 3 0.9885 0.4791 0.260 0.368 0.372
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.6308 -0.0600 0.492 0.000 0.508
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.8513 0.0937 0.568 0.116 0.316
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 1 0.9773 -0.2929 0.412 0.236 0.352
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.4178 0.5903 0.828 0.000 0.172
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.2959 0.5828 0.900 0.000 0.100
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 2 0.9920 -0.5138 0.272 0.368 0.360
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.5363 0.5382 0.724 0.000 0.276
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.3129 0.8365 0.008 0.904 0.088
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.1163 0.6215 0.972 0.000 0.028
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.5560 0.4371 0.700 0.000 0.300
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.8586 0.000 1.000 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.5327 0.5365 0.728 0.000 0.272
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.5363 0.5382 0.724 0.000 0.276
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.1860 0.6244 0.948 0.000 0.052
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.5517 0.5174 0.728 0.004 0.268
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0237 0.8585 0.000 0.996 0.004
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.2261 0.6307 0.932 0.000 0.068
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.5291 0.5399 0.732 0.000 0.268
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0237 0.8585 0.000 0.996 0.004
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.5363 0.5382 0.724 0.000 0.276
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.5785 0.4066 0.668 0.000 0.332
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.5650 0.4251 0.688 0.000 0.312
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.4136 0.7518 0.116 0.864 0.020
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0237 0.8585 0.000 0.996 0.004
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.1163 0.6215 0.972 0.000 0.028
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.3816 0.5883 0.852 0.000 0.148
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.9250 0.4982 0.304 0.184 0.512
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.8982 0.4845 0.308 0.156 0.536
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.5650 0.4251 0.688 0.000 0.312
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.4121 0.5897 0.832 0.000 0.168
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.8586 0.000 1.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.6260 0.1803 0.552 0.000 0.448
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.6299 0.0926 0.524 0.000 0.476
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.5560 0.4638 0.700 0.000 0.300
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.1163 0.6215 0.972 0.000 0.028
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.5650 0.4251 0.688 0.000 0.312
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.6468 0.0886 0.444 0.004 0.552
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.5650 0.4251 0.688 0.000 0.312
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.8586 0.000 1.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.1315 0.8534 0.008 0.972 0.020
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.9229 -0.2925 0.152 0.428 0.420
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0747 0.6256 0.984 0.000 0.016
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.8586 0.000 1.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.2860 0.8379 0.004 0.912 0.084
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.5178 0.5390 0.744 0.000 0.256
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.4121 0.5897 0.832 0.000 0.168
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.5327 0.5381 0.728 0.000 0.272
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.1860 0.6281 0.948 0.000 0.052
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.5327 0.5381 0.728 0.000 0.272
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.8513 0.0937 0.568 0.116 0.316
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.5327 0.5365 0.728 0.000 0.272
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.8513 0.0937 0.568 0.116 0.316
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.1860 0.6244 0.948 0.000 0.052
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.8586 0.000 1.000 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.1163 0.6215 0.972 0.000 0.028
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.5650 0.4251 0.688 0.000 0.312
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 3 0.9885 0.4791 0.260 0.368 0.372
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0237 0.8585 0.000 0.996 0.004
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.4349 0.7326 0.128 0.852 0.020
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.9148 0.5261 0.220 0.236 0.544
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.1163 0.6215 0.972 0.000 0.028
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.3129 0.8365 0.008 0.904 0.088
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.1860 0.6244 0.948 0.000 0.052
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.3129 0.8365 0.008 0.904 0.088
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.5291 0.5399 0.732 0.000 0.268
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.5650 0.4251 0.688 0.000 0.312
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.6302 0.0993 0.520 0.000 0.480
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0983 0.8554 0.004 0.980 0.016
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.4346 0.5971 0.816 0.000 0.184
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.8554 0.4411 0.324 0.116 0.560
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.9034 0.1545 0.200 0.556 0.244
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.7690 0.1551 0.536 0.048 0.416
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.8793 0.4725 0.308 0.140 0.552
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.6308 -0.0542 0.492 0.000 0.508
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 3 0.7537 0.1870 0.056 0.332 0.612
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.7437 0.4801 0.200 0.692 0.108
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0237 0.8585 0.000 0.996 0.004
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.5363 0.5382 0.724 0.000 0.276
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.9885 0.4791 0.260 0.368 0.372
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.5387 0.2833 0.400 0.000 0.584 0.016
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 4 0.9857 0.7788 0.172 0.268 0.256 0.304
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.1139 0.8599 0.008 0.972 0.012 0.008
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.4143 0.7517 0.072 0.852 0.040 0.036
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.6637 0.2748 0.540 0.000 0.368 0.092
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.8448 0.0935 0.200 0.056 0.504 0.240
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.8448 0.0935 0.200 0.056 0.504 0.240
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 4 0.9857 0.7788 0.172 0.268 0.256 0.304
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.3401 0.5984 0.152 0.000 0.840 0.008
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.2973 0.4933 0.856 0.000 0.144 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.7371 0.1895 0.168 0.000 0.472 0.360
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.8542 -0.0542 0.120 0.532 0.124 0.224
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 4 0.9053 0.3792 0.084 0.184 0.356 0.376
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.5906 0.3040 0.644 0.000 0.292 0.064
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.3401 0.5984 0.152 0.000 0.840 0.008
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.8291 0.1448 0.200 0.052 0.524 0.224
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.2596 0.5127 0.908 0.000 0.068 0.024
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.2734 0.8184 0.036 0.916 0.024 0.024
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.6504 0.2750 0.476 0.000 0.072 0.452
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0188 0.8674 0.000 0.996 0.000 0.004
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 4 0.6951 0.3647 0.000 0.140 0.304 0.556
#> F325847E-F046-4B67-B01C-16919C401020 3 0.6359 0.4617 0.220 0.000 0.648 0.132
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.3401 0.5984 0.152 0.000 0.840 0.008
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0188 0.8674 0.000 0.996 0.000 0.004
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.6504 0.2750 0.476 0.000 0.072 0.452
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.7533 0.3176 0.124 0.048 0.604 0.224
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.5144 0.4307 0.732 0.000 0.216 0.052
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.6123 0.3722 0.336 0.000 0.600 0.064
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.2335 0.5145 0.920 0.000 0.060 0.020
#> A8E48877-F8AB-44DD-A18B-194D87C44931 4 0.9857 0.7788 0.172 0.268 0.256 0.304
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.5783 0.5469 0.188 0.000 0.704 0.108
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.2520 0.8380 0.004 0.904 0.004 0.088
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.2973 0.4933 0.856 0.000 0.144 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.6532 0.2551 0.548 0.000 0.368 0.084
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.2224 0.5206 0.928 0.000 0.040 0.032
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.6504 0.2750 0.476 0.000 0.072 0.452
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.4139 0.5882 0.144 0.000 0.816 0.040
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.9203 -0.1820 0.104 0.228 0.432 0.236
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.1139 0.8599 0.008 0.972 0.012 0.008
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.3806 0.5991 0.156 0.000 0.824 0.020
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.6504 0.2750 0.476 0.000 0.072 0.452
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.7533 0.3176 0.124 0.048 0.604 0.224
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.6504 0.2750 0.476 0.000 0.072 0.452
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.5144 0.4307 0.732 0.000 0.216 0.052
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.6500 0.0831 0.484 0.000 0.444 0.072
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 4 0.9857 0.7788 0.172 0.268 0.256 0.304
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.3806 0.5991 0.156 0.000 0.824 0.020
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.8549 -0.1398 0.408 0.044 0.360 0.188
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.9733 -0.0777 0.264 0.160 0.348 0.228
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.6549 0.3512 0.612 0.000 0.268 0.120
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.4072 0.4635 0.828 0.000 0.052 0.120
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 4 0.9857 0.7593 0.172 0.268 0.256 0.304
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.6532 0.2551 0.548 0.000 0.368 0.084
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.2520 0.8380 0.004 0.904 0.004 0.088
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.2224 0.5206 0.928 0.000 0.040 0.032
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.6546 0.2747 0.492 0.000 0.076 0.432
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0188 0.8673 0.000 0.996 0.000 0.004
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.6648 0.2689 0.536 0.000 0.372 0.092
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.6532 0.2551 0.548 0.000 0.368 0.084
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.2973 0.4933 0.856 0.000 0.144 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.6500 0.0831 0.484 0.000 0.444 0.072
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0188 0.8674 0.000 0.996 0.000 0.004
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.3763 0.4872 0.832 0.000 0.144 0.024
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.6637 0.2748 0.540 0.000 0.368 0.092
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0188 0.8674 0.000 0.996 0.000 0.004
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.6532 0.2551 0.548 0.000 0.368 0.084
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.4936 0.3780 0.372 0.000 0.624 0.004
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.6504 0.2750 0.476 0.000 0.072 0.452
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.3907 0.7686 0.064 0.864 0.036 0.036
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0188 0.8674 0.000 0.996 0.000 0.004
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.2224 0.5206 0.928 0.000 0.040 0.032
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.5906 0.3040 0.644 0.000 0.292 0.064
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.8393 0.1111 0.116 0.092 0.520 0.272
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.8116 0.1685 0.112 0.076 0.544 0.268
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.6504 0.2750 0.476 0.000 0.072 0.452
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.6685 0.3329 0.600 0.000 0.268 0.132
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0188 0.8673 0.000 0.996 0.000 0.004
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.4485 0.5491 0.248 0.000 0.740 0.012
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.4175 0.5965 0.200 0.000 0.784 0.016
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.5155 0.1614 0.468 0.000 0.528 0.004
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.2224 0.5206 0.928 0.000 0.040 0.032
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.6504 0.2750 0.476 0.000 0.072 0.452
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.5788 0.5462 0.176 0.004 0.716 0.104
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.6504 0.2750 0.476 0.000 0.072 0.452
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0188 0.8673 0.000 0.996 0.000 0.004
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.1042 0.8597 0.000 0.972 0.008 0.020
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.8978 -0.4396 0.064 0.408 0.256 0.272
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.2376 0.5128 0.916 0.000 0.068 0.016
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0188 0.8673 0.000 0.996 0.000 0.004
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.2266 0.8401 0.000 0.912 0.004 0.084
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.6668 0.0796 0.380 0.000 0.528 0.092
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.6661 0.3362 0.604 0.000 0.264 0.132
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.6626 0.2242 0.528 0.000 0.384 0.088
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.3497 0.4877 0.852 0.000 0.124 0.024
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.6626 0.2242 0.528 0.000 0.384 0.088
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.8549 -0.1398 0.408 0.044 0.360 0.188
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.6648 0.2689 0.536 0.000 0.372 0.092
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.8549 -0.1398 0.408 0.044 0.360 0.188
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.2973 0.4933 0.856 0.000 0.144 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0188 0.8673 0.000 0.996 0.000 0.004
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.2224 0.5206 0.928 0.000 0.040 0.032
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.6504 0.2750 0.476 0.000 0.072 0.452
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 4 0.9857 0.7788 0.172 0.268 0.256 0.304
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0188 0.8674 0.000 0.996 0.000 0.004
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.4143 0.7517 0.072 0.852 0.040 0.036
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.8908 -0.2953 0.096 0.140 0.396 0.368
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.2224 0.5206 0.928 0.000 0.040 0.032
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.2520 0.8380 0.004 0.904 0.004 0.088
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.2973 0.4933 0.856 0.000 0.144 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.2520 0.8380 0.004 0.904 0.004 0.088
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.6637 0.2748 0.540 0.000 0.368 0.092
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.6504 0.2750 0.476 0.000 0.072 0.452
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.3494 0.5980 0.172 0.000 0.824 0.004
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0707 0.8633 0.000 0.980 0.000 0.020
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.6618 0.3510 0.604 0.000 0.272 0.124
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.7533 0.3176 0.124 0.048 0.604 0.224
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.8919 -0.3576 0.104 0.460 0.152 0.284
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.7552 0.3145 0.264 0.024 0.564 0.148
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.7994 0.2105 0.112 0.072 0.560 0.256
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.3479 0.5977 0.148 0.000 0.840 0.012
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 4 0.7548 0.4233 0.008 0.240 0.216 0.536
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.7065 0.4248 0.104 0.676 0.084 0.136
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0188 0.8674 0.000 0.996 0.000 0.004
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.6532 0.2551 0.548 0.000 0.368 0.084
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 4 0.9857 0.7788 0.172 0.268 0.256 0.304
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.7013 0.3853 0.256 0.000 0.472 0.020 0.252
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 5 0.5784 0.4647 0.028 0.132 0.052 0.064 0.724
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.2122 0.8981 0.000 0.924 0.008 0.036 0.032
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.4196 0.8265 0.012 0.828 0.068 0.044 0.048
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.7658 0.4498 0.444 0.000 0.084 0.172 0.300
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 5 0.5935 0.0928 0.028 0.016 0.464 0.020 0.472
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 5 0.5935 0.0928 0.028 0.016 0.464 0.020 0.472
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 5 0.5784 0.4647 0.028 0.132 0.052 0.064 0.724
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.5242 0.5361 0.040 0.000 0.556 0.004 0.400
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.3964 0.5435 0.796 0.000 0.032 0.012 0.160
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.8550 0.1242 0.204 0.000 0.304 0.240 0.252
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.7757 0.1048 0.020 0.432 0.136 0.060 0.352
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 5 0.4946 0.3768 0.020 0.052 0.052 0.092 0.784
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.5418 0.1180 0.480 0.000 0.476 0.028 0.016
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.5242 0.5361 0.040 0.000 0.556 0.004 0.400
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.5763 -0.1199 0.028 0.012 0.488 0.016 0.456
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.1661 0.8948 0.000 0.940 0.000 0.024 0.036
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.3206 0.5384 0.868 0.000 0.044 0.016 0.072
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.2986 0.8709 0.008 0.892 0.036 0.032 0.032
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.5804 0.9931 0.352 0.000 0.000 0.544 0.104
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.1579 0.8951 0.000 0.944 0.000 0.024 0.032
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 5 0.6700 0.2898 0.040 0.028 0.088 0.228 0.616
#> F325847E-F046-4B67-B01C-16919C401020 3 0.5512 0.2278 0.048 0.000 0.596 0.016 0.340
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.5184 0.5372 0.036 0.000 0.556 0.004 0.404
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0290 0.8987 0.000 0.992 0.000 0.000 0.008
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.5804 0.9931 0.352 0.000 0.000 0.544 0.104
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 5 0.4659 0.1423 0.028 0.004 0.208 0.020 0.740
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.5984 0.4890 0.628 0.000 0.248 0.096 0.028
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.5512 0.4358 0.112 0.000 0.712 0.040 0.136
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.3328 0.5401 0.860 0.000 0.036 0.020 0.084
#> A8E48877-F8AB-44DD-A18B-194D87C44931 5 0.5784 0.4647 0.028 0.132 0.052 0.064 0.724
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 5 0.5816 -0.2990 0.056 0.000 0.420 0.016 0.508
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.2838 0.8751 0.008 0.888 0.008 0.076 0.020
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.3964 0.5435 0.796 0.000 0.032 0.012 0.160
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.7679 0.4350 0.440 0.000 0.104 0.140 0.316
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.2838 0.4845 0.884 0.000 0.008 0.036 0.072
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.5804 0.9931 0.352 0.000 0.000 0.544 0.104
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.5106 0.4850 0.036 0.000 0.508 0.000 0.456
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 5 0.6836 0.2386 0.032 0.156 0.152 0.036 0.624
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.2122 0.8981 0.000 0.924 0.008 0.036 0.032
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.5272 0.5335 0.040 0.000 0.540 0.004 0.416
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.5804 0.9931 0.352 0.000 0.000 0.544 0.104
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 5 0.4659 0.1423 0.028 0.004 0.208 0.020 0.740
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.5804 0.9931 0.352 0.000 0.000 0.544 0.104
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.5984 0.4890 0.628 0.000 0.248 0.096 0.028
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.5526 0.1583 0.316 0.000 0.616 0.024 0.044
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 5 0.5784 0.4647 0.028 0.132 0.052 0.064 0.724
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.5272 0.5335 0.040 0.000 0.540 0.004 0.416
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.6942 0.0723 0.316 0.008 0.072 0.072 0.532
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.8304 0.2763 0.160 0.124 0.116 0.076 0.524
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.6845 0.4308 0.500 0.000 0.284 0.196 0.020
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.3218 0.3923 0.860 0.000 0.032 0.096 0.012
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 5 0.6031 0.4575 0.028 0.132 0.064 0.068 0.708
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.7679 0.4350 0.440 0.000 0.104 0.140 0.316
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.2838 0.8751 0.008 0.888 0.008 0.076 0.020
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.2838 0.4845 0.884 0.000 0.008 0.036 0.072
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.6196 0.9226 0.364 0.000 0.020 0.528 0.088
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.2313 0.8872 0.000 0.912 0.004 0.040 0.044
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.7675 0.4469 0.444 0.000 0.088 0.168 0.300
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.7679 0.4350 0.440 0.000 0.104 0.140 0.316
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.3964 0.5435 0.796 0.000 0.032 0.012 0.160
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.5526 0.1583 0.316 0.000 0.616 0.024 0.044
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0290 0.8987 0.000 0.992 0.000 0.000 0.008
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.4875 0.5768 0.772 0.000 0.064 0.072 0.092
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.7658 0.4498 0.444 0.000 0.084 0.172 0.300
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0290 0.8987 0.000 0.992 0.000 0.000 0.008
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.7679 0.4350 0.440 0.000 0.104 0.140 0.316
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.6900 0.3881 0.268 0.000 0.372 0.004 0.356
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.5804 0.9931 0.352 0.000 0.000 0.544 0.104
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.3987 0.8368 0.012 0.840 0.064 0.044 0.040
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0290 0.8987 0.000 0.992 0.000 0.000 0.008
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.2838 0.4845 0.884 0.000 0.008 0.036 0.072
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.5418 0.1180 0.480 0.000 0.476 0.028 0.016
#> 721CDBE6-FC85-4C30-B23E-28407340286F 5 0.4931 0.1760 0.036 0.000 0.192 0.040 0.732
#> 392897E4-6009-422C-B461-649F4DDF260C 5 0.5091 0.1550 0.032 0.000 0.204 0.048 0.716
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.5804 0.9931 0.352 0.000 0.000 0.544 0.104
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.6678 0.3979 0.492 0.000 0.316 0.180 0.012
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.2313 0.8872 0.000 0.912 0.004 0.040 0.044
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.6322 0.4826 0.140 0.000 0.480 0.004 0.376
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.5960 0.5126 0.096 0.000 0.504 0.004 0.396
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 5 0.7008 -0.3321 0.348 0.000 0.272 0.008 0.372
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.2838 0.4845 0.884 0.000 0.008 0.036 0.072
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.5804 0.9931 0.352 0.000 0.000 0.544 0.104
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 5 0.5594 -0.2825 0.048 0.000 0.408 0.012 0.532
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.5804 0.9931 0.352 0.000 0.000 0.544 0.104
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.2313 0.8872 0.000 0.912 0.004 0.040 0.044
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.1992 0.8938 0.000 0.924 0.000 0.032 0.044
#> 53A96249-66D5-4C26-893B-ADC71481D261 5 0.7043 0.2647 0.016 0.300 0.032 0.124 0.528
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.3282 0.5447 0.860 0.000 0.044 0.012 0.084
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.2313 0.8872 0.000 0.912 0.004 0.040 0.044
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.2617 0.8771 0.004 0.896 0.008 0.076 0.016
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.4480 0.2899 0.140 0.000 0.776 0.068 0.016
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.6666 0.4025 0.496 0.000 0.312 0.180 0.012
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.7809 0.3880 0.424 0.000 0.116 0.144 0.316
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.4482 0.5191 0.788 0.000 0.100 0.024 0.088
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.7809 0.3880 0.424 0.000 0.116 0.144 0.316
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 5 0.6942 0.0723 0.316 0.008 0.072 0.072 0.532
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.7675 0.4469 0.444 0.000 0.088 0.168 0.300
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 5 0.6942 0.0723 0.316 0.008 0.072 0.072 0.532
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.3964 0.5435 0.796 0.000 0.032 0.012 0.160
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.2313 0.8872 0.000 0.912 0.004 0.040 0.044
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.2838 0.4845 0.884 0.000 0.008 0.036 0.072
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.5804 0.9931 0.352 0.000 0.000 0.544 0.104
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 5 0.5784 0.4647 0.028 0.132 0.052 0.064 0.724
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0290 0.8987 0.000 0.992 0.000 0.000 0.008
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.4196 0.8265 0.012 0.828 0.068 0.044 0.048
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 5 0.5396 0.3165 0.020 0.028 0.112 0.096 0.744
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.2838 0.4845 0.884 0.000 0.008 0.036 0.072
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.2838 0.8751 0.008 0.888 0.008 0.076 0.020
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.3964 0.5435 0.796 0.000 0.032 0.012 0.160
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.2838 0.8751 0.008 0.888 0.008 0.076 0.020
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.7658 0.4498 0.444 0.000 0.084 0.172 0.300
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.5804 0.9931 0.352 0.000 0.000 0.544 0.104
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.5304 0.5407 0.056 0.000 0.560 0.000 0.384
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.1836 0.8957 0.000 0.932 0.000 0.032 0.036
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.7079 0.4266 0.484 0.000 0.288 0.196 0.032
#> 2629FEE3-A203-4411-8A70-02A796C9505C 5 0.4659 0.1423 0.028 0.004 0.208 0.020 0.740
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 5 0.7373 0.1430 0.012 0.332 0.108 0.064 0.484
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.5536 0.1751 0.068 0.004 0.672 0.020 0.236
#> B12A4446-2310-4139-897F-CA030478CBD5 5 0.5182 0.1269 0.032 0.000 0.216 0.048 0.704
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.5258 0.5315 0.040 0.000 0.548 0.004 0.408
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 5 0.7331 0.3035 0.048 0.112 0.040 0.232 0.568
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.7019 0.4926 0.012 0.576 0.108 0.064 0.240
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0162 0.8980 0.000 0.996 0.000 0.000 0.004
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.7679 0.4350 0.440 0.000 0.104 0.140 0.316
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 5 0.5784 0.4647 0.028 0.132 0.052 0.064 0.724
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.6513 0.27488 0.140 0.000 0.604 0.144 NA 0.028
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 6 0.5263 0.59145 0.000 0.048 0.168 0.104 NA 0.680
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.3905 0.79669 0.000 0.780 0.000 0.008 NA 0.076
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.3856 0.74239 0.032 0.784 0.000 0.028 NA 0.156
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.7707 0.20871 0.288 0.000 0.332 0.080 NA 0.028
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 6 0.6679 0.28280 0.136 0.000 0.324 0.004 NA 0.468
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 6 0.6679 0.28280 0.136 0.000 0.324 0.004 NA 0.468
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 6 0.5263 0.59145 0.000 0.048 0.168 0.104 NA 0.680
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0551 0.40619 0.004 0.000 0.984 0.000 NA 0.004
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.5438 0.57658 0.568 0.000 0.172 0.260 NA 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.8209 -0.01477 0.160 0.000 0.352 0.048 NA 0.200
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 6 0.6134 0.11702 0.056 0.356 0.024 0.036 NA 0.524
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 6 0.6334 0.31296 0.000 0.036 0.304 0.016 NA 0.528
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.6400 0.35884 0.600 0.000 0.164 0.068 NA 0.020
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0767 0.40708 0.008 0.000 0.976 0.000 NA 0.004
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 6 0.6766 0.24005 0.136 0.000 0.352 0.004 NA 0.436
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.3842 0.77549 0.000 0.768 0.000 0.000 NA 0.076
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.4777 0.61745 0.664 0.000 0.092 0.240 NA 0.004
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.2915 0.78205 0.008 0.848 0.000 0.024 NA 0.120
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0790 0.99252 0.000 0.000 0.032 0.968 NA 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.3717 0.78003 0.000 0.780 0.000 0.000 NA 0.072
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 6 0.6611 0.37114 0.008 0.020 0.148 0.036 NA 0.556
#> F325847E-F046-4B67-B01C-16919C401020 3 0.6528 0.06493 0.120 0.000 0.544 0.004 NA 0.240
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0984 0.40693 0.012 0.000 0.968 0.000 NA 0.008
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0260 0.81352 0.000 0.992 0.000 0.000 NA 0.008
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0790 0.99252 0.000 0.000 0.032 0.968 NA 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.5115 0.06912 0.000 0.000 0.584 0.012 NA 0.336
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.5138 0.49480 0.736 0.000 0.028 0.076 NA 0.100
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.6551 0.21377 0.092 0.000 0.600 0.028 NA 0.120
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.4750 0.61626 0.656 0.000 0.100 0.244 NA 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 6 0.5263 0.59145 0.000 0.048 0.168 0.104 NA 0.680
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.4813 0.30111 0.016 0.000 0.728 0.024 NA 0.168
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.3341 0.76828 0.000 0.836 0.000 0.016 NA 0.060
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.5438 0.57658 0.568 0.000 0.172 0.260 NA 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 3 0.8451 0.17433 0.248 0.000 0.308 0.156 NA 0.068
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.5151 0.58029 0.608 0.000 0.060 0.312 NA 0.004
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0790 0.99252 0.000 0.000 0.032 0.968 NA 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.1873 0.39277 0.008 0.000 0.924 0.000 NA 0.048
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.6926 -0.14381 0.008 0.152 0.472 0.012 NA 0.308
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.3905 0.79669 0.000 0.780 0.000 0.008 NA 0.076
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.1180 0.40694 0.012 0.000 0.960 0.000 NA 0.016
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0790 0.99252 0.000 0.000 0.032 0.968 NA 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.5115 0.06912 0.000 0.000 0.584 0.012 NA 0.336
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0790 0.99252 0.000 0.000 0.032 0.968 NA 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.5138 0.49480 0.736 0.000 0.028 0.076 NA 0.100
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.6998 0.15830 0.464 0.000 0.308 0.040 NA 0.036
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 6 0.5263 0.59145 0.000 0.048 0.168 0.104 NA 0.680
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.1180 0.40694 0.012 0.000 0.960 0.000 NA 0.016
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.8402 0.21644 0.180 0.000 0.340 0.124 NA 0.264
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 6 0.8514 0.13603 0.120 0.080 0.280 0.044 NA 0.392
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.6461 0.25978 0.544 0.000 0.040 0.036 NA 0.092
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.4661 0.53613 0.688 0.000 0.000 0.240 NA 0.024
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 6 0.5537 0.58755 0.012 0.048 0.160 0.104 NA 0.676
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.8451 0.17433 0.248 0.000 0.308 0.156 NA 0.068
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.3341 0.76828 0.000 0.836 0.000 0.016 NA 0.060
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.5151 0.58029 0.608 0.000 0.060 0.312 NA 0.004
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.1785 0.91527 0.048 0.000 0.016 0.928 NA 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.4756 0.72136 0.000 0.664 0.000 0.000 NA 0.112
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.7674 0.21124 0.288 0.000 0.336 0.076 NA 0.028
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.8451 0.17433 0.248 0.000 0.308 0.156 NA 0.068
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.5438 0.57658 0.568 0.000 0.172 0.260 NA 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.6998 0.15830 0.464 0.000 0.308 0.040 NA 0.036
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0260 0.81352 0.000 0.992 0.000 0.000 NA 0.008
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.5938 0.59317 0.616 0.000 0.120 0.208 NA 0.008
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.7707 0.20871 0.288 0.000 0.332 0.080 NA 0.028
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0260 0.81352 0.000 0.992 0.000 0.000 NA 0.008
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 3 0.8451 0.17433 0.248 0.000 0.308 0.156 NA 0.068
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.4085 0.37863 0.208 0.000 0.744 0.032 NA 0.004
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0790 0.99252 0.000 0.000 0.032 0.968 NA 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.3709 0.75201 0.028 0.796 0.000 0.028 NA 0.148
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0260 0.81352 0.000 0.992 0.000 0.000 NA 0.008
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.5151 0.58029 0.608 0.000 0.060 0.312 NA 0.004
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.6400 0.35884 0.600 0.000 0.164 0.068 NA 0.020
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.5008 -0.00273 0.008 0.000 0.564 0.004 NA 0.376
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.5140 0.02684 0.008 0.000 0.580 0.012 NA 0.352
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0790 0.99252 0.000 0.000 0.032 0.968 NA 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.4650 0.26164 0.652 0.000 0.040 0.000 NA 0.016
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.4756 0.72136 0.000 0.664 0.000 0.000 NA 0.112
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.2986 0.41583 0.076 0.000 0.868 0.032 NA 0.012
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.2290 0.41259 0.040 0.000 0.912 0.020 NA 0.016
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.5411 0.20867 0.300 0.000 0.608 0.056 NA 0.016
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.5151 0.58029 0.608 0.000 0.060 0.312 NA 0.004
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0790 0.99252 0.000 0.000 0.032 0.968 NA 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.4713 0.28808 0.016 0.000 0.724 0.012 NA 0.180
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0790 0.99252 0.000 0.000 0.032 0.968 NA 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.4756 0.72136 0.000 0.664 0.000 0.000 NA 0.112
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.3677 0.79380 0.000 0.808 0.008 0.004 NA 0.064
#> 53A96249-66D5-4C26-893B-ADC71481D261 6 0.7842 0.27828 0.000 0.220 0.224 0.016 NA 0.368
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.4746 0.61813 0.660 0.000 0.104 0.236 NA 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.4756 0.72136 0.000 0.664 0.000 0.000 NA 0.112
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.3086 0.77495 0.000 0.852 0.000 0.012 NA 0.056
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.7144 -0.04806 0.352 0.000 0.320 0.000 NA 0.080
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.4587 0.26520 0.656 0.000 0.036 0.000 NA 0.016
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.8225 0.21307 0.252 0.000 0.340 0.128 NA 0.056
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.5658 0.60240 0.592 0.000 0.148 0.240 NA 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.8225 0.21307 0.252 0.000 0.340 0.128 NA 0.056
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 3 0.8402 0.21644 0.180 0.000 0.340 0.124 NA 0.264
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 3 0.7674 0.21124 0.288 0.000 0.336 0.076 NA 0.028
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 3 0.8402 0.21644 0.180 0.000 0.340 0.124 NA 0.264
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.5438 0.57658 0.568 0.000 0.172 0.260 NA 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.4756 0.72136 0.000 0.664 0.000 0.000 NA 0.112
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.5151 0.58029 0.608 0.000 0.060 0.312 NA 0.004
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0790 0.99252 0.000 0.000 0.032 0.968 NA 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 6 0.5263 0.59145 0.000 0.048 0.168 0.104 NA 0.680
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0260 0.81352 0.000 0.992 0.000 0.000 NA 0.008
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.3856 0.74239 0.032 0.784 0.000 0.028 NA 0.156
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 6 0.5991 0.21255 0.000 0.020 0.396 0.008 NA 0.472
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.5151 0.58029 0.608 0.000 0.060 0.312 NA 0.004
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.3341 0.76828 0.000 0.836 0.000 0.016 NA 0.060
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.5438 0.57658 0.568 0.000 0.172 0.260 NA 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.3341 0.76828 0.000 0.836 0.000 0.016 NA 0.060
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.7707 0.20871 0.288 0.000 0.332 0.080 NA 0.028
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0790 0.99252 0.000 0.000 0.032 0.968 NA 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.1503 0.40996 0.032 0.000 0.944 0.000 NA 0.016
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.3426 0.79618 0.000 0.816 0.000 0.004 NA 0.064
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.6694 0.22938 0.528 0.000 0.048 0.040 NA 0.100
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.5115 0.06912 0.000 0.000 0.584 0.012 NA 0.336
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 6 0.5360 0.39472 0.040 0.248 0.020 0.040 NA 0.652
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.7382 -0.10236 0.220 0.000 0.364 0.000 NA 0.288
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.5147 0.05801 0.008 0.000 0.592 0.012 NA 0.336
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0767 0.40389 0.004 0.000 0.976 0.000 NA 0.012
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 6 0.6999 0.39599 0.016 0.092 0.072 0.032 NA 0.552
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.6364 0.27454 0.040 0.452 0.004 0.040 NA 0.420
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0520 0.81396 0.000 0.984 0.000 0.000 NA 0.008
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.8451 0.17433 0.248 0.000 0.308 0.156 NA 0.068
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 6 0.5263 0.59145 0.000 0.048 0.168 0.104 NA 0.680
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "kmeans"]
# you can also extract it by
# res = res_list["SD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.983 0.953 0.972 0.4762 0.521 0.521
#> 3 3 0.468 0.625 0.776 0.3470 0.704 0.485
#> 4 4 0.538 0.586 0.746 0.1287 0.813 0.530
#> 5 5 0.601 0.620 0.706 0.0789 0.863 0.563
#> 6 6 0.705 0.680 0.771 0.0484 0.946 0.757
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.0000 0.980 1.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.1633 0.961 0.024 0.976
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0672 0.963 0.008 0.992
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0376 0.962 0.004 0.996
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.7056 0.756 0.808 0.192
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.2948 0.943 0.948 0.052
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 2 0.2423 0.954 0.040 0.960
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.1633 0.961 0.024 0.976
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.0376 0.979 0.996 0.004
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.1184 0.979 0.984 0.016
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.0376 0.979 0.996 0.004
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.1184 0.962 0.016 0.984
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.2603 0.954 0.044 0.956
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.1414 0.978 0.980 0.020
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.0376 0.979 0.996 0.004
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.1184 0.959 0.016 0.984
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0672 0.963 0.008 0.992
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.1414 0.978 0.980 0.020
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0672 0.963 0.008 0.992
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.1184 0.979 0.984 0.016
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0672 0.963 0.008 0.992
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.2603 0.954 0.044 0.956
#> F325847E-F046-4B67-B01C-16919C401020 1 0.0672 0.978 0.992 0.008
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.0376 0.979 0.996 0.004
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0672 0.963 0.008 0.992
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.1184 0.979 0.984 0.016
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 2 0.2603 0.954 0.044 0.956
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0376 0.979 0.996 0.004
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.0672 0.978 0.992 0.008
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.1414 0.978 0.980 0.020
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.2778 0.954 0.048 0.952
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.0376 0.979 0.996 0.004
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0672 0.963 0.008 0.992
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.1184 0.979 0.984 0.016
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.0000 0.980 1.000 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.1184 0.979 0.984 0.016
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.1184 0.979 0.984 0.016
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.0376 0.979 0.996 0.004
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.2603 0.954 0.044 0.956
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0376 0.962 0.004 0.996
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.0376 0.979 0.996 0.004
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.1184 0.979 0.984 0.016
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.2603 0.954 0.044 0.956
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.1184 0.979 0.984 0.016
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.0938 0.980 0.988 0.012
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.0672 0.978 0.992 0.008
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.2778 0.954 0.048 0.952
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.0376 0.979 0.996 0.004
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.0376 0.980 0.996 0.004
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.2778 0.954 0.048 0.952
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0938 0.980 0.988 0.012
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.1414 0.978 0.980 0.020
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.7139 0.755 0.804 0.196
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.0376 0.979 0.996 0.004
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.2778 0.936 0.048 0.952
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.1184 0.979 0.984 0.016
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.1414 0.978 0.980 0.020
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.1184 0.961 0.016 0.984
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.0000 0.980 1.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0000 0.980 1.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.1184 0.979 0.984 0.016
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.0376 0.979 0.996 0.004
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0376 0.962 0.004 0.996
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.1184 0.979 0.984 0.016
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.0938 0.974 0.988 0.012
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0672 0.963 0.008 0.992
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.0376 0.979 0.996 0.004
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0376 0.979 0.996 0.004
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.1184 0.979 0.984 0.016
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0376 0.962 0.004 0.996
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0672 0.963 0.008 0.992
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.1184 0.979 0.984 0.016
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.1414 0.978 0.980 0.020
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.0376 0.979 0.996 0.004
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.0376 0.979 0.996 0.004
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.1184 0.979 0.984 0.016
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0376 0.979 0.996 0.004
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0376 0.962 0.004 0.996
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.0376 0.979 0.996 0.004
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.0376 0.979 0.996 0.004
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0938 0.980 0.988 0.012
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.1414 0.978 0.980 0.020
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.6148 0.833 0.152 0.848
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.0376 0.979 0.996 0.004
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.1184 0.979 0.984 0.016
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0672 0.963 0.008 0.992
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0376 0.962 0.004 0.996
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.1414 0.960 0.020 0.980
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.1414 0.978 0.980 0.020
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.961 0.000 1.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0672 0.963 0.008 0.992
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0672 0.978 0.992 0.008
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0672 0.980 0.992 0.008
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.0000 0.980 1.000 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.1184 0.979 0.984 0.016
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.0000 0.980 1.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.9815 0.295 0.420 0.580
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.0000 0.980 1.000 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.1184 0.979 0.984 0.016
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.1184 0.979 0.984 0.016
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0938 0.960 0.012 0.988
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.1184 0.979 0.984 0.016
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.1184 0.979 0.984 0.016
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.1633 0.960 0.024 0.976
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0672 0.963 0.008 0.992
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.8267 0.654 0.260 0.740
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.2603 0.954 0.044 0.956
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.1184 0.979 0.984 0.016
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.8267 0.654 0.260 0.740
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.1184 0.979 0.984 0.016
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0672 0.963 0.008 0.992
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.7139 0.755 0.804 0.196
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.1184 0.979 0.984 0.016
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.0376 0.979 0.996 0.004
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0376 0.962 0.004 0.996
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.6438 0.807 0.836 0.164
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.2603 0.954 0.044 0.956
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.1184 0.962 0.016 0.984
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.0672 0.978 0.992 0.008
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.0376 0.979 0.996 0.004
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.0376 0.979 0.996 0.004
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0672 0.963 0.008 0.992
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.1184 0.962 0.016 0.984
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0672 0.963 0.008 0.992
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.0000 0.980 1.000 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.2603 0.954 0.044 0.956
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.6274 -0.1787 0.456 0.000 0.544
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.4887 0.7207 0.000 0.772 0.228
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.8830 0.000 1.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.1989 0.8543 0.048 0.948 0.004
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.6597 0.3985 0.312 0.024 0.664
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.1964 0.6664 0.056 0.000 0.944
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.6318 0.5473 0.068 0.172 0.760
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.4702 0.7372 0.000 0.788 0.212
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0747 0.6828 0.016 0.000 0.984
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.5465 0.7493 0.712 0.000 0.288
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.3941 0.5849 0.156 0.000 0.844
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.3983 0.8284 0.068 0.884 0.048
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.5216 0.4427 0.000 0.260 0.740
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.4178 0.7781 0.828 0.000 0.172
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0747 0.6828 0.016 0.000 0.984
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.5201 0.7070 0.004 0.760 0.236
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.8830 0.000 1.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.4235 0.7791 0.824 0.000 0.176
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.8830 0.000 1.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.2796 0.7599 0.908 0.000 0.092
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.8830 0.000 1.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.5327 0.4217 0.000 0.272 0.728
#> F325847E-F046-4B67-B01C-16919C401020 3 0.2448 0.6557 0.076 0.000 0.924
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.2796 0.6431 0.092 0.000 0.908
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.8830 0.000 1.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.2796 0.7599 0.908 0.000 0.092
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.2537 0.6561 0.000 0.080 0.920
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.5650 0.6434 0.688 0.000 0.312
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.5327 0.5095 0.272 0.000 0.728
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.4235 0.7779 0.824 0.000 0.176
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.6235 0.3534 0.000 0.564 0.436
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.0000 0.6801 0.000 0.000 1.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0237 0.8812 0.000 0.996 0.004
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.5465 0.7493 0.712 0.000 0.288
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.6309 0.2786 0.500 0.000 0.500
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.4452 0.7931 0.808 0.000 0.192
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.2796 0.7599 0.908 0.000 0.092
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0747 0.6828 0.016 0.000 0.984
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.4750 0.5184 0.000 0.216 0.784
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.8830 0.000 1.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0747 0.6828 0.016 0.000 0.984
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.2796 0.7599 0.908 0.000 0.092
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.5327 0.4192 0.000 0.272 0.728
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.2796 0.7599 0.908 0.000 0.092
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.5678 0.7011 0.684 0.000 0.316
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.5905 0.3719 0.352 0.000 0.648
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.6235 0.3534 0.000 0.564 0.436
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0592 0.6820 0.012 0.000 0.988
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.5988 0.1688 0.368 0.000 0.632
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.6045 0.2132 0.000 0.380 0.620
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.5560 0.6480 0.700 0.000 0.300
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.3340 0.7822 0.880 0.000 0.120
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 3 0.6750 0.4259 0.336 0.024 0.640
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.5810 0.5725 0.664 0.000 0.336
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.4811 0.7610 0.148 0.828 0.024
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.4504 0.7927 0.804 0.000 0.196
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0892 0.7286 0.980 0.000 0.020
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.8830 0.000 1.000 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.6295 -0.1183 0.472 0.000 0.528
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.6309 -0.3015 0.500 0.000 0.500
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.5465 0.7493 0.712 0.000 0.288
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.5098 0.5439 0.248 0.000 0.752
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.8830 0.000 1.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.4235 0.7811 0.824 0.000 0.176
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.5650 0.3930 0.312 0.000 0.688
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.8830 0.000 1.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.5859 0.5587 0.656 0.000 0.344
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.4504 0.5397 0.196 0.000 0.804
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.2796 0.7599 0.908 0.000 0.092
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.8830 0.000 1.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.8830 0.000 1.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.4504 0.7927 0.804 0.000 0.196
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.4346 0.7732 0.816 0.000 0.184
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0747 0.6828 0.016 0.000 0.984
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0892 0.6818 0.020 0.000 0.980
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.2796 0.7599 0.908 0.000 0.092
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.6180 0.3997 0.584 0.000 0.416
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.8830 0.000 1.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.1860 0.6691 0.052 0.000 0.948
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0747 0.6828 0.016 0.000 0.984
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.5678 0.3582 0.316 0.000 0.684
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.3482 0.7826 0.872 0.000 0.128
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.5111 0.6828 0.820 0.036 0.144
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0747 0.6828 0.016 0.000 0.984
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.2796 0.7599 0.908 0.000 0.092
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.8830 0.000 1.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0000 0.8830 0.000 1.000 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.4796 0.7275 0.000 0.780 0.220
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.4235 0.7779 0.824 0.000 0.176
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.8830 0.000 1.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.8830 0.000 1.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.5733 0.4256 0.324 0.000 0.676
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.5560 0.6480 0.700 0.000 0.300
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.6111 0.0916 0.396 0.000 0.604
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.5098 0.7783 0.752 0.000 0.248
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.6309 -0.2958 0.496 0.000 0.504
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 3 0.9488 0.2838 0.248 0.256 0.496
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 3 0.6140 0.0606 0.404 0.000 0.596
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.6302 0.3864 0.520 0.000 0.480
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.5465 0.7493 0.712 0.000 0.288
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.8830 0.000 1.000 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.3340 0.7823 0.880 0.000 0.120
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.2796 0.7599 0.908 0.000 0.092
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.6180 0.4051 0.000 0.584 0.416
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.8830 0.000 1.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.6601 0.5551 0.296 0.676 0.028
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.5327 0.4189 0.000 0.272 0.728
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.4504 0.7927 0.804 0.000 0.196
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.6497 0.5064 0.336 0.648 0.016
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.5397 0.7568 0.720 0.000 0.280
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0424 0.8793 0.000 0.992 0.008
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.6726 0.2969 0.332 0.024 0.644
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.2796 0.7599 0.908 0.000 0.092
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.2165 0.6623 0.064 0.000 0.936
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.8830 0.000 1.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 3 0.7072 0.0146 0.476 0.020 0.504
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.4931 0.4909 0.000 0.232 0.768
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.2301 0.8524 0.004 0.936 0.060
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.3752 0.6267 0.144 0.000 0.856
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0747 0.6828 0.016 0.000 0.984
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0747 0.6828 0.016 0.000 0.984
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.4750 0.7339 0.000 0.784 0.216
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0237 0.8815 0.000 0.996 0.004
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.8830 0.000 1.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.6309 -0.2881 0.496 0.000 0.504
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.6305 0.2077 0.000 0.516 0.484
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.6462 0.3755 0.580 0.000 0.332 0.088
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.7804 0.3987 0.076 0.512 0.348 0.064
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.1124 0.8510 0.004 0.972 0.012 0.012
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.2973 0.8102 0.096 0.884 0.000 0.020
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.6617 0.2074 0.532 0.000 0.380 0.088
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.4898 0.5969 0.260 0.000 0.716 0.024
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.5837 0.5578 0.196 0.032 0.724 0.048
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.7454 0.4576 0.060 0.548 0.332 0.060
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.3088 0.7093 0.128 0.000 0.864 0.008
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.6369 0.4144 0.572 0.000 0.076 0.352
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.4706 0.6014 0.248 0.000 0.732 0.020
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.7819 0.6522 0.188 0.604 0.128 0.080
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.2522 0.6601 0.012 0.052 0.920 0.016
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.5172 0.4246 0.704 0.000 0.036 0.260
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.3351 0.6977 0.148 0.000 0.844 0.008
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.8136 0.4038 0.112 0.496 0.332 0.060
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0188 0.8544 0.000 0.996 0.000 0.004
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.5343 0.4131 0.656 0.000 0.028 0.316
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0524 0.8536 0.004 0.988 0.000 0.008
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.2611 0.9771 0.096 0.000 0.008 0.896
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0188 0.8544 0.000 0.996 0.000 0.004
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.2761 0.6546 0.016 0.064 0.908 0.012
#> F325847E-F046-4B67-B01C-16919C401020 3 0.4991 0.5332 0.388 0.000 0.608 0.004
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.4328 0.6144 0.244 0.000 0.748 0.008
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.1256 0.8509 0.008 0.964 0.000 0.028
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.2611 0.9771 0.096 0.000 0.008 0.896
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.1631 0.6761 0.020 0.008 0.956 0.016
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.3996 0.5327 0.836 0.000 0.104 0.060
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.5257 0.4142 0.444 0.000 0.548 0.008
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.5473 0.4064 0.644 0.000 0.032 0.324
#> A8E48877-F8AB-44DD-A18B-194D87C44931 3 0.8009 -0.1593 0.084 0.408 0.444 0.064
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.2859 0.7094 0.112 0.000 0.880 0.008
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.1388 0.8500 0.012 0.960 0.000 0.028
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.6369 0.4144 0.572 0.000 0.076 0.352
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.6133 0.5265 0.676 0.000 0.188 0.136
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.5873 0.3306 0.548 0.000 0.036 0.416
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.2805 0.9715 0.100 0.000 0.012 0.888
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.3088 0.7093 0.128 0.000 0.864 0.008
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.4149 0.6386 0.068 0.048 0.852 0.032
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0524 0.8541 0.004 0.988 0.000 0.008
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.3088 0.7093 0.128 0.000 0.864 0.008
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.2611 0.9771 0.096 0.000 0.008 0.896
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.3102 0.6479 0.016 0.064 0.896 0.024
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.2611 0.9771 0.096 0.000 0.008 0.896
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.5321 0.5022 0.716 0.000 0.056 0.228
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.5776 0.3197 0.468 0.000 0.504 0.028
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 3 0.8056 -0.1623 0.088 0.408 0.440 0.064
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.3088 0.7093 0.128 0.000 0.864 0.008
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.6739 0.4106 0.576 0.000 0.304 0.120
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.8313 0.3441 0.216 0.192 0.532 0.060
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.3533 0.5287 0.864 0.000 0.056 0.080
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.5400 0.3585 0.608 0.000 0.020 0.372
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.6413 0.0122 0.516 0.000 0.416 0.068
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.4171 0.5291 0.828 0.000 0.088 0.084
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.5021 0.6849 0.240 0.724 0.000 0.036
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.5942 0.3354 0.548 0.000 0.040 0.412
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.3569 0.8171 0.196 0.000 0.000 0.804
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.3605 0.8093 0.004 0.864 0.088 0.044
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.5565 0.4340 0.700 0.000 0.232 0.068
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.6240 0.5193 0.664 0.000 0.200 0.136
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.6369 0.4144 0.572 0.000 0.076 0.352
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.5731 0.4085 0.428 0.000 0.544 0.028
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.1042 0.8518 0.008 0.972 0.000 0.020
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.5742 0.3899 0.596 0.000 0.036 0.368
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.6586 0.2111 0.544 0.000 0.368 0.088
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0524 0.8536 0.004 0.988 0.000 0.008
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.4036 0.5278 0.836 0.000 0.088 0.076
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.5038 0.5359 0.296 0.000 0.684 0.020
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.2805 0.9715 0.100 0.000 0.012 0.888
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.1042 0.8518 0.008 0.972 0.000 0.020
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0524 0.8536 0.004 0.988 0.000 0.008
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.5942 0.3354 0.548 0.000 0.040 0.412
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.5083 0.4306 0.716 0.000 0.036 0.248
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.2198 0.7061 0.072 0.000 0.920 0.008
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.2799 0.7103 0.108 0.000 0.884 0.008
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.2611 0.9771 0.096 0.000 0.008 0.896
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.3934 0.5181 0.836 0.000 0.116 0.048
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0524 0.8541 0.004 0.988 0.000 0.008
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.3591 0.6840 0.168 0.000 0.824 0.008
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.3088 0.7093 0.128 0.000 0.864 0.008
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.6532 0.2943 0.336 0.000 0.572 0.092
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.5467 0.3768 0.612 0.000 0.024 0.364
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.3067 0.9351 0.084 0.004 0.024 0.888
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.3088 0.7093 0.128 0.000 0.864 0.008
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.2741 0.9755 0.096 0.000 0.012 0.892
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0524 0.8541 0.004 0.988 0.000 0.008
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.4057 0.7910 0.008 0.836 0.120 0.036
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.6383 0.5212 0.016 0.600 0.336 0.048
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.5473 0.4064 0.644 0.000 0.032 0.324
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0524 0.8541 0.004 0.988 0.000 0.008
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.1256 0.8509 0.008 0.964 0.000 0.028
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.5399 0.3652 0.468 0.000 0.520 0.012
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.3533 0.5287 0.864 0.000 0.056 0.080
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.6819 0.3008 0.540 0.000 0.348 0.112
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.6123 0.3988 0.572 0.000 0.056 0.372
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.6181 0.5177 0.668 0.000 0.204 0.128
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.9252 0.1637 0.376 0.172 0.340 0.112
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.6746 0.3091 0.552 0.000 0.340 0.108
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.6063 0.5228 0.680 0.000 0.196 0.124
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.6369 0.4144 0.572 0.000 0.076 0.352
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.3605 0.8093 0.004 0.864 0.088 0.044
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.5660 0.3488 0.576 0.000 0.028 0.396
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.2611 0.9771 0.096 0.000 0.008 0.896
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 3 0.7750 -0.1544 0.064 0.412 0.460 0.064
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0895 0.8522 0.004 0.976 0.000 0.020
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.5805 0.4045 0.388 0.576 0.000 0.036
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.2510 0.6566 0.008 0.064 0.916 0.012
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.5942 0.3354 0.548 0.000 0.040 0.412
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.5664 0.6472 0.228 0.696 0.000 0.076
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.6383 0.4096 0.568 0.000 0.076 0.356
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.1733 0.8457 0.024 0.948 0.000 0.028
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.6937 0.2467 0.508 0.000 0.376 0.116
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.2611 0.9771 0.096 0.000 0.008 0.896
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.3591 0.6840 0.168 0.000 0.824 0.008
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0188 0.8544 0.000 0.996 0.000 0.004
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.5753 0.3590 0.680 0.000 0.248 0.072
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.2188 0.6672 0.012 0.032 0.936 0.020
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.7374 0.6541 0.136 0.640 0.164 0.060
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.4936 0.5372 0.372 0.000 0.624 0.004
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.2675 0.7101 0.100 0.000 0.892 0.008
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.2976 0.7106 0.120 0.000 0.872 0.008
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.5904 0.5884 0.020 0.652 0.300 0.028
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.4123 0.8025 0.012 0.844 0.088 0.056
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.8544 0.000 1.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.6240 0.5193 0.664 0.000 0.200 0.136
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.7790 -0.1094 0.068 0.396 0.472 0.064
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.7316 -0.0936 0.396 0.000 0.400 0.056 0.148
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 5 0.7463 0.2341 0.004 0.244 0.140 0.092 0.520
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.1750 0.8368 0.000 0.936 0.000 0.028 0.036
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.2963 0.7936 0.048 0.888 0.004 0.016 0.044
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.5451 0.5100 0.144 0.000 0.100 0.040 0.716
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.6319 0.1875 0.052 0.000 0.472 0.048 0.428
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 5 0.6470 -0.1141 0.032 0.000 0.392 0.088 0.488
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 5 0.7414 0.1565 0.000 0.284 0.132 0.092 0.492
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0510 0.7489 0.016 0.000 0.984 0.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.3046 0.8232 0.880 0.000 0.052 0.048 0.020
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.3016 0.6820 0.132 0.000 0.848 0.000 0.020
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 5 0.7459 0.0409 0.068 0.312 0.024 0.092 0.504
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.4325 0.6052 0.000 0.004 0.756 0.048 0.192
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.3951 0.6638 0.812 0.000 0.016 0.044 0.128
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0771 0.7476 0.020 0.000 0.976 0.000 0.004
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 5 0.8316 0.1886 0.024 0.220 0.180 0.116 0.460
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.1211 0.8419 0.000 0.960 0.000 0.024 0.016
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.1281 0.8040 0.956 0.000 0.012 0.000 0.032
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0579 0.8434 0.000 0.984 0.000 0.008 0.008
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.3522 0.9869 0.212 0.000 0.004 0.780 0.004
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0912 0.8433 0.000 0.972 0.000 0.016 0.012
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.4446 0.6104 0.004 0.004 0.756 0.048 0.188
#> F325847E-F046-4B67-B01C-16919C401020 3 0.5641 0.5449 0.124 0.000 0.684 0.024 0.168
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.1983 0.7286 0.060 0.000 0.924 0.008 0.008
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.1547 0.8371 0.004 0.948 0.000 0.032 0.016
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.3522 0.9869 0.212 0.000 0.004 0.780 0.004
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.4291 0.6112 0.004 0.000 0.760 0.048 0.188
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.6768 0.1747 0.556 0.000 0.140 0.044 0.260
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.6343 0.4869 0.184 0.000 0.624 0.040 0.152
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.1195 0.8091 0.960 0.000 0.012 0.000 0.028
#> A8E48877-F8AB-44DD-A18B-194D87C44931 5 0.7504 0.2729 0.004 0.216 0.164 0.092 0.524
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.1569 0.7422 0.012 0.000 0.948 0.008 0.032
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.1787 0.8334 0.004 0.936 0.000 0.044 0.016
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.3082 0.8220 0.880 0.000 0.052 0.036 0.032
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.7580 0.3843 0.268 0.000 0.156 0.096 0.480
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.2844 0.8106 0.880 0.000 0.020 0.088 0.012
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.3522 0.9869 0.212 0.000 0.004 0.780 0.004
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0510 0.7489 0.016 0.000 0.984 0.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.5598 0.3030 0.000 0.000 0.524 0.076 0.400
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.1668 0.8381 0.000 0.940 0.000 0.028 0.032
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0671 0.7485 0.016 0.000 0.980 0.000 0.004
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.3522 0.9869 0.212 0.000 0.004 0.780 0.004
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.4946 0.5393 0.000 0.004 0.696 0.068 0.232
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.3522 0.9869 0.212 0.000 0.004 0.780 0.004
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.6872 0.3120 0.336 0.000 0.064 0.092 0.508
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.6678 0.4187 0.224 0.000 0.576 0.040 0.160
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 5 0.7504 0.2729 0.004 0.216 0.164 0.092 0.524
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.1012 0.7479 0.020 0.000 0.968 0.000 0.012
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.6890 0.3975 0.268 0.000 0.112 0.068 0.552
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.4840 0.4003 0.008 0.032 0.176 0.032 0.752
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.6634 0.3308 0.352 0.000 0.068 0.064 0.516
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.1579 0.8050 0.944 0.000 0.000 0.024 0.032
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 5 0.5430 0.4240 0.084 0.000 0.080 0.104 0.732
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.6996 0.3820 0.300 0.000 0.100 0.076 0.524
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.6434 0.5264 0.128 0.648 0.004 0.068 0.152
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.3038 0.8085 0.872 0.000 0.024 0.088 0.016
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.4090 0.8683 0.268 0.000 0.000 0.716 0.016
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.5057 0.6704 0.000 0.716 0.008 0.100 0.176
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.7012 0.4271 0.252 0.000 0.160 0.052 0.536
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.7646 0.3776 0.264 0.000 0.168 0.096 0.472
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.3082 0.8220 0.880 0.000 0.052 0.036 0.032
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.6592 0.4348 0.200 0.000 0.596 0.044 0.160
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0912 0.8410 0.000 0.972 0.000 0.016 0.012
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.1525 0.8287 0.948 0.000 0.012 0.036 0.004
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.5694 0.5080 0.152 0.000 0.108 0.044 0.696
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0579 0.8434 0.000 0.984 0.000 0.008 0.008
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.7094 0.3622 0.316 0.000 0.100 0.080 0.504
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.3634 0.6299 0.184 0.000 0.796 0.012 0.008
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.3522 0.9869 0.212 0.000 0.004 0.780 0.004
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.1074 0.8403 0.004 0.968 0.000 0.016 0.012
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0579 0.8434 0.000 0.984 0.000 0.008 0.008
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.2935 0.8094 0.876 0.000 0.024 0.088 0.012
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.3830 0.6765 0.824 0.000 0.020 0.040 0.116
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.2037 0.7191 0.004 0.000 0.920 0.012 0.064
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.1314 0.7464 0.016 0.000 0.960 0.012 0.012
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.3522 0.9869 0.212 0.000 0.004 0.780 0.004
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.7017 0.3342 0.336 0.000 0.112 0.060 0.492
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.1493 0.8399 0.000 0.948 0.000 0.028 0.024
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.1124 0.7424 0.036 0.000 0.960 0.000 0.004
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0510 0.7489 0.016 0.000 0.984 0.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.4318 0.4306 0.348 0.000 0.644 0.004 0.004
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.1518 0.8126 0.952 0.000 0.012 0.016 0.020
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.3907 0.9749 0.212 0.004 0.004 0.768 0.012
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0671 0.7485 0.016 0.000 0.980 0.000 0.004
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.3643 0.9846 0.212 0.000 0.004 0.776 0.008
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.1493 0.8399 0.000 0.948 0.000 0.028 0.024
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.5204 0.6146 0.000 0.680 0.008 0.076 0.236
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.7547 0.2417 0.000 0.464 0.136 0.096 0.304
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.1195 0.8091 0.960 0.000 0.012 0.000 0.028
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.1493 0.8399 0.000 0.948 0.000 0.028 0.024
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.1682 0.8348 0.004 0.940 0.000 0.044 0.012
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.6575 0.4551 0.184 0.000 0.600 0.044 0.172
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.6705 0.3071 0.364 0.000 0.076 0.060 0.500
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.7431 0.4327 0.188 0.000 0.256 0.068 0.488
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.2827 0.8300 0.892 0.000 0.044 0.044 0.020
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 5 0.7703 0.3725 0.264 0.000 0.184 0.092 0.460
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 5 0.8400 0.2892 0.308 0.092 0.096 0.068 0.436
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.7434 0.4408 0.184 0.000 0.240 0.076 0.500
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.6033 -0.0358 0.476 0.000 0.044 0.036 0.444
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.3046 0.8232 0.880 0.000 0.052 0.048 0.020
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.4796 0.6898 0.000 0.740 0.008 0.088 0.164
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.1704 0.8195 0.928 0.000 0.000 0.068 0.004
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.3522 0.9869 0.212 0.000 0.004 0.780 0.004
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 5 0.7587 0.2673 0.004 0.216 0.176 0.092 0.512
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0912 0.8410 0.000 0.972 0.000 0.016 0.012
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.6975 0.3141 0.252 0.544 0.004 0.040 0.160
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.4377 0.6076 0.000 0.008 0.760 0.048 0.184
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.2935 0.8094 0.876 0.000 0.024 0.088 0.012
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.6438 0.5326 0.132 0.648 0.004 0.068 0.148
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.3046 0.8232 0.880 0.000 0.052 0.048 0.020
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.2267 0.8250 0.008 0.916 0.000 0.048 0.028
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.5842 0.5009 0.164 0.000 0.100 0.052 0.684
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.3522 0.9869 0.212 0.000 0.004 0.780 0.004
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.1695 0.7359 0.044 0.000 0.940 0.008 0.008
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.1018 0.8435 0.000 0.968 0.000 0.016 0.016
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.6139 0.4565 0.236 0.000 0.076 0.056 0.632
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.4492 0.5958 0.000 0.004 0.744 0.056 0.196
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 5 0.6793 0.0948 0.020 0.304 0.028 0.096 0.552
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.6180 0.4995 0.160 0.000 0.648 0.044 0.148
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.1507 0.7417 0.012 0.000 0.952 0.012 0.024
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0451 0.7478 0.008 0.000 0.988 0.000 0.004
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.6864 0.4478 0.004 0.588 0.120 0.068 0.220
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.5602 0.5801 0.000 0.636 0.008 0.096 0.260
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.1117 0.8438 0.000 0.964 0.000 0.016 0.020
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.7646 0.3776 0.264 0.000 0.168 0.096 0.472
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 5 0.7565 0.2741 0.004 0.212 0.176 0.092 0.516
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.6850 0.0486 0.328 0.000 0.436 0.020 0.184 0.032
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 6 0.5069 0.6829 0.016 0.080 0.048 0.000 0.128 0.728
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.3811 0.7574 0.000 0.804 0.000 0.040 0.040 0.116
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.2686 0.7288 0.008 0.880 0.000 0.008 0.024 0.080
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.3841 0.7377 0.068 0.000 0.020 0.008 0.812 0.092
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 6 0.6837 0.2801 0.004 0.000 0.260 0.048 0.248 0.440
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 6 0.5827 0.4426 0.000 0.000 0.228 0.044 0.128 0.600
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 6 0.4782 0.6847 0.000 0.092 0.048 0.000 0.128 0.732
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0405 0.7527 0.004 0.000 0.988 0.000 0.008 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.1434 0.8706 0.948 0.000 0.024 0.008 0.020 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.2095 0.7283 0.052 0.000 0.916 0.004 0.016 0.012
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 6 0.6025 0.4660 0.020 0.240 0.000 0.024 0.124 0.592
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.4804 0.4159 0.000 0.000 0.656 0.040 0.028 0.276
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.5121 0.5526 0.684 0.000 0.000 0.028 0.152 0.136
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0520 0.7522 0.008 0.000 0.984 0.000 0.008 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 6 0.5107 0.6238 0.000 0.060 0.120 0.044 0.040 0.736
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.3342 0.7733 0.000 0.844 0.000 0.044 0.040 0.072
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.1594 0.8490 0.932 0.000 0.000 0.000 0.052 0.016
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0146 0.7860 0.000 0.996 0.000 0.000 0.000 0.004
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.2932 0.9926 0.164 0.000 0.000 0.820 0.016 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.2777 0.7813 0.000 0.880 0.000 0.036 0.036 0.048
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.4637 0.4511 0.000 0.000 0.680 0.036 0.028 0.256
#> F325847E-F046-4B67-B01C-16919C401020 3 0.5691 0.5347 0.032 0.000 0.660 0.020 0.156 0.132
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.1242 0.7469 0.012 0.000 0.960 0.008 0.008 0.012
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.2032 0.7626 0.000 0.920 0.000 0.024 0.036 0.020
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.2932 0.9926 0.164 0.000 0.000 0.820 0.016 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.4751 0.4133 0.000 0.000 0.656 0.040 0.024 0.280
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.7832 0.0105 0.400 0.000 0.192 0.028 0.236 0.144
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.6588 0.4840 0.080 0.000 0.592 0.028 0.164 0.136
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.1225 0.8527 0.952 0.000 0.000 0.000 0.036 0.012
#> A8E48877-F8AB-44DD-A18B-194D87C44931 6 0.5142 0.6869 0.016 0.072 0.060 0.000 0.128 0.724
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.2112 0.7313 0.000 0.000 0.916 0.020 0.028 0.036
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.2763 0.7449 0.000 0.880 0.000 0.040 0.052 0.028
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.1434 0.8706 0.948 0.000 0.024 0.008 0.020 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.5385 0.7398 0.120 0.000 0.060 0.036 0.716 0.068
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.2127 0.8684 0.920 0.000 0.024 0.008 0.016 0.032
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.2932 0.9926 0.164 0.000 0.000 0.820 0.016 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0260 0.7529 0.000 0.000 0.992 0.000 0.008 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 6 0.6467 0.1140 0.008 0.000 0.400 0.048 0.112 0.432
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.3811 0.7574 0.000 0.804 0.000 0.040 0.040 0.116
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0260 0.7529 0.000 0.000 0.992 0.000 0.008 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.2932 0.9926 0.164 0.000 0.000 0.820 0.016 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.4814 0.3515 0.000 0.000 0.628 0.040 0.020 0.312
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.2932 0.9926 0.164 0.000 0.000 0.820 0.016 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.4165 0.7696 0.160 0.000 0.028 0.012 0.772 0.028
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.6716 0.4709 0.092 0.000 0.580 0.028 0.164 0.136
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 6 0.5142 0.6869 0.016 0.072 0.060 0.000 0.128 0.724
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0363 0.7530 0.000 0.000 0.988 0.000 0.012 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.4897 0.7293 0.176 0.000 0.028 0.016 0.720 0.060
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.5713 0.2180 0.020 0.000 0.048 0.032 0.564 0.336
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.4850 0.6895 0.116 0.000 0.028 0.004 0.724 0.128
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.1930 0.8493 0.916 0.000 0.000 0.000 0.036 0.048
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 6 0.4025 0.4933 0.016 0.000 0.024 0.000 0.228 0.732
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.4639 0.7423 0.104 0.000 0.032 0.028 0.768 0.068
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.6291 0.4786 0.044 0.624 0.000 0.044 0.140 0.148
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.1971 0.8681 0.928 0.000 0.024 0.008 0.016 0.024
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.3416 0.9237 0.140 0.000 0.000 0.804 0.056 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.5442 0.3051 0.000 0.512 0.000 0.044 0.040 0.404
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.3798 0.7625 0.096 0.000 0.056 0.008 0.816 0.024
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.5538 0.7400 0.148 0.000 0.060 0.036 0.696 0.060
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.1434 0.8706 0.948 0.000 0.024 0.008 0.020 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.6882 0.4627 0.084 0.000 0.568 0.040 0.168 0.140
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.7862 0.000 1.000 0.000 0.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0717 0.8691 0.976 0.000 0.000 0.000 0.016 0.008
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.3873 0.7404 0.068 0.000 0.024 0.008 0.812 0.088
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0146 0.7860 0.000 0.996 0.000 0.000 0.000 0.004
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.5774 0.6610 0.108 0.000 0.032 0.032 0.660 0.168
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.2971 0.6893 0.100 0.000 0.860 0.012 0.012 0.016
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.2932 0.9926 0.164 0.000 0.000 0.820 0.016 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0405 0.7866 0.000 0.988 0.000 0.004 0.000 0.008
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0146 0.7860 0.000 0.996 0.000 0.000 0.000 0.004
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.2127 0.8684 0.920 0.000 0.024 0.008 0.016 0.032
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.5016 0.5609 0.696 0.000 0.000 0.028 0.144 0.132
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.1874 0.7278 0.000 0.000 0.928 0.028 0.016 0.028
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.1353 0.7403 0.000 0.000 0.952 0.024 0.012 0.012
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.2932 0.9926 0.164 0.000 0.000 0.820 0.016 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.5445 0.6768 0.116 0.000 0.044 0.016 0.692 0.132
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.3789 0.7599 0.000 0.808 0.000 0.044 0.040 0.108
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0924 0.7502 0.008 0.000 0.972 0.008 0.008 0.004
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0260 0.7529 0.000 0.000 0.992 0.000 0.008 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.3938 0.4720 0.312 0.000 0.672 0.012 0.004 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.1408 0.8608 0.944 0.000 0.000 0.000 0.020 0.036
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.3037 0.9875 0.160 0.000 0.000 0.820 0.016 0.004
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0551 0.7521 0.000 0.000 0.984 0.004 0.008 0.004
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.2932 0.9926 0.164 0.000 0.000 0.820 0.016 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.3789 0.7599 0.000 0.808 0.000 0.044 0.040 0.108
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.6192 0.3142 0.000 0.520 0.012 0.076 0.052 0.340
#> 53A96249-66D5-4C26-893B-ADC71481D261 6 0.6651 0.1998 0.000 0.372 0.056 0.056 0.048 0.468
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.1082 0.8577 0.956 0.000 0.000 0.000 0.040 0.004
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.3789 0.7599 0.000 0.808 0.000 0.044 0.040 0.108
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.2007 0.7631 0.000 0.920 0.000 0.032 0.036 0.012
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.6542 0.4849 0.076 0.000 0.596 0.028 0.164 0.136
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.5030 0.6862 0.120 0.000 0.036 0.004 0.712 0.128
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.5017 0.7283 0.120 0.000 0.136 0.012 0.712 0.020
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.1346 0.8712 0.952 0.000 0.024 0.008 0.016 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 5 0.4898 0.7589 0.148 0.000 0.084 0.016 0.728 0.024
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 5 0.7845 0.2278 0.272 0.032 0.024 0.048 0.392 0.232
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.5015 0.7486 0.112 0.000 0.108 0.012 0.728 0.040
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 5 0.5501 0.4809 0.376 0.000 0.012 0.020 0.540 0.052
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.1434 0.8706 0.948 0.000 0.024 0.008 0.020 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.5439 0.4074 0.000 0.560 0.000 0.052 0.040 0.348
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.1296 0.8658 0.952 0.000 0.000 0.004 0.012 0.032
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.2932 0.9926 0.164 0.000 0.000 0.820 0.016 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 6 0.5142 0.6869 0.016 0.072 0.060 0.000 0.128 0.724
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0146 0.7860 0.000 0.996 0.000 0.000 0.000 0.004
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.6513 0.3827 0.116 0.596 0.000 0.016 0.140 0.132
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.4701 0.4445 0.000 0.000 0.676 0.040 0.028 0.256
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.2127 0.8684 0.920 0.000 0.024 0.008 0.016 0.032
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.6258 0.4855 0.044 0.628 0.000 0.044 0.140 0.144
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.1346 0.8712 0.952 0.000 0.024 0.008 0.016 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.3150 0.7293 0.000 0.856 0.000 0.040 0.068 0.036
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.4324 0.7384 0.092 0.000 0.024 0.008 0.776 0.100
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.2932 0.9926 0.164 0.000 0.000 0.820 0.016 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0924 0.7502 0.008 0.000 0.972 0.008 0.008 0.004
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.2973 0.7783 0.000 0.868 0.000 0.036 0.040 0.056
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.4808 0.6554 0.080 0.000 0.012 0.012 0.712 0.184
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.4770 0.4054 0.000 0.000 0.652 0.040 0.024 0.284
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 6 0.3544 0.6595 0.000 0.104 0.004 0.004 0.072 0.816
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.6431 0.4907 0.064 0.000 0.604 0.028 0.164 0.140
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.2032 0.7284 0.000 0.000 0.920 0.036 0.020 0.024
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0146 0.7523 0.000 0.000 0.996 0.000 0.004 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.6390 0.2243 0.000 0.528 0.036 0.056 0.056 0.324
#> A608BCEB-2C27-4927-A308-E6975F641722 6 0.4613 0.1845 0.000 0.352 0.000 0.020 0.020 0.608
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.2709 0.7823 0.000 0.884 0.000 0.032 0.040 0.044
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.5538 0.7400 0.148 0.000 0.060 0.036 0.696 0.060
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 6 0.5142 0.6869 0.016 0.072 0.060 0.000 0.128 0.724
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "skmeans"]
# you can also extract it by
# res = res_list["SD:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.975 0.989 0.4931 0.506 0.506
#> 3 3 0.961 0.917 0.959 0.3449 0.752 0.545
#> 4 4 0.755 0.629 0.828 0.1255 0.898 0.706
#> 5 5 0.782 0.790 0.868 0.0560 0.914 0.694
#> 6 6 0.822 0.791 0.882 0.0488 0.935 0.719
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.0000 0.991 1.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.0000 0.984 0.000 1.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.984 0.000 1.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0000 0.984 0.000 1.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 2 0.5059 0.875 0.112 0.888
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 2 0.7674 0.724 0.224 0.776
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 2 0.0000 0.984 0.000 1.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0000 0.984 0.000 1.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.0000 0.991 1.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.991 1.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.0000 0.991 1.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.0000 0.984 0.000 1.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.0000 0.984 0.000 1.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.991 1.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.0000 0.991 1.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.0000 0.984 0.000 1.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.984 0.000 1.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0000 0.991 1.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.984 0.000 1.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.0000 0.991 1.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.984 0.000 1.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.0000 0.984 0.000 1.000
#> F325847E-F046-4B67-B01C-16919C401020 1 0.0000 0.991 1.000 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.0000 0.991 1.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.984 0.000 1.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.0000 0.991 1.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 2 0.0000 0.984 0.000 1.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0000 0.991 1.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.0000 0.991 1.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.991 1.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.0000 0.984 0.000 1.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.0000 0.991 1.000 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.984 0.000 1.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.991 1.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.0000 0.991 1.000 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.991 1.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.0000 0.991 1.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.0000 0.991 1.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.0000 0.984 0.000 1.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.984 0.000 1.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.0000 0.991 1.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.0000 0.991 1.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.0000 0.984 0.000 1.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.0000 0.991 1.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.0000 0.991 1.000 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.0000 0.991 1.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.0000 0.984 0.000 1.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.0000 0.991 1.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.0376 0.988 0.996 0.004
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.0000 0.984 0.000 1.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0000 0.991 1.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0000 0.991 1.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 2 0.6343 0.817 0.160 0.840
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.0000 0.991 1.000 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.0000 0.984 0.000 1.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.991 1.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0000 0.991 1.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.984 0.000 1.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.0000 0.991 1.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0000 0.991 1.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.991 1.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.0000 0.991 1.000 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.984 0.000 1.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0000 0.991 1.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.9170 0.494 0.668 0.332
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.984 0.000 1.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.0000 0.991 1.000 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0000 0.991 1.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.0000 0.991 1.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.984 0.000 1.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.984 0.000 1.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.991 1.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.991 1.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.6343 0.805 0.840 0.160
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.0000 0.991 1.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.0000 0.991 1.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0000 0.991 1.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.984 0.000 1.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.0000 0.991 1.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.0000 0.991 1.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.991 1.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.991 1.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.0000 0.984 0.000 1.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.0000 0.991 1.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.0000 0.991 1.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.984 0.000 1.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0000 0.984 0.000 1.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0000 0.984 0.000 1.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.991 1.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.984 0.000 1.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.984 0.000 1.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0000 0.991 1.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0000 0.991 1.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.0000 0.991 1.000 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.991 1.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.0000 0.991 1.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.0000 0.984 0.000 1.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.0000 0.991 1.000 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.4431 0.894 0.908 0.092
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.991 1.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.984 0.000 1.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.991 1.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.0000 0.991 1.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.0000 0.984 0.000 1.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.984 0.000 1.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.0000 0.984 0.000 1.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.0000 0.984 0.000 1.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.991 1.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.0000 0.984 0.000 1.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.991 1.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.984 0.000 1.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 2 0.5059 0.875 0.112 0.888
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.0000 0.991 1.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.0000 0.991 1.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.984 0.000 1.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 2 0.7453 0.743 0.212 0.788
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.0000 0.984 0.000 1.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0000 0.984 0.000 1.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.0672 0.984 0.992 0.008
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.0000 0.991 1.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.0000 0.991 1.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0000 0.984 0.000 1.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.984 0.000 1.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.984 0.000 1.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.0000 0.991 1.000 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.0000 0.984 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.5363 0.6774 0.724 0.000 0.276
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.0000 0.9723 0.000 1.000 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.9723 0.000 1.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0237 0.9696 0.004 0.996 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 2 0.7585 -0.0628 0.476 0.484 0.040
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.0237 0.9573 0.004 0.000 0.996
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.2860 0.9091 0.004 0.084 0.912
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0000 0.9723 0.000 1.000 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.9586 0.000 0.000 1.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.1163 0.9381 0.972 0.000 0.028
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.4291 0.7924 0.180 0.000 0.820
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.0237 0.9696 0.004 0.996 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.2356 0.9203 0.000 0.072 0.928
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.9429 1.000 0.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.9586 0.000 0.000 1.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.0237 0.9693 0.000 0.996 0.004
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.9723 0.000 1.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0000 0.9429 1.000 0.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.9723 0.000 1.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.0237 0.9434 0.996 0.000 0.004
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.9723 0.000 1.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.2356 0.9203 0.000 0.072 0.928
#> F325847E-F046-4B67-B01C-16919C401020 3 0.0237 0.9573 0.004 0.000 0.996
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.9586 0.000 0.000 1.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.9723 0.000 1.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.0237 0.9434 0.996 0.000 0.004
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.0237 0.9573 0.000 0.004 0.996
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.1964 0.9246 0.944 0.000 0.056
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.1163 0.9479 0.028 0.000 0.972
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.9429 1.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.0000 0.9723 0.000 1.000 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.0000 0.9586 0.000 0.000 1.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.9723 0.000 1.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.1163 0.9381 0.972 0.000 0.028
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.2066 0.9249 0.940 0.000 0.060
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0237 0.9434 0.996 0.000 0.004
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.0237 0.9434 0.996 0.000 0.004
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0000 0.9586 0.000 0.000 1.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.2711 0.9067 0.000 0.088 0.912
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.9723 0.000 1.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0000 0.9586 0.000 0.000 1.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.0237 0.9434 0.996 0.000 0.004
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.2356 0.9203 0.000 0.072 0.928
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.0237 0.9434 0.996 0.000 0.004
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.0000 0.9429 1.000 0.000 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.1529 0.9425 0.040 0.000 0.960
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.0000 0.9723 0.000 1.000 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0000 0.9586 0.000 0.000 1.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.2066 0.9291 0.940 0.000 0.060
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.0000 0.9723 0.000 1.000 0.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.1529 0.9320 0.960 0.000 0.040
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0000 0.9429 1.000 0.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 2 0.2261 0.9146 0.068 0.932 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.1964 0.9246 0.944 0.000 0.056
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.0892 0.9574 0.020 0.980 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0747 0.9419 0.984 0.000 0.016
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0000 0.9429 1.000 0.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.9723 0.000 1.000 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.2711 0.9111 0.912 0.000 0.088
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.2625 0.9157 0.916 0.000 0.084
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.1163 0.9381 0.972 0.000 0.028
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.1529 0.9425 0.040 0.000 0.960
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.9723 0.000 1.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0000 0.9429 1.000 0.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.7029 0.2737 0.540 0.020 0.440
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.9723 0.000 1.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.1964 0.9246 0.944 0.000 0.056
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.4062 0.7951 0.164 0.000 0.836
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.0237 0.9434 0.996 0.000 0.004
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.9723 0.000 1.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.9723 0.000 1.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0747 0.9419 0.984 0.000 0.016
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.9429 1.000 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0000 0.9586 0.000 0.000 1.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0000 0.9586 0.000 0.000 1.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.0237 0.9434 0.996 0.000 0.004
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.1964 0.9246 0.944 0.000 0.056
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.9723 0.000 1.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.9586 0.000 0.000 1.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0000 0.9586 0.000 0.000 1.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.4842 0.7387 0.224 0.000 0.776
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.9429 1.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.4235 0.7830 0.176 0.824 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.9586 0.000 0.000 1.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.0237 0.9434 0.996 0.000 0.004
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.9723 0.000 1.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0000 0.9723 0.000 1.000 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0000 0.9723 0.000 1.000 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.9429 1.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.9723 0.000 1.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.9723 0.000 1.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.1643 0.9415 0.044 0.000 0.956
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.1860 0.9267 0.948 0.000 0.052
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.2959 0.9046 0.900 0.000 0.100
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0747 0.9419 0.984 0.000 0.016
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.2878 0.9076 0.904 0.000 0.096
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.5060 0.7748 0.156 0.816 0.028
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.2878 0.9076 0.904 0.000 0.096
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.1163 0.9381 0.972 0.000 0.028
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.1163 0.9381 0.972 0.000 0.028
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.9723 0.000 1.000 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.9429 1.000 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.0237 0.9434 0.996 0.000 0.004
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.0000 0.9723 0.000 1.000 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.9723 0.000 1.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.1289 0.9477 0.032 0.968 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.2356 0.9203 0.000 0.072 0.928
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0747 0.9419 0.984 0.000 0.016
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.1753 0.9345 0.048 0.952 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.1163 0.9381 0.972 0.000 0.028
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.9723 0.000 1.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.7498 0.2737 0.548 0.412 0.040
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.0237 0.9434 0.996 0.000 0.004
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.9586 0.000 0.000 1.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.9723 0.000 1.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.7192 0.3545 0.588 0.380 0.032
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.2066 0.9288 0.000 0.060 0.940
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0000 0.9723 0.000 1.000 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.1163 0.9479 0.028 0.000 0.972
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0000 0.9586 0.000 0.000 1.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.9586 0.000 0.000 1.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0000 0.9723 0.000 1.000 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.9723 0.000 1.000 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.9723 0.000 1.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.2625 0.9157 0.916 0.000 0.084
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.0000 0.9723 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.7613 -0.2537 0.440 0.000 0.208 0.352
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.1022 0.9337 0.032 0.968 0.000 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.2281 0.8664 0.096 0.904 0.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.5247 0.5045 0.684 0.032 0.000 0.284
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.3975 0.7526 0.240 0.000 0.760 0.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.4353 0.7503 0.232 0.012 0.756 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0817 0.9367 0.024 0.976 0.000 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.8870 0.000 0.000 1.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 4 0.5352 0.5344 0.388 0.000 0.016 0.596
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.5478 0.5634 0.248 0.000 0.696 0.056
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.3942 0.7303 0.236 0.764 0.000 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.1151 0.8772 0.024 0.008 0.968 0.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.4817 -0.2287 0.612 0.000 0.000 0.388
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.8870 0.000 0.000 1.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.0817 0.9367 0.024 0.976 0.000 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.4994 -0.3801 0.520 0.000 0.000 0.480
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 0.5258 0.000 0.000 0.000 1.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.1004 0.8784 0.024 0.004 0.972 0.000
#> F325847E-F046-4B67-B01C-16919C401020 3 0.3801 0.7542 0.220 0.000 0.780 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.8870 0.000 0.000 1.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 0.5258 0.000 0.000 0.000 1.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.0817 0.8791 0.024 0.000 0.976 0.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.5036 -0.0724 0.696 0.000 0.024 0.280
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.3569 0.7775 0.196 0.000 0.804 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.4992 -0.3737 0.524 0.000 0.000 0.476
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.1118 0.9317 0.036 0.964 0.000 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.0000 0.8870 0.000 0.000 1.000 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 4 0.5352 0.5344 0.388 0.000 0.016 0.596
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.6077 0.3563 0.496 0.000 0.044 0.460
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 4 0.4713 0.5469 0.360 0.000 0.000 0.640
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 0.5258 0.000 0.000 0.000 1.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0000 0.8870 0.000 0.000 1.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.3015 0.8161 0.024 0.092 0.884 0.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0000 0.8870 0.000 0.000 1.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 0.5258 0.000 0.000 0.000 1.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.1284 0.8753 0.024 0.012 0.964 0.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 0.5258 0.000 0.000 0.000 1.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 4 0.4996 -0.3715 0.484 0.000 0.000 0.516
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.4220 0.7243 0.248 0.000 0.748 0.004
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.1118 0.9317 0.036 0.964 0.000 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0000 0.8870 0.000 0.000 1.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 4 0.5512 -0.3104 0.488 0.000 0.016 0.496
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.4713 0.4856 0.360 0.640 0.000 0.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.4795 0.5170 0.696 0.000 0.012 0.292
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 4 0.4941 0.4639 0.436 0.000 0.000 0.564
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.5878 0.2701 0.632 0.312 0.000 0.056
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.5013 0.5180 0.688 0.000 0.020 0.292
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.3688 0.7437 0.208 0.792 0.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 4 0.4730 0.5472 0.364 0.000 0.000 0.636
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.3688 0.1958 0.208 0.000 0.000 0.792
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0817 0.9367 0.024 0.976 0.000 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.5088 0.5180 0.688 0.000 0.024 0.288
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.6211 0.3486 0.488 0.000 0.052 0.460
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 4 0.5352 0.5344 0.388 0.000 0.016 0.596
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.4220 0.7243 0.248 0.000 0.748 0.004
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 4 0.4877 0.5196 0.408 0.000 0.000 0.592
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.5265 0.5076 0.684 0.004 0.024 0.288
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.4988 0.5187 0.692 0.000 0.020 0.288
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.5312 0.5566 0.268 0.000 0.692 0.040
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 0.5258 0.000 0.000 0.000 1.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 4 0.4730 0.5472 0.364 0.000 0.000 0.636
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.4817 -0.2287 0.612 0.000 0.000 0.388
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0000 0.8870 0.000 0.000 1.000 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0000 0.8870 0.000 0.000 1.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 0.5258 0.000 0.000 0.000 1.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.4955 0.5178 0.708 0.000 0.024 0.268
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.8870 0.000 0.000 1.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0000 0.8870 0.000 0.000 1.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.7044 0.2942 0.276 0.000 0.560 0.164
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 4 0.4933 0.4725 0.432 0.000 0.000 0.568
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0188 0.5225 0.000 0.004 0.000 0.996
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.8870 0.000 0.000 1.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0000 0.5258 0.000 0.000 0.000 1.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0817 0.9367 0.024 0.976 0.000 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.4996 -0.3873 0.516 0.000 0.000 0.484
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.4008 0.7335 0.244 0.000 0.756 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.4936 0.5192 0.700 0.000 0.020 0.280
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 4 0.7146 -0.3709 0.412 0.000 0.132 0.456
#> C2662596-6E2F-4924-B051-CEA1AC87B197 4 0.5352 0.5344 0.388 0.000 0.016 0.596
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 4 0.6549 -0.3578 0.436 0.000 0.076 0.488
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.7102 0.4080 0.148 0.612 0.016 0.224
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.7037 0.3522 0.464 0.000 0.120 0.416
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.4535 0.0473 0.744 0.000 0.016 0.240
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 4 0.5352 0.5344 0.388 0.000 0.016 0.596
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0817 0.9367 0.024 0.976 0.000 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 4 0.4746 0.5428 0.368 0.000 0.000 0.632
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 0.5258 0.000 0.000 0.000 1.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.1022 0.9337 0.032 0.968 0.000 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.4509 0.6341 0.288 0.708 0.000 0.004
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.1151 0.8772 0.024 0.008 0.968 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 4 0.4730 0.5472 0.364 0.000 0.000 0.636
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.5066 0.7305 0.148 0.764 0.000 0.088
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 4 0.5352 0.5344 0.388 0.000 0.016 0.596
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.5649 0.4411 0.580 0.028 0.000 0.392
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 0.5258 0.000 0.000 0.000 1.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.8870 0.000 0.000 1.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.5157 0.5056 0.688 0.028 0.000 0.284
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.1151 0.8772 0.024 0.008 0.968 0.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0817 0.9367 0.024 0.976 0.000 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.3942 0.7396 0.236 0.000 0.764 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0000 0.8870 0.000 0.000 1.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.8870 0.000 0.000 1.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0817 0.9367 0.024 0.976 0.000 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.9425 0.000 1.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.6211 0.3486 0.488 0.000 0.052 0.460
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.1118 0.9317 0.036 0.964 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.2753 0.7752 0.856 0.000 0.136 0.000 0.008
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.4197 0.7995 0.000 0.776 0.000 0.148 0.076
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.8801 0.000 1.000 0.000 0.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.1894 0.8312 0.000 0.920 0.000 0.008 0.072
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.1211 0.7822 0.024 0.000 0.000 0.016 0.960
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.6009 0.5177 0.000 0.000 0.544 0.136 0.320
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.6705 0.4769 0.000 0.024 0.512 0.148 0.316
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.4078 0.8041 0.000 0.784 0.000 0.148 0.068
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.8964 1.000 0.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.4161 0.2370 0.392 0.000 0.608 0.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.6086 0.5201 0.000 0.544 0.000 0.152 0.304
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.2438 0.8075 0.000 0.000 0.900 0.060 0.040
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.3885 0.6284 0.724 0.000 0.000 0.008 0.268
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.5136 0.7718 0.000 0.736 0.032 0.148 0.084
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.8801 0.000 1.000 0.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0955 0.8907 0.968 0.000 0.000 0.004 0.028
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.8801 0.000 1.000 0.000 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.3346 0.9922 0.092 0.000 0.000 0.844 0.064
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.8801 0.000 1.000 0.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.2074 0.8165 0.000 0.000 0.920 0.044 0.036
#> F325847E-F046-4B67-B01C-16919C401020 3 0.3835 0.6715 0.000 0.000 0.732 0.008 0.260
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.8801 0.000 1.000 0.000 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.3346 0.9922 0.092 0.000 0.000 0.844 0.064
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.2438 0.8075 0.000 0.000 0.900 0.060 0.040
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.5590 0.4853 0.608 0.000 0.076 0.008 0.308
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.3611 0.7155 0.004 0.000 0.780 0.008 0.208
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0955 0.8907 0.968 0.000 0.000 0.004 0.028
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.4409 0.7953 0.004 0.768 0.000 0.148 0.080
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.8801 0.000 1.000 0.000 0.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.8964 1.000 0.000 0.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.5767 0.7115 0.156 0.000 0.036 0.124 0.684
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0609 0.8959 0.980 0.000 0.000 0.020 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.3346 0.9922 0.092 0.000 0.000 0.844 0.064
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.5821 0.6392 0.000 0.124 0.696 0.116 0.064
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.8801 0.000 1.000 0.000 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.3346 0.9922 0.092 0.000 0.000 0.844 0.064
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.3350 0.7699 0.000 0.004 0.844 0.112 0.040
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.3346 0.9922 0.092 0.000 0.000 0.844 0.064
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.4333 0.7036 0.048 0.000 0.000 0.212 0.740
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.4265 0.6538 0.012 0.000 0.712 0.008 0.268
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.4409 0.7953 0.004 0.768 0.000 0.148 0.080
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.4384 0.6013 0.324 0.000 0.000 0.016 0.660
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.5788 0.3459 0.000 0.300 0.000 0.120 0.580
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.1907 0.7824 0.044 0.000 0.000 0.028 0.928
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0865 0.8958 0.972 0.000 0.000 0.024 0.004
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 5 0.3930 0.6081 0.000 0.056 0.000 0.152 0.792
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.2693 0.7873 0.028 0.000 0.016 0.060 0.896
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.3671 0.6470 0.000 0.756 0.000 0.008 0.236
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0609 0.8959 0.980 0.000 0.000 0.020 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.4002 0.9107 0.084 0.000 0.000 0.796 0.120
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.3506 0.8227 0.000 0.824 0.000 0.132 0.044
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.2564 0.7930 0.052 0.000 0.024 0.020 0.904
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.5767 0.7115 0.156 0.000 0.036 0.124 0.684
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.8964 1.000 0.000 0.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.4493 0.6428 0.016 0.000 0.700 0.012 0.272
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.8801 0.000 1.000 0.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0771 0.8958 0.976 0.000 0.000 0.020 0.004
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.1106 0.7860 0.024 0.000 0.000 0.012 0.964
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.8801 0.000 1.000 0.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.2228 0.7819 0.020 0.000 0.016 0.044 0.920
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.4268 0.0682 0.444 0.000 0.556 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.3346 0.9922 0.092 0.000 0.000 0.844 0.064
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.8801 0.000 1.000 0.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.8801 0.000 1.000 0.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0609 0.8959 0.980 0.000 0.000 0.020 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.3756 0.6547 0.744 0.000 0.000 0.008 0.248
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0162 0.8426 0.000 0.000 0.996 0.000 0.004
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.3346 0.9922 0.092 0.000 0.000 0.844 0.064
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.2060 0.7761 0.024 0.000 0.036 0.012 0.928
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.8801 0.000 1.000 0.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.4273 0.2089 0.552 0.000 0.448 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0771 0.8958 0.976 0.000 0.000 0.020 0.004
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.3346 0.9922 0.092 0.000 0.000 0.844 0.064
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.3346 0.9922 0.092 0.000 0.000 0.844 0.064
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.8801 0.000 1.000 0.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0162 0.8792 0.000 0.996 0.000 0.004 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.3317 0.8290 0.000 0.840 0.000 0.116 0.044
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0955 0.8907 0.968 0.000 0.000 0.004 0.028
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.8801 0.000 1.000 0.000 0.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.8801 0.000 1.000 0.000 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.3861 0.6680 0.000 0.000 0.728 0.008 0.264
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.1907 0.7824 0.044 0.000 0.000 0.028 0.928
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.6108 0.6495 0.044 0.000 0.152 0.148 0.656
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.8964 1.000 0.000 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 5 0.6080 0.6884 0.096 0.000 0.064 0.176 0.664
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.4889 0.0593 0.476 0.504 0.000 0.004 0.016
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.5848 0.6997 0.060 0.000 0.124 0.124 0.692
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.2068 0.8085 0.904 0.000 0.000 0.004 0.092
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.8964 1.000 0.000 0.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.3365 0.8275 0.000 0.836 0.000 0.120 0.044
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0771 0.8958 0.976 0.000 0.000 0.020 0.004
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.3346 0.9922 0.092 0.000 0.000 0.844 0.064
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.4409 0.7953 0.004 0.768 0.000 0.148 0.080
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.8801 0.000 1.000 0.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.5361 0.5323 0.084 0.664 0.000 0.008 0.244
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.2308 0.8136 0.000 0.004 0.912 0.048 0.036
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0609 0.8959 0.980 0.000 0.000 0.020 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.4937 0.5406 0.000 0.672 0.000 0.264 0.064
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.8964 1.000 0.000 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.8801 0.000 1.000 0.000 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.2729 0.7845 0.056 0.000 0.000 0.060 0.884
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.3346 0.9922 0.092 0.000 0.000 0.844 0.064
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.8801 0.000 1.000 0.000 0.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.0671 0.7733 0.004 0.000 0.000 0.016 0.980
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.2754 0.7952 0.000 0.000 0.880 0.080 0.040
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.4036 0.8057 0.000 0.788 0.000 0.144 0.068
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.3835 0.6715 0.000 0.000 0.732 0.008 0.260
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0000 0.8801 0.000 1.000 0.000 0.000 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.3911 0.8091 0.000 0.796 0.000 0.144 0.060
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.8801 0.000 1.000 0.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.5767 0.7115 0.156 0.000 0.036 0.124 0.684
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.4409 0.7953 0.004 0.768 0.000 0.148 0.080
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.4572 0.7014 0.740 0.000 0.176 0.012 0.028 0.044
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 6 0.2402 0.8233 0.000 0.120 0.000 0.000 0.012 0.868
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.1663 0.8271 0.000 0.912 0.000 0.000 0.000 0.088
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.0806 0.8477 0.008 0.000 0.000 0.000 0.972 0.020
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 6 0.4894 0.4998 0.000 0.000 0.212 0.012 0.100 0.676
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 6 0.3257 0.6936 0.000 0.000 0.084 0.012 0.064 0.840
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 6 0.2402 0.8233 0.000 0.120 0.000 0.000 0.012 0.868
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.8292 0.000 0.000 1.000 0.000 0.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0146 0.9251 0.996 0.000 0.000 0.004 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.2135 0.7464 0.128 0.000 0.872 0.000 0.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 6 0.3072 0.7203 0.000 0.084 0.000 0.000 0.076 0.840
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.3314 0.6330 0.000 0.000 0.764 0.012 0.000 0.224
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.4613 0.6343 0.692 0.000 0.000 0.000 0.180 0.128
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.8292 0.000 0.000 1.000 0.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 6 0.3268 0.7705 0.000 0.096 0.024 0.012 0.020 0.848
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0458 0.9218 0.984 0.000 0.000 0.000 0.016 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0622 0.9979 0.012 0.000 0.000 0.980 0.008 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.2805 0.7098 0.000 0.000 0.828 0.012 0.000 0.160
#> F325847E-F046-4B67-B01C-16919C401020 3 0.4387 0.6523 0.000 0.000 0.720 0.000 0.152 0.128
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.8292 0.000 0.000 1.000 0.000 0.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0622 0.9979 0.012 0.000 0.000 0.980 0.008 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.3141 0.6651 0.000 0.000 0.788 0.012 0.000 0.200
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.6578 0.4806 0.564 0.000 0.100 0.008 0.196 0.132
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.4774 0.6399 0.012 0.000 0.700 0.000 0.172 0.116
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0363 0.9236 0.988 0.000 0.000 0.000 0.012 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 6 0.2402 0.8233 0.000 0.120 0.000 0.000 0.012 0.868
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.0000 0.8292 0.000 0.000 1.000 0.000 0.000 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0146 0.9251 0.996 0.000 0.000 0.004 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.3655 0.8270 0.028 0.000 0.004 0.052 0.824 0.092
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0551 0.9245 0.984 0.000 0.000 0.008 0.004 0.004
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0622 0.9979 0.012 0.000 0.000 0.980 0.008 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0000 0.8292 0.000 0.000 1.000 0.000 0.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 6 0.5205 0.3765 0.000 0.028 0.344 0.012 0.028 0.588
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0000 0.8292 0.000 0.000 1.000 0.000 0.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0622 0.9979 0.012 0.000 0.000 0.980 0.008 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.4076 0.2659 0.000 0.000 0.592 0.012 0.000 0.396
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0622 0.9979 0.012 0.000 0.000 0.980 0.008 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.2959 0.8154 0.008 0.000 0.000 0.124 0.844 0.024
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.5119 0.6128 0.020 0.000 0.672 0.000 0.180 0.128
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 6 0.2402 0.8233 0.000 0.120 0.000 0.000 0.012 0.868
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0000 0.8292 0.000 0.000 1.000 0.000 0.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.2805 0.7525 0.184 0.000 0.000 0.000 0.812 0.004
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.5153 -0.0863 0.000 0.084 0.000 0.000 0.460 0.456
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.1908 0.8072 0.004 0.000 0.000 0.000 0.900 0.096
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0922 0.9209 0.968 0.000 0.000 0.004 0.024 0.004
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 6 0.0260 0.7453 0.000 0.000 0.000 0.000 0.008 0.992
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.2361 0.8420 0.000 0.000 0.000 0.028 0.884 0.088
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.2843 0.7608 0.000 0.848 0.000 0.000 0.036 0.116
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0436 0.9248 0.988 0.000 0.000 0.004 0.004 0.004
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.1088 0.9763 0.016 0.000 0.000 0.960 0.024 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 6 0.3864 0.2558 0.000 0.480 0.000 0.000 0.000 0.520
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.0520 0.8473 0.008 0.000 0.000 0.000 0.984 0.008
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.3820 0.8247 0.040 0.000 0.004 0.052 0.816 0.088
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0146 0.9251 0.996 0.000 0.000 0.004 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.5195 0.6091 0.024 0.000 0.668 0.000 0.180 0.128
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0748 0.9244 0.976 0.000 0.000 0.004 0.016 0.004
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.0520 0.8473 0.008 0.000 0.000 0.000 0.984 0.008
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.2982 0.7938 0.004 0.000 0.000 0.012 0.820 0.164
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.2805 0.6927 0.184 0.000 0.812 0.000 0.000 0.004
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0622 0.9979 0.012 0.000 0.000 0.980 0.008 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0436 0.9248 0.988 0.000 0.000 0.004 0.004 0.004
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.4434 0.6561 0.712 0.000 0.000 0.000 0.172 0.116
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0363 0.8253 0.000 0.000 0.988 0.012 0.000 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0146 0.8280 0.000 0.000 0.996 0.004 0.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0622 0.9979 0.012 0.000 0.000 0.980 0.008 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.2213 0.8029 0.004 0.000 0.008 0.000 0.888 0.100
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.8292 0.000 0.000 1.000 0.000 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0000 0.8292 0.000 0.000 1.000 0.000 0.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.3851 0.1499 0.460 0.000 0.540 0.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0748 0.9244 0.976 0.000 0.000 0.004 0.016 0.004
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0622 0.9979 0.012 0.000 0.000 0.980 0.008 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.8292 0.000 0.000 1.000 0.000 0.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0622 0.9979 0.012 0.000 0.000 0.980 0.008 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0622 0.8924 0.000 0.980 0.000 0.012 0.000 0.008
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.4293 -0.1350 0.000 0.536 0.004 0.012 0.000 0.448
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0363 0.9236 0.988 0.000 0.000 0.000 0.012 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.4809 0.6190 0.004 0.000 0.680 0.000 0.188 0.128
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.1908 0.8072 0.004 0.000 0.000 0.000 0.900 0.096
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.3624 0.7798 0.016 0.000 0.112 0.060 0.812 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0146 0.9251 0.996 0.000 0.000 0.004 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 5 0.3761 0.7971 0.028 0.000 0.044 0.112 0.812 0.004
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.4818 0.3267 0.372 0.572 0.000 0.000 0.052 0.004
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.3243 0.8234 0.016 0.000 0.064 0.036 0.860 0.024
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.1610 0.8638 0.916 0.000 0.000 0.000 0.084 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0146 0.9251 0.996 0.000 0.000 0.004 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.4051 -0.0564 0.000 0.560 0.000 0.008 0.000 0.432
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0748 0.9244 0.976 0.000 0.000 0.004 0.016 0.004
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0622 0.9979 0.012 0.000 0.000 0.980 0.008 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 6 0.2402 0.8233 0.000 0.120 0.000 0.000 0.012 0.868
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.4678 0.6134 0.024 0.728 0.000 0.000 0.128 0.120
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.2946 0.6934 0.000 0.000 0.812 0.012 0.000 0.176
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0436 0.9248 0.988 0.000 0.000 0.004 0.004 0.004
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.2721 0.7944 0.000 0.868 0.000 0.040 0.004 0.088
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0146 0.9251 0.996 0.000 0.000 0.004 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.2122 0.8391 0.024 0.000 0.000 0.000 0.900 0.076
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0622 0.9979 0.012 0.000 0.000 0.980 0.008 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.8292 0.000 0.000 1.000 0.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.2146 0.7918 0.004 0.000 0.000 0.000 0.880 0.116
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.3541 0.5748 0.000 0.000 0.728 0.012 0.000 0.260
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 6 0.2178 0.8201 0.000 0.132 0.000 0.000 0.000 0.868
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.4720 0.6289 0.004 0.000 0.692 0.000 0.176 0.128
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0260 0.8268 0.000 0.000 0.992 0.008 0.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.8292 0.000 0.000 1.000 0.000 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0405 0.8994 0.000 0.988 0.000 0.008 0.000 0.004
#> A608BCEB-2C27-4927-A308-E6975F641722 6 0.2969 0.7553 0.000 0.224 0.000 0.000 0.000 0.776
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.9070 0.000 1.000 0.000 0.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.3820 0.8247 0.040 0.000 0.004 0.052 0.816 0.088
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 6 0.2402 0.8233 0.000 0.120 0.000 0.000 0.012 0.868
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "pam"]
# you can also extract it by
# res = res_list["SD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.759 0.871 0.938 0.4944 0.498 0.498
#> 3 3 0.439 0.678 0.805 0.2502 0.821 0.661
#> 4 4 0.662 0.793 0.879 0.1406 0.883 0.705
#> 5 5 0.709 0.683 0.861 0.1124 0.797 0.438
#> 6 6 0.804 0.786 0.895 0.0544 0.886 0.555
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.0000 0.9358 1.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.0376 0.9273 0.004 0.996
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.9268 0.000 1.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.2778 0.9229 0.048 0.952
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 2 0.3274 0.9212 0.060 0.940
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 2 0.4562 0.9036 0.096 0.904
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 2 0.4431 0.9060 0.092 0.908
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0000 0.9268 0.000 1.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.0000 0.9358 1.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.9358 1.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.0000 0.9358 1.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.1633 0.9275 0.024 0.976
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.4431 0.9060 0.092 0.908
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.9358 1.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.0000 0.9358 1.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.2948 0.9236 0.052 0.948
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.9268 0.000 1.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0000 0.9358 1.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.9268 0.000 1.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.8327 0.6281 0.736 0.264
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.9268 0.000 1.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.9732 0.3700 0.404 0.596
#> F325847E-F046-4B67-B01C-16919C401020 1 0.9393 0.4427 0.644 0.356
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.0000 0.9358 1.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.9268 0.000 1.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.7674 0.6927 0.776 0.224
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 2 0.5178 0.8880 0.116 0.884
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0000 0.9358 1.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.0000 0.9358 1.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.9358 1.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.1633 0.9275 0.024 0.976
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.9970 0.0699 0.532 0.468
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.9268 0.000 1.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0672 0.9303 0.992 0.008
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 2 0.9393 0.5136 0.356 0.644
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.9358 1.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.0000 0.9358 1.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.0000 0.9358 1.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.4431 0.9060 0.092 0.908
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.9268 0.000 1.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.0000 0.9358 1.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.0000 0.9358 1.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.4022 0.9123 0.080 0.920
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.0000 0.9358 1.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 2 0.4690 0.9009 0.100 0.900
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.0000 0.9358 1.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.1633 0.9275 0.024 0.976
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.0000 0.9358 1.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.1414 0.9209 0.980 0.020
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.2236 0.9261 0.036 0.964
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 2 0.4690 0.9009 0.100 0.900
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0000 0.9358 1.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 2 0.1843 0.9270 0.028 0.972
#> AD294665-6F90-459C-90D5-3058F210225D 2 0.9170 0.5585 0.332 0.668
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.3733 0.9143 0.072 0.928
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.9358 1.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0000 0.9358 1.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.9268 0.000 1.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.0000 0.9358 1.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0000 0.9358 1.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.9358 1.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.9286 0.4331 0.656 0.344
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.9268 0.000 1.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0000 0.9358 1.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.8909 0.5170 0.692 0.308
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.9268 0.000 1.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 2 0.4562 0.9036 0.096 0.904
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0000 0.9358 1.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.0672 0.9307 0.992 0.008
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.3274 0.9189 0.060 0.940
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.9268 0.000 1.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.9358 1.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.9358 1.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.5059 0.8334 0.888 0.112
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.0000 0.9358 1.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.0000 0.9358 1.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0000 0.9358 1.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.9268 0.000 1.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.0000 0.9358 1.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.8555 0.5994 0.720 0.280
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.9358 1.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.9358 1.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.4161 0.8905 0.084 0.916
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.0000 0.9358 1.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.9393 0.4833 0.644 0.356
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.9268 0.000 1.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.2236 0.9264 0.036 0.964
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0000 0.9268 0.000 1.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.9358 1.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.9268 0.000 1.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.9268 0.000 1.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0000 0.9358 1.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.8207 0.6247 0.744 0.256
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.5408 0.8239 0.876 0.124
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.9358 1.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.0000 0.9358 1.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.2423 0.9080 0.960 0.040
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.0672 0.9307 0.992 0.008
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.2423 0.9066 0.960 0.040
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0376 0.9332 0.996 0.004
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.9268 0.000 1.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.9358 1.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.0000 0.9358 1.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.0376 0.9273 0.004 0.996
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.9268 0.000 1.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.9248 0.5263 0.340 0.660
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.7376 0.7784 0.208 0.792
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.9358 1.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.4562 0.9038 0.096 0.904
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.9358 1.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.3114 0.9215 0.056 0.944
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 2 0.1184 0.9279 0.016 0.984
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.0000 0.9358 1.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.0000 0.9358 1.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.3114 0.9203 0.056 0.944
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 2 0.4562 0.9036 0.096 0.904
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.4431 0.9060 0.092 0.908
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0938 0.9278 0.012 0.988
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 2 0.9608 0.4492 0.384 0.616
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.8608 0.5917 0.716 0.284
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.0376 0.9333 0.996 0.004
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.5519 0.8691 0.128 0.872
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.9268 0.000 1.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.9268 0.000 1.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.8207 0.6552 0.744 0.256
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.1633 0.9275 0.024 0.976
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.1163 0.8133 0.972 0.000 0.028
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 3 0.6282 0.6772 0.012 0.324 0.664
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0592 0.8317 0.000 0.988 0.012
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.2356 0.7942 0.072 0.928 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.8842 0.4549 0.116 0.432 0.452
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.9046 0.6323 0.312 0.160 0.528
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.8433 0.7168 0.176 0.204 0.620
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 3 0.5810 0.6579 0.000 0.336 0.664
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.6267 -0.0976 0.548 0.000 0.452
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.8222 1.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.0000 0.8222 1.000 0.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.7306 0.1054 0.044 0.616 0.340
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.8703 0.6989 0.180 0.228 0.592
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.8222 1.000 0.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.0000 0.8222 1.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.8523 0.4113 0.092 0.444 0.464
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.8374 0.000 1.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0000 0.8222 1.000 0.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.8374 0.000 1.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.6062 0.6006 0.616 0.000 0.384
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.8374 0.000 1.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.7804 0.7198 0.216 0.120 0.664
#> F325847E-F046-4B67-B01C-16919C401020 3 0.8236 0.4786 0.416 0.076 0.508
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.0000 0.8222 1.000 0.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.8374 0.000 1.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.6008 0.6119 0.628 0.000 0.372
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.7909 0.7308 0.188 0.148 0.664
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.1163 0.8133 0.972 0.000 0.028
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.1163 0.8133 0.972 0.000 0.028
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.8222 1.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 3 0.6998 0.7126 0.044 0.292 0.664
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.7265 0.7016 0.240 0.076 0.684
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.8374 0.000 1.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0747 0.8188 0.984 0.016 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 3 0.8768 0.5202 0.408 0.112 0.480
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.4235 0.7402 0.824 0.000 0.176
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.9721 0.3569 0.432 0.232 0.336
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.1163 0.8133 0.972 0.000 0.028
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.8512 0.3356 0.176 0.612 0.212
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.8374 0.000 1.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.1163 0.8133 0.972 0.000 0.028
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.5810 0.6414 0.664 0.000 0.336
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.7917 0.7368 0.152 0.184 0.664
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.5968 0.6343 0.636 0.000 0.364
#> CB207A52-09AC-49D3-8240-5840CDFBB154 3 0.5304 0.6801 0.068 0.108 0.824
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.3933 0.7593 0.880 0.092 0.028
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 3 0.6998 0.7126 0.044 0.292 0.664
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.1289 0.8137 0.968 0.000 0.032
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.1529 0.8102 0.960 0.040 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.7351 0.7245 0.068 0.268 0.664
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 3 0.9588 0.6025 0.240 0.284 0.476
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.5698 0.4997 0.736 0.012 0.252
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 3 0.6998 0.7126 0.044 0.292 0.664
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.8436 0.6172 0.324 0.108 0.568
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.3686 0.7190 0.140 0.860 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.4235 0.7402 0.824 0.000 0.176
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.5810 0.6414 0.664 0.000 0.336
#> A54731AE-FC40-407F-8D10-67DDC122237D 3 0.6062 0.5968 0.000 0.384 0.616
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.0000 0.8222 1.000 0.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.4750 0.7311 0.784 0.000 0.216
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.8222 1.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.7209 0.3462 0.604 0.360 0.036
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.8374 0.000 1.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0892 0.8167 0.980 0.000 0.020
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.5236 0.6757 0.804 0.168 0.028
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.8374 0.000 1.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 3 0.7927 0.7323 0.176 0.160 0.664
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0000 0.8222 1.000 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.5810 0.6414 0.664 0.000 0.336
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.3116 0.7600 0.108 0.892 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.8374 0.000 1.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.4235 0.7402 0.824 0.000 0.176
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.8222 1.000 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.6232 0.5495 0.740 0.040 0.220
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.1964 0.8117 0.944 0.000 0.056
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.5810 0.6414 0.664 0.000 0.336
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.2261 0.7860 0.932 0.000 0.068
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.8374 0.000 1.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.0892 0.8166 0.980 0.000 0.020
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.7513 0.6176 0.344 0.052 0.604
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.8222 1.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.8222 1.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 3 0.1964 0.4796 0.000 0.056 0.944
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.1753 0.8048 0.952 0.000 0.048
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.6299 0.4744 0.524 0.000 0.476
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.8374 0.000 1.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.4357 0.7565 0.052 0.868 0.080
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.6386 -0.1084 0.004 0.584 0.412
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.8222 1.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.8374 0.000 1.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.8374 0.000 1.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0000 0.8222 1.000 0.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.7898 0.2102 0.616 0.084 0.300
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.4636 0.7608 0.848 0.036 0.116
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.8222 1.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.2261 0.8019 0.932 0.000 0.068
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.5968 0.3901 0.636 0.364 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.3267 0.7611 0.884 0.000 0.116
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.2625 0.7881 0.916 0.084 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0592 0.8200 0.988 0.012 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.1411 0.8151 0.000 0.964 0.036
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.8222 1.000 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.5810 0.6414 0.664 0.000 0.336
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 3 0.6155 0.6710 0.008 0.328 0.664
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.8374 0.000 1.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.4178 0.6840 0.172 0.828 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 1 0.9334 -0.4173 0.428 0.164 0.408
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.2711 0.7927 0.912 0.000 0.088
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.5798 0.6498 0.176 0.780 0.044
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.8222 1.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.2959 0.7700 0.100 0.900 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.6772 0.7025 0.032 0.304 0.664
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.5810 0.6414 0.664 0.000 0.336
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.0424 0.8204 0.992 0.000 0.008
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.4094 0.7516 0.100 0.872 0.028
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 2 0.8433 0.3533 0.176 0.620 0.204
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.7927 0.7323 0.176 0.160 0.664
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 3 0.6597 0.6927 0.024 0.312 0.664
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.7236 0.2720 0.576 0.392 0.032
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.6446 0.5370 0.736 0.052 0.212
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.3267 0.7480 0.884 0.000 0.116
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.8996 -0.1509 0.140 0.504 0.356
#> A608BCEB-2C27-4927-A308-E6975F641722 3 0.6062 0.5988 0.000 0.384 0.616
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0892 0.8233 0.000 0.980 0.020
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.8714 0.4966 0.408 0.108 0.484
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.6998 0.7126 0.044 0.292 0.664
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.0592 0.87119 0.984 0.000 0.016 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 3 0.1940 0.79426 0.000 0.076 0.924 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.1211 0.91343 0.000 0.960 0.040 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0000 0.93633 0.000 1.000 0.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.6362 0.60955 0.096 0.288 0.616 0.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.5018 0.48663 0.332 0.012 0.656 0.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.4274 0.76583 0.108 0.072 0.820 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 3 0.1940 0.79426 0.000 0.076 0.924 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.4193 0.61700 0.268 0.000 0.732 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.87173 1.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.1940 0.86190 0.924 0.000 0.076 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.6491 0.00928 0.072 0.496 0.432 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.4411 0.76172 0.108 0.080 0.812 0.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.87173 1.000 0.000 0.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.1940 0.86190 0.924 0.000 0.076 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.6649 0.45045 0.100 0.340 0.560 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.93633 0.000 1.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0000 0.87173 1.000 0.000 0.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.93633 0.000 1.000 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.93633 0.000 1.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.2469 0.77077 0.108 0.000 0.892 0.000
#> F325847E-F046-4B67-B01C-16919C401020 3 0.3907 0.66997 0.232 0.000 0.768 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.1940 0.86190 0.924 0.000 0.076 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.93633 0.000 1.000 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.0707 0.78190 0.020 0.000 0.980 0.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0592 0.87119 0.984 0.000 0.016 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.0707 0.87140 0.980 0.000 0.020 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.87173 1.000 0.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 3 0.1940 0.79426 0.000 0.076 0.924 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.2530 0.76967 0.112 0.000 0.888 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.93633 0.000 1.000 0.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0188 0.87119 0.996 0.004 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 3 0.3528 0.72529 0.192 0.000 0.808 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.2469 0.79930 0.892 0.000 0.000 0.108
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.2216 0.85643 0.908 0.000 0.092 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.5496 0.64517 0.108 0.732 0.160 0.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.93633 0.000 1.000 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.2216 0.85643 0.908 0.000 0.092 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.3117 0.78957 0.092 0.028 0.880 0.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 3 0.5778 0.44338 0.040 0.000 0.604 0.356
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.4773 0.77485 0.788 0.120 0.092 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 3 0.1940 0.79426 0.000 0.076 0.924 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.2216 0.85643 0.908 0.000 0.092 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.1118 0.85843 0.964 0.036 0.000 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.1940 0.79426 0.000 0.076 0.924 0.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 3 0.5948 0.75075 0.160 0.144 0.696 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.4950 0.23794 0.620 0.004 0.376 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 3 0.1940 0.79426 0.000 0.076 0.924 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.5696 0.69505 0.232 0.000 0.692 0.076
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.1302 0.90085 0.044 0.956 0.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.2469 0.79930 0.892 0.000 0.000 0.108
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 3 0.2408 0.78157 0.000 0.104 0.896 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.1940 0.86190 0.924 0.000 0.076 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.5423 0.69803 0.740 0.000 0.144 0.116
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0188 0.87119 0.996 0.004 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.6811 0.25425 0.496 0.404 0.100 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.93633 0.000 1.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.1118 0.86169 0.964 0.000 0.036 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.5266 0.74045 0.752 0.140 0.108 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.93633 0.000 1.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 3 0.4514 0.79204 0.112 0.072 0.812 0.004
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0817 0.87183 0.976 0.000 0.024 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.93633 0.000 1.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.93633 0.000 1.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.2469 0.79930 0.892 0.000 0.000 0.108
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.87173 1.000 0.000 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.4624 0.53872 0.660 0.000 0.340 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.2676 0.85427 0.896 0.000 0.092 0.012
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.3569 0.76970 0.804 0.000 0.196 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.93633 0.000 1.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.2216 0.85643 0.908 0.000 0.092 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.3311 0.73974 0.172 0.000 0.828 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.87173 1.000 0.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.87173 1.000 0.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.2408 0.85222 0.896 0.000 0.104 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.93633 0.000 1.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.3754 0.81430 0.064 0.852 0.084 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 3 0.4843 0.39072 0.000 0.396 0.604 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0188 0.87119 0.996 0.004 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.93633 0.000 1.000 0.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.93633 0.000 1.000 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.1940 0.86190 0.924 0.000 0.076 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.5512 -0.03829 0.492 0.016 0.492 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.3335 0.83655 0.856 0.000 0.128 0.016
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.87173 1.000 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.2021 0.84081 0.932 0.000 0.012 0.056
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.5016 0.30547 0.600 0.396 0.004 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.4284 0.73358 0.764 0.000 0.224 0.012
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.1833 0.84755 0.944 0.032 0.024 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0188 0.87119 0.996 0.004 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.1557 0.90153 0.000 0.944 0.056 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.87173 1.000 0.000 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 3 0.1940 0.79426 0.000 0.076 0.924 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.93633 0.000 1.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.0592 0.92712 0.016 0.984 0.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.5928 0.02701 0.456 0.036 0.508 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.1474 0.84811 0.948 0.000 0.000 0.052
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.3160 0.81598 0.108 0.872 0.000 0.020
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.87173 1.000 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0336 0.93197 0.008 0.992 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.1940 0.79426 0.000 0.076 0.924 0.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.2011 0.86085 0.920 0.000 0.080 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0592 0.92643 0.000 0.984 0.016 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 2 0.5624 0.62541 0.108 0.720 0.172 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.0707 0.78190 0.020 0.000 0.980 0.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 3 0.1940 0.79426 0.000 0.076 0.924 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.7012 0.30330 0.504 0.372 0.124 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.3764 0.75432 0.784 0.000 0.216 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.3837 0.73749 0.776 0.000 0.224 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 3 0.5682 0.25318 0.024 0.456 0.520 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 3 0.2408 0.78202 0.000 0.104 0.896 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0188 0.93411 0.000 0.996 0.004 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.4711 0.75558 0.152 0.064 0.784 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.1940 0.79426 0.000 0.076 0.924 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.2286 0.77290 0.888 0.000 0.108 0.000 0.004
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 5 0.0000 0.85306 0.000 0.000 0.000 0.000 1.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.3661 0.59651 0.000 0.724 0.000 0.000 0.276
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0000 0.85227 0.000 1.000 0.000 0.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.5176 0.28311 0.000 0.048 0.572 0.000 0.380
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.2852 0.64117 0.000 0.000 0.828 0.000 0.172
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.3424 0.57718 0.000 0.000 0.760 0.000 0.240
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 5 0.0000 0.85306 0.000 0.000 0.000 0.000 1.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.2727 0.68264 0.016 0.000 0.868 0.000 0.116
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.87417 1.000 0.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.4210 0.23338 0.588 0.000 0.412 0.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 3 0.4235 0.28353 0.000 0.000 0.576 0.000 0.424
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.6278 0.24232 0.000 0.252 0.536 0.000 0.212
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.87417 1.000 0.000 0.000 0.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.4273 0.17518 0.448 0.000 0.552 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.4235 0.28353 0.000 0.000 0.576 0.000 0.424
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.85227 0.000 1.000 0.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0000 0.87417 1.000 0.000 0.000 0.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.85227 0.000 1.000 0.000 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 1.00000 0.000 0.000 0.000 1.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.85227 0.000 1.000 0.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.4302 0.03619 0.000 0.000 0.520 0.000 0.480
#> F325847E-F046-4B67-B01C-16919C401020 3 0.0290 0.71418 0.008 0.000 0.992 0.000 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.2773 0.67866 0.164 0.000 0.836 0.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.85227 0.000 1.000 0.000 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 1.00000 0.000 0.000 0.000 1.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.3752 0.47587 0.000 0.000 0.708 0.000 0.292
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.4138 0.33599 0.616 0.000 0.384 0.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.2516 0.75047 0.860 0.000 0.140 0.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.87417 1.000 0.000 0.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 5 0.0000 0.85306 0.000 0.000 0.000 0.000 1.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.0510 0.71267 0.000 0.000 0.984 0.000 0.016
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.85227 0.000 1.000 0.000 0.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.87417 1.000 0.000 0.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.4309 0.67511 0.148 0.000 0.084 0.000 0.768
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.87417 1.000 0.000 0.000 0.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 1.00000 0.000 0.000 0.000 1.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.4278 0.08749 0.452 0.000 0.548 0.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.4101 0.43977 0.000 0.004 0.664 0.000 0.332
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0880 0.83518 0.000 0.968 0.032 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.1121 0.71817 0.044 0.000 0.956 0.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 1.00000 0.000 0.000 0.000 1.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.2424 0.66709 0.000 0.000 0.868 0.000 0.132
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 1.00000 0.000 0.000 0.000 1.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.6801 0.15246 0.000 0.000 0.348 0.292 0.360
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.3480 0.55696 0.248 0.000 0.752 0.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 5 0.0000 0.85306 0.000 0.000 0.000 0.000 1.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.3895 0.42794 0.320 0.000 0.680 0.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.4656 -0.11504 0.508 0.012 0.480 0.000 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.4138 0.34129 0.000 0.000 0.616 0.000 0.384
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.3826 0.55395 0.008 0.004 0.236 0.000 0.752
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.3336 0.62554 0.772 0.000 0.000 0.000 0.228
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 5 0.0000 0.85306 0.000 0.000 0.000 0.000 1.000
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.6680 0.56301 0.088 0.000 0.128 0.168 0.616
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.4976 -0.00498 0.000 0.504 0.468 0.000 0.028
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.87417 1.000 0.000 0.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0000 1.00000 0.000 0.000 0.000 1.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 5 0.3966 0.35134 0.000 0.336 0.000 0.000 0.664
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.3395 0.61511 0.236 0.000 0.764 0.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.6630 0.33786 0.560 0.000 0.028 0.164 0.248
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.87417 1.000 0.000 0.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.1809 0.70719 0.012 0.000 0.928 0.000 0.060
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.85227 0.000 1.000 0.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0609 0.86163 0.980 0.000 0.000 0.000 0.020
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.1251 0.71725 0.036 0.000 0.956 0.000 0.008
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.85227 0.000 1.000 0.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.1502 0.81294 0.004 0.000 0.056 0.000 0.940
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.2852 0.70550 0.828 0.000 0.172 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 1.00000 0.000 0.000 0.000 1.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.85227 0.000 1.000 0.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.85227 0.000 1.000 0.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.87417 1.000 0.000 0.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.87417 1.000 0.000 0.000 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.5002 0.28545 0.364 0.000 0.596 0.000 0.040
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.4302 -0.00444 0.480 0.000 0.520 0.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 1.00000 0.000 0.000 0.000 1.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 3 0.3305 0.62487 0.224 0.000 0.776 0.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.85227 0.000 1.000 0.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.1671 0.71360 0.076 0.000 0.924 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.1408 0.71298 0.008 0.000 0.948 0.000 0.044
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.87417 1.000 0.000 0.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.87417 1.000 0.000 0.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0000 1.00000 0.000 0.000 0.000 1.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0404 0.71451 0.012 0.000 0.988 0.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0000 1.00000 0.000 0.000 0.000 1.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.85227 0.000 1.000 0.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.5950 0.45026 0.000 0.592 0.220 0.000 0.188
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.4930 0.56493 0.000 0.684 0.072 0.000 0.244
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.87417 1.000 0.000 0.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.2732 0.71903 0.000 0.840 0.000 0.000 0.160
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.85227 0.000 1.000 0.000 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.2605 0.69034 0.148 0.000 0.852 0.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 3 0.4983 0.54454 0.272 0.000 0.664 0.000 0.064
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.2020 0.70543 0.100 0.000 0.900 0.000 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.87417 1.000 0.000 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.4649 0.32408 0.404 0.000 0.580 0.016 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.3949 0.55267 0.300 0.696 0.004 0.000 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 3 0.4783 0.62329 0.176 0.000 0.724 0.000 0.100
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.1608 0.81784 0.928 0.000 0.072 0.000 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.87417 1.000 0.000 0.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.5779 0.19301 0.000 0.508 0.092 0.000 0.400
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.87417 1.000 0.000 0.000 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 1.00000 0.000 0.000 0.000 1.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 5 0.0000 0.85306 0.000 0.000 0.000 0.000 1.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.85227 0.000 1.000 0.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.0290 0.84810 0.008 0.992 0.000 0.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0510 0.71282 0.000 0.000 0.984 0.000 0.016
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.87417 1.000 0.000 0.000 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.5195 -0.04488 0.004 0.488 0.480 0.004 0.024
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.87417 1.000 0.000 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0609 0.84100 0.000 0.980 0.020 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.0404 0.84672 0.000 0.000 0.012 0.000 0.988
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 1.00000 0.000 0.000 0.000 1.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.4235 0.20367 0.576 0.000 0.424 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.1792 0.79591 0.000 0.916 0.084 0.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 3 0.4455 0.29272 0.000 0.008 0.588 0.000 0.404
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.0609 0.71283 0.000 0.000 0.980 0.000 0.020
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 5 0.0000 0.85306 0.000 0.000 0.000 0.000 1.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.0404 0.71451 0.012 0.000 0.988 0.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0404 0.71248 0.000 0.000 0.988 0.000 0.012
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.1410 0.71691 0.060 0.000 0.940 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.4380 0.59632 0.000 0.708 0.032 0.000 0.260
#> A608BCEB-2C27-4927-A308-E6975F641722 5 0.0000 0.85306 0.000 0.000 0.000 0.000 1.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.85227 0.000 1.000 0.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.3878 0.62074 0.236 0.000 0.016 0.000 0.748
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 5 0.0000 0.85306 0.000 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.5296 0.5102 0.600 0.000 0.216 0.000 0.184 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 6 0.0000 0.8239 0.000 0.000 0.000 0.000 0.000 1.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.3244 0.6376 0.000 0.732 0.000 0.000 0.000 0.268
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0363 0.9093 0.000 0.988 0.000 0.000 0.000 0.012
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.3071 0.7635 0.000 0.000 0.016 0.000 0.804 0.180
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.4328 -0.0494 0.000 0.000 0.520 0.000 0.020 0.460
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 6 0.3756 0.3784 0.000 0.000 0.400 0.000 0.000 0.600
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 6 0.0000 0.8239 0.000 0.000 0.000 0.000 0.000 1.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.3806 0.7056 0.032 0.000 0.780 0.000 0.020 0.168
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.9219 1.000 0.000 0.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.3641 0.6832 0.248 0.000 0.732 0.000 0.020 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 6 0.3342 0.6676 0.000 0.012 0.228 0.000 0.000 0.760
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.1624 0.7800 0.000 0.020 0.936 0.000 0.004 0.040
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0260 0.9161 0.992 0.000 0.008 0.000 0.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.3460 0.7090 0.220 0.000 0.760 0.000 0.020 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 6 0.3342 0.6676 0.000 0.012 0.228 0.000 0.000 0.760
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.9157 0.000 1.000 0.000 0.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0000 0.9219 1.000 0.000 0.000 0.000 0.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.9157 0.000 1.000 0.000 0.000 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.9157 0.000 1.000 0.000 0.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.0632 0.7914 0.000 0.000 0.976 0.000 0.000 0.024
#> F325847E-F046-4B67-B01C-16919C401020 3 0.2631 0.7004 0.000 0.000 0.820 0.000 0.180 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.3487 0.7049 0.224 0.000 0.756 0.000 0.020 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.9157 0.000 1.000 0.000 0.000 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.1806 0.7777 0.000 0.000 0.908 0.000 0.004 0.088
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.4550 0.2800 0.544 0.000 0.420 0.000 0.036 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.3323 0.6570 0.752 0.000 0.240 0.000 0.008 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.9219 1.000 0.000 0.000 0.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 6 0.0000 0.8239 0.000 0.000 0.000 0.000 0.000 1.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.4325 0.5390 0.000 0.000 0.692 0.000 0.064 0.244
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.9157 0.000 1.000 0.000 0.000 0.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.9219 1.000 0.000 0.000 0.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.3265 0.6351 0.000 0.000 0.004 0.000 0.748 0.248
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.9219 1.000 0.000 0.000 0.000 0.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.1616 0.7959 0.048 0.000 0.932 0.000 0.020 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 6 0.4453 0.3539 0.000 0.032 0.400 0.000 0.000 0.568
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0858 0.8971 0.000 0.968 0.028 0.000 0.000 0.004
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.1434 0.7950 0.048 0.000 0.940 0.000 0.012 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.3547 0.3989 0.000 0.000 0.668 0.000 0.000 0.332
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.0622 0.8354 0.000 0.000 0.012 0.008 0.980 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.0547 0.7933 0.020 0.000 0.980 0.000 0.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 6 0.0000 0.8239 0.000 0.000 0.000 0.000 0.000 1.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0725 0.7947 0.012 0.000 0.976 0.000 0.012 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.1549 0.8311 0.044 0.000 0.020 0.000 0.936 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.2948 0.7595 0.000 0.000 0.008 0.000 0.804 0.188
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.0665 0.8356 0.004 0.000 0.008 0.000 0.980 0.008
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.3101 0.6643 0.756 0.000 0.000 0.000 0.000 0.244
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 6 0.0000 0.8239 0.000 0.000 0.000 0.000 0.000 1.000
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.5173 0.5736 0.048 0.000 0.192 0.000 0.680 0.080
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.2331 0.8358 0.000 0.888 0.032 0.000 0.000 0.080
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.9219 1.000 0.000 0.000 0.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 6 0.2730 0.6762 0.000 0.192 0.000 0.000 0.000 0.808
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.2738 0.7670 0.004 0.000 0.176 0.000 0.820 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.0146 0.8356 0.000 0.000 0.004 0.000 0.996 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.9219 1.000 0.000 0.000 0.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.3087 0.6486 0.004 0.012 0.808 0.000 0.000 0.176
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.9157 0.000 1.000 0.000 0.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.1124 0.8931 0.956 0.000 0.008 0.000 0.000 0.036
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.2762 0.7636 0.000 0.000 0.196 0.000 0.804 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.9157 0.000 1.000 0.000 0.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 6 0.3217 0.6011 0.000 0.000 0.008 0.000 0.224 0.768
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.3713 0.6019 0.744 0.000 0.224 0.000 0.032 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.9157 0.000 1.000 0.000 0.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.9157 0.000 1.000 0.000 0.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.9219 1.000 0.000 0.000 0.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.9219 1.000 0.000 0.000 0.000 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0603 0.7937 0.016 0.000 0.980 0.000 0.004 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.1082 0.7929 0.040 0.000 0.956 0.000 0.004 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.2300 0.7975 0.000 0.000 0.144 0.000 0.856 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.9157 0.000 1.000 0.000 0.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.3432 0.7140 0.216 0.000 0.764 0.000 0.020 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.3374 0.6807 0.000 0.000 0.772 0.000 0.020 0.208
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.9219 1.000 0.000 0.000 0.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.9219 1.000 0.000 0.000 0.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0547 0.7905 0.000 0.000 0.980 0.000 0.020 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.9157 0.000 1.000 0.000 0.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 3 0.6103 -0.1513 0.000 0.320 0.380 0.000 0.000 0.300
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.4194 0.5330 0.000 0.664 0.020 0.000 0.008 0.308
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.9219 1.000 0.000 0.000 0.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.3198 0.6195 0.000 0.740 0.000 0.000 0.000 0.260
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.9157 0.000 1.000 0.000 0.000 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.3403 0.7196 0.212 0.000 0.768 0.000 0.020 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.0508 0.8383 0.004 0.000 0.012 0.000 0.984 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.2003 0.8147 0.000 0.000 0.116 0.000 0.884 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.9219 1.000 0.000 0.000 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 5 0.4209 0.6555 0.104 0.000 0.160 0.000 0.736 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.4176 0.6456 0.220 0.716 0.000 0.000 0.064 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.0458 0.8388 0.000 0.000 0.016 0.000 0.984 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 5 0.2883 0.7212 0.212 0.000 0.000 0.000 0.788 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.9219 1.000 0.000 0.000 0.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 6 0.4499 0.1201 0.000 0.428 0.032 0.000 0.000 0.540
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.9219 1.000 0.000 0.000 0.000 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 6 0.0000 0.8239 0.000 0.000 0.000 0.000 0.000 1.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.9157 0.000 1.000 0.000 0.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.0291 0.9122 0.004 0.992 0.004 0.000 0.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0146 0.7894 0.000 0.000 0.996 0.000 0.000 0.004
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.9219 1.000 0.000 0.000 0.000 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.4512 0.6020 0.000 0.708 0.192 0.004 0.000 0.096
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.9219 1.000 0.000 0.000 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.9157 0.000 1.000 0.000 0.000 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.2664 0.7665 0.000 0.000 0.000 0.000 0.816 0.184
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.3566 0.6959 0.236 0.000 0.744 0.000 0.020 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0790 0.8960 0.000 0.968 0.032 0.000 0.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.3122 0.7651 0.000 0.000 0.020 0.000 0.804 0.176
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.1957 0.7422 0.000 0.000 0.888 0.000 0.000 0.112
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 6 0.0000 0.8239 0.000 0.000 0.000 0.000 0.000 1.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.0146 0.7900 0.000 0.000 0.996 0.000 0.004 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.1714 0.7517 0.000 0.000 0.908 0.000 0.000 0.092
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.3315 0.7232 0.200 0.000 0.780 0.000 0.020 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.4352 0.6494 0.000 0.720 0.056 0.000 0.012 0.212
#> A608BCEB-2C27-4927-A308-E6975F641722 6 0.0000 0.8239 0.000 0.000 0.000 0.000 0.000 1.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.9157 0.000 1.000 0.000 0.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.0146 0.8356 0.000 0.000 0.004 0.000 0.996 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 6 0.0000 0.8239 0.000 0.000 0.000 0.000 0.000 1.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "mclust"]
# you can also extract it by
# res = res_list["SD:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.258 0.516 0.740 0.3395 0.511 0.511
#> 3 3 0.167 0.346 0.617 0.5653 0.720 0.536
#> 4 4 0.530 0.524 0.797 0.3155 0.706 0.405
#> 5 5 0.604 0.495 0.697 0.1102 0.876 0.600
#> 6 6 0.620 0.483 0.709 0.0424 0.850 0.440
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.9909 0.162 0.556 0.444
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.9732 0.536 0.404 0.596
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.495 0.000 1.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0938 0.497 0.012 0.988
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 2 0.9732 0.536 0.404 0.596
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 2 0.9710 0.537 0.400 0.600
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 2 0.9732 0.536 0.404 0.596
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.7056 0.513 0.192 0.808
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 2 0.9909 0.451 0.444 0.556
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.7453 0.710 0.788 0.212
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.9710 0.383 0.600 0.400
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.9710 0.537 0.400 0.600
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.9732 0.536 0.404 0.596
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.7453 0.710 0.788 0.212
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 2 0.9909 0.451 0.444 0.556
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.9661 0.537 0.392 0.608
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.495 0.000 1.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.7453 0.710 0.788 0.212
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.495 0.000 1.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.0000 0.578 1.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.495 0.000 1.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.9732 0.536 0.404 0.596
#> F325847E-F046-4B67-B01C-16919C401020 1 0.9732 0.353 0.596 0.404
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 2 0.9909 0.451 0.444 0.556
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.495 0.000 1.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.0000 0.578 1.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 2 0.9732 0.536 0.404 0.596
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.9552 0.457 0.624 0.376
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 2 0.9983 0.310 0.476 0.524
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.7453 0.710 0.788 0.212
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.9732 0.536 0.404 0.596
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 2 0.9833 0.498 0.424 0.576
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0938 0.497 0.012 0.988
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.7453 0.710 0.788 0.212
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.9661 0.399 0.608 0.392
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.7453 0.710 0.788 0.212
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.0000 0.578 1.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 2 0.9909 0.451 0.444 0.556
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.9710 0.537 0.400 0.600
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.6438 0.512 0.164 0.836
#> 604C06E9-A00E-435E-847A-3992922A5C56 2 0.9909 0.451 0.444 0.556
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.0000 0.578 1.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.9710 0.537 0.400 0.600
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.0000 0.578 1.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.7376 0.708 0.792 0.208
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.9710 0.383 0.600 0.400
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.9710 0.537 0.400 0.600
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 2 0.9909 0.451 0.444 0.556
#> 5E343116-414B-41F2-AAEE-A3225450135A 2 1.0000 0.189 0.496 0.504
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.9710 0.537 0.400 0.600
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.9608 0.434 0.616 0.384
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.7453 0.710 0.788 0.212
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 2 0.9944 0.385 0.456 0.544
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.9170 0.550 0.668 0.332
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.9710 0.537 0.400 0.600
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.7453 0.710 0.788 0.212
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0000 0.578 1.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.495 0.000 1.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.9686 0.385 0.604 0.396
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.9170 0.550 0.668 0.332
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.7602 0.703 0.780 0.220
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.9909 0.167 0.556 0.444
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.495 0.000 1.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.7453 0.710 0.788 0.212
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 2 0.9732 0.536 0.404 0.596
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.495 0.000 1.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.9323 0.518 0.652 0.348
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 2 0.9896 0.453 0.440 0.560
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.0000 0.578 1.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.495 0.000 1.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.495 0.000 1.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.7453 0.710 0.788 0.212
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.7453 0.710 0.788 0.212
#> 721CDBE6-FC85-4C30-B23E-28407340286F 2 0.9833 0.498 0.424 0.576
#> 392897E4-6009-422C-B461-649F4DDF260C 2 0.9909 0.451 0.444 0.556
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.0000 0.578 1.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.9710 0.383 0.600 0.400
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.495 0.000 1.000
#> E5557F52-015D-49DC-9E23-989FC259976F 2 0.9909 0.451 0.444 0.556
#> F3135F5E-2E90-4923-B634-E994563D17B7 2 0.9909 0.451 0.444 0.556
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.9833 0.173 0.576 0.424
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.7453 0.710 0.788 0.212
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.0000 0.578 1.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 2 0.9909 0.451 0.444 0.556
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.0000 0.578 1.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.495 0.000 1.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.5408 0.509 0.124 0.876
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.7376 0.513 0.208 0.792
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.7453 0.710 0.788 0.212
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.495 0.000 1.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.495 0.000 1.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 2 0.9993 0.286 0.484 0.516
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.8763 0.613 0.704 0.296
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.9580 0.436 0.620 0.380
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.7453 0.710 0.788 0.212
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.9286 0.526 0.656 0.344
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.9710 0.537 0.400 0.600
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.9775 0.315 0.588 0.412
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 2 0.9909 0.419 0.444 0.556
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.7453 0.710 0.788 0.212
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.495 0.000 1.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.7453 0.710 0.788 0.212
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.0000 0.578 1.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.9710 0.537 0.400 0.600
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.495 0.000 1.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.9710 0.537 0.400 0.600
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.9710 0.537 0.400 0.600
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.7453 0.710 0.788 0.212
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.9710 0.537 0.400 0.600
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.7453 0.710 0.788 0.212
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.2043 0.500 0.032 0.968
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 2 0.9732 0.536 0.404 0.596
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.0000 0.578 1.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 2 0.9909 0.451 0.444 0.556
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.495 0.000 1.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 2 1.0000 0.191 0.496 0.504
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.9732 0.536 0.404 0.596
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.5946 0.511 0.144 0.856
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.9710 0.383 0.600 0.400
#> B12A4446-2310-4139-897F-CA030478CBD5 2 0.9754 0.529 0.408 0.592
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 2 0.9909 0.451 0.444 0.556
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.9710 0.537 0.400 0.600
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.495 0.000 1.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.495 0.000 1.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.9775 0.315 0.588 0.412
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.9732 0.536 0.404 0.596
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.5706 0.4392 0.680 0.320 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.9189 0.2432 0.336 0.500 0.164
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.9161 0.2564 0.280 0.532 0.188
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.9873 -0.1407 0.392 0.348 0.260
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.6468 0.1895 0.552 0.444 0.004
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 2 0.6286 0.0793 0.464 0.536 0.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 2 0.6950 0.1834 0.408 0.572 0.020
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.9151 0.2674 0.292 0.528 0.180
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 2 0.5905 0.2738 0.352 0.648 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0424 0.4942 0.992 0.008 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 2 0.7236 0.1348 0.392 0.576 0.032
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.9847 0.0869 0.316 0.416 0.268
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.5327 0.3442 0.272 0.728 0.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.3267 0.4464 0.884 0.000 0.116
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 2 0.5905 0.2738 0.352 0.648 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.8427 0.2828 0.240 0.612 0.148
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.9125 0.2613 0.268 0.540 0.192
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.3267 0.4464 0.884 0.000 0.116
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.9161 0.2564 0.280 0.532 0.188
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 3 0.5560 0.8627 0.300 0.000 0.700
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.9016 0.2663 0.252 0.556 0.192
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.5728 0.3409 0.272 0.720 0.008
#> F325847E-F046-4B67-B01C-16919C401020 2 0.8535 0.0985 0.332 0.556 0.112
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.6291 0.1317 0.532 0.468 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.9198 0.2560 0.280 0.528 0.192
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 3 0.5560 0.8627 0.300 0.000 0.700
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 2 0.5882 0.2779 0.348 0.652 0.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.7944 0.5007 0.644 0.244 0.112
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.6154 0.2864 0.592 0.408 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.4397 0.4868 0.856 0.028 0.116
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.5905 0.2899 0.352 0.648 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 2 0.6180 0.2068 0.416 0.584 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.9399 0.2477 0.292 0.500 0.208
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.4853 1.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.5497 0.4676 0.708 0.292 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.4555 -0.0893 0.800 0.000 0.200
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 3 0.5621 0.8575 0.308 0.000 0.692
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 2 0.5926 0.2672 0.356 0.644 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.6095 0.2477 0.392 0.608 0.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.9412 0.2310 0.336 0.476 0.188
#> 604C06E9-A00E-435E-847A-3992922A5C56 2 0.5905 0.2738 0.352 0.648 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 3 0.5560 0.8627 0.300 0.000 0.700
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.5327 0.3442 0.272 0.728 0.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 3 0.6299 0.6247 0.476 0.000 0.524
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.6075 0.4465 0.676 0.316 0.008
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.8516 0.4316 0.560 0.328 0.112
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.5988 0.2727 0.368 0.632 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 2 0.5988 0.2421 0.368 0.632 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.5706 0.4266 0.680 0.320 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.6126 0.2307 0.400 0.600 0.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.7126 0.5426 0.720 0.164 0.116
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.2537 0.4734 0.920 0.000 0.080
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 2 0.8789 -0.2005 0.428 0.460 0.112
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.7497 0.4798 0.652 0.276 0.072
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.8113 0.3975 0.644 0.212 0.144
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.4504 -0.0743 0.804 0.000 0.196
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 3 0.6302 0.3091 0.480 0.000 0.520
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.6398 0.3043 0.060 0.748 0.192
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.5785 0.4253 0.668 0.332 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.5560 0.4605 0.700 0.300 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.2066 0.5481 0.940 0.060 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.8779 0.2923 0.472 0.416 0.112
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.9263 0.2374 0.220 0.528 0.252
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.2096 0.5385 0.944 0.052 0.004
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.6274 0.1671 0.544 0.456 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.9198 0.2560 0.280 0.528 0.192
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.8262 0.4785 0.608 0.276 0.116
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 2 0.6140 0.1619 0.404 0.596 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 3 0.5560 0.8627 0.300 0.000 0.700
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.9236 0.2377 0.220 0.532 0.248
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.9198 0.2560 0.280 0.528 0.192
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.3816 0.1120 0.852 0.000 0.148
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.3267 0.4464 0.884 0.000 0.116
#> 721CDBE6-FC85-4C30-B23E-28407340286F 2 0.5760 0.3026 0.328 0.672 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 2 0.5733 0.3066 0.324 0.676 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 3 0.5560 0.8627 0.300 0.000 0.700
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.8721 0.3535 0.504 0.384 0.112
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.9263 0.2374 0.220 0.528 0.252
#> E5557F52-015D-49DC-9E23-989FC259976F 2 0.5926 0.2672 0.356 0.644 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 2 0.5905 0.2738 0.352 0.648 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.6008 0.2357 0.628 0.372 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.3267 0.4464 0.884 0.000 0.116
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 3 0.6819 0.5908 0.476 0.012 0.512
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 2 0.5905 0.2738 0.352 0.648 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 3 0.6260 0.6841 0.448 0.000 0.552
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.9198 0.2560 0.280 0.528 0.192
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.8917 0.2706 0.244 0.568 0.188
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.5848 0.3437 0.268 0.720 0.012
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.3267 0.4464 0.884 0.000 0.116
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.8957 0.2684 0.244 0.564 0.192
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.9198 0.2560 0.280 0.528 0.192
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.8768 0.3074 0.480 0.408 0.112
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.6325 0.5154 0.772 0.112 0.116
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.6154 0.3074 0.592 0.408 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0237 0.4797 0.996 0.000 0.004
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.5529 0.4644 0.704 0.296 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.5220 0.4280 0.780 0.208 0.012
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.6168 0.3282 0.588 0.412 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.4968 0.4487 0.800 0.188 0.012
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.4853 1.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.6258 0.3052 0.052 0.752 0.196
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.4357 0.5548 0.868 0.080 0.052
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 3 0.5560 0.8627 0.300 0.000 0.700
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.5327 0.3442 0.272 0.728 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.9198 0.2560 0.280 0.528 0.192
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.7975 0.4170 0.656 0.204 0.140
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.5327 0.3442 0.272 0.728 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.3141 0.3893 0.912 0.020 0.068
#> A60DC925-7343-496E-900D-0DD81D5C8123 1 0.7252 0.4362 0.704 0.196 0.100
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.1163 0.5189 0.972 0.028 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 1 0.9515 -0.1859 0.424 0.388 0.188
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.6527 0.2780 0.588 0.404 0.008
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 3 0.5560 0.8627 0.300 0.000 0.700
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 2 0.5926 0.2672 0.356 0.644 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.8212 0.2831 0.168 0.640 0.192
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.8902 0.3115 0.480 0.396 0.124
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.5621 0.3197 0.308 0.692 0.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.9120 0.2492 0.200 0.544 0.256
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 2 0.8762 -0.1231 0.404 0.484 0.112
#> B12A4446-2310-4139-897F-CA030478CBD5 2 0.5810 0.2941 0.336 0.664 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 2 0.5905 0.2738 0.352 0.648 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.7931 0.3168 0.284 0.624 0.092
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.5803 0.2937 0.016 0.736 0.248
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.9198 0.2560 0.280 0.528 0.192
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.5678 0.4434 0.684 0.316 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.5760 0.3158 0.328 0.672 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.6686 0.4080 0.200 0.000 0.620 0.180
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.5203 0.1977 0.000 0.576 0.416 0.008
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.8818 0.000 1.000 0.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.3123 0.7615 0.156 0.844 0.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.7172 0.1045 0.376 0.000 0.484 0.140
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.6856 0.3284 0.284 0.000 0.576 0.140
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.5856 0.0601 0.464 0.000 0.504 0.032
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 3 0.4967 0.1016 0.000 0.452 0.548 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.4855 0.2168 0.600 0.000 0.000 0.400
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.0469 0.7848 0.000 0.000 0.988 0.012
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.7205 0.3286 0.548 0.252 0.200 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.4538 1.000 0.000 0.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.5638 0.2948 0.028 0.388 0.584 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0188 0.8805 0.000 0.996 0.004 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.2011 0.4312 0.920 0.000 0.000 0.080
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.8818 0.000 1.000 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 0.7342 0.000 0.000 0.000 1.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0469 0.8770 0.000 0.988 0.012 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> F325847E-F046-4B67-B01C-16919C401020 1 0.4941 0.1234 0.564 0.000 0.436 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.6142 0.4950 0.184 0.000 0.676 0.140
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.8818 0.000 1.000 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 0.7342 0.000 0.000 0.000 1.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.3764 0.4785 0.784 0.000 0.216 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.7220 0.0375 0.440 0.000 0.420 0.140
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.4538 1.000 0.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 3 0.3196 0.7128 0.000 0.008 0.856 0.136
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.2868 0.7145 0.000 0.000 0.864 0.136
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.8818 0.000 1.000 0.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.4855 0.2168 0.600 0.000 0.000 0.400
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 4 0.7824 -0.1044 0.264 0.000 0.336 0.400
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.4888 0.1961 0.588 0.000 0.000 0.412
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 0.7342 0.000 0.000 0.000 1.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.2921 0.7116 0.000 0.000 0.860 0.140
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.8818 0.000 1.000 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 0.7342 0.000 0.000 0.000 1.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 0.7342 0.000 0.000 0.000 1.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 4 0.7841 -0.1322 0.324 0.000 0.276 0.400
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.4855 0.2082 0.600 0.000 0.400 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 3 0.3703 0.7017 0.012 0.008 0.840 0.140
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 4 0.7666 -0.1699 0.388 0.000 0.212 0.400
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.5209 0.6112 0.104 0.000 0.756 0.140
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.3528 0.4828 0.808 0.000 0.192 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.4134 0.3090 0.740 0.000 0.000 0.260
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.4855 0.2082 0.600 0.000 0.400 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.4356 0.3946 0.708 0.000 0.292 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.4830 0.2004 0.608 0.392 0.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.4916 0.1717 0.576 0.000 0.000 0.424
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.4866 0.1537 0.596 0.000 0.000 0.404
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.3610 0.7114 0.000 0.800 0.200 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.7569 0.1610 0.436 0.000 0.368 0.196
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 4 0.7729 -0.0990 0.228 0.000 0.372 0.400
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.4855 0.2168 0.600 0.000 0.000 0.400
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.4855 0.2082 0.600 0.000 0.400 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.8818 0.000 1.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.4855 0.2168 0.600 0.000 0.000 0.400
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.6915 0.3029 0.296 0.000 0.564 0.140
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.8818 0.000 1.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.4193 0.4247 0.732 0.000 0.268 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.0817 0.7772 0.024 0.000 0.976 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 0.7342 0.000 0.000 0.000 1.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.2760 0.7883 0.128 0.872 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.8818 0.000 1.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.4888 0.1961 0.588 0.000 0.000 0.412
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.4538 1.000 0.000 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 0.7342 0.000 0.000 0.000 1.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.4454 0.3718 0.692 0.000 0.308 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.8818 0.000 1.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.4134 0.5321 0.260 0.000 0.740 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.2973 0.3927 0.856 0.000 0.000 0.144
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.2011 0.6368 0.000 0.000 0.080 0.920
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0000 0.7342 0.000 0.000 0.000 1.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.8818 0.000 1.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.4585 0.4453 0.000 0.668 0.332 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 3 0.0469 0.7837 0.000 0.012 0.988 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.4538 1.000 0.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.2011 0.8355 0.000 0.920 0.080 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.8818 0.000 1.000 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.4855 0.2082 0.600 0.000 0.400 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.2589 0.4822 0.884 0.000 0.116 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.5463 0.5409 0.052 0.000 0.692 0.256
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.4855 0.2168 0.600 0.000 0.000 0.400
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.7746 -0.0964 0.232 0.000 0.392 0.376
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.8130 0.1073 0.392 0.012 0.232 0.364
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 3 0.6907 0.2948 0.172 0.000 0.588 0.240
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.6938 0.1619 0.488 0.000 0.112 0.400
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.4855 0.2168 0.600 0.000 0.000 0.400
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.3610 0.7114 0.000 0.800 0.200 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.4830 0.2229 0.608 0.000 0.000 0.392
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 0.7342 0.000 0.000 0.000 1.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.8818 0.000 1.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.4830 0.2005 0.608 0.392 0.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.4855 0.2168 0.600 0.000 0.000 0.400
#> A60DC925-7343-496E-900D-0DD81D5C8123 1 0.5636 0.3644 0.680 0.260 0.000 0.060
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.4855 0.2168 0.600 0.000 0.000 0.400
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.8818 0.000 1.000 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.7408 0.1873 0.276 0.000 0.512 0.212
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 0.7342 0.000 0.000 0.000 1.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.2149 0.8293 0.000 0.912 0.088 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.4730 0.2777 0.636 0.000 0.364 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.6386 0.5666 0.140 0.648 0.212 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.4855 0.2082 0.600 0.000 0.400 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 3 0.4599 0.5996 0.000 0.212 0.760 0.028
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.3610 0.7114 0.000 0.800 0.200 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.8818 0.000 1.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.7469 0.1445 0.200 0.000 0.488 0.312
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.0000 0.7897 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 5 0.1471 0.44602 0.020 0.000 0.024 0.004 0.952
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 3 0.5458 -0.04041 0.000 0.380 0.552 0.000 0.068
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.88225 0.000 1.000 0.000 0.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.3123 0.70984 0.184 0.812 0.000 0.004 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.6638 -0.19068 0.328 0.000 0.436 0.000 0.236
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 5 0.6495 0.00188 0.216 0.000 0.304 0.000 0.480
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.6855 0.18663 0.296 0.004 0.420 0.000 0.280
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 3 0.4298 0.08111 0.000 0.352 0.640 0.000 0.008
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.7374 0.50961 0.272 0.000 0.456 0.044 0.228
#> 5482053D-9F48-4773-B68A-302B3A612503 5 0.3579 0.45371 0.240 0.000 0.004 0.000 0.756
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.6758 0.36070 0.120 0.000 0.468 0.032 0.380
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.7145 0.24645 0.448 0.240 0.292 0.004 0.016
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.0162 0.59735 0.004 0.000 0.996 0.000 0.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.3684 0.45737 0.720 0.000 0.000 0.000 0.280
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.7374 0.50961 0.272 0.000 0.456 0.044 0.228
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.5836 0.06436 0.100 0.328 0.568 0.004 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.88225 0.000 1.000 0.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.3913 0.39799 0.676 0.000 0.000 0.000 0.324
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.88225 0.000 1.000 0.000 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.1197 0.93925 0.000 0.000 0.000 0.952 0.048
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0162 0.88077 0.000 0.996 0.004 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.0000 0.59660 0.000 0.000 1.000 0.000 0.000
#> F325847E-F046-4B67-B01C-16919C401020 1 0.4602 0.23959 0.708 0.000 0.052 0.000 0.240
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 5 0.7614 -0.43539 0.272 0.000 0.340 0.044 0.344
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.88225 0.000 1.000 0.000 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.1197 0.93925 0.000 0.000 0.000 0.952 0.048
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.3934 0.58743 0.160 0.000 0.796 0.008 0.036
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.4287 0.46982 0.540 0.000 0.000 0.000 0.460
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 5 0.5654 -0.27203 0.380 0.000 0.084 0.000 0.536
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.3684 0.45737 0.720 0.000 0.000 0.000 0.280
#> A8E48877-F8AB-44DD-A18B-194D87C44931 3 0.2535 0.55523 0.000 0.032 0.892 0.000 0.076
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.7482 0.46025 0.252 0.000 0.404 0.040 0.304
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.88225 0.000 1.000 0.000 0.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 5 0.3424 0.45354 0.240 0.000 0.000 0.000 0.760
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.1195 0.45744 0.012 0.000 0.028 0.000 0.960
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 5 0.6703 0.25864 0.244 0.000 0.000 0.360 0.396
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.1197 0.93925 0.000 0.000 0.000 0.952 0.048
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.7070 0.52733 0.208 0.000 0.524 0.044 0.224
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.1956 0.56363 0.000 0.008 0.916 0.000 0.076
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.88225 0.000 1.000 0.000 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.7374 0.50961 0.272 0.000 0.456 0.044 0.228
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.1197 0.93925 0.000 0.000 0.000 0.952 0.048
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.0000 0.59660 0.000 0.000 1.000 0.000 0.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.3074 0.78465 0.000 0.000 0.000 0.804 0.196
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.5382 -0.06705 0.276 0.000 0.052 0.020 0.652
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.5039 0.44420 0.512 0.000 0.032 0.000 0.456
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 3 0.3180 0.53816 0.000 0.068 0.856 0.000 0.076
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.6920 0.53274 0.184 0.000 0.548 0.044 0.224
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.1410 0.44983 0.060 0.000 0.000 0.000 0.940
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.4147 0.54207 0.008 0.060 0.792 0.000 0.140
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.4604 0.47995 0.560 0.000 0.012 0.000 0.428
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.4161 0.27607 0.608 0.000 0.000 0.000 0.392
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.6327 0.33062 0.484 0.000 0.348 0.000 0.168
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.5044 0.46140 0.504 0.000 0.032 0.000 0.464
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.5953 0.33222 0.504 0.384 0.000 0.000 0.112
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 5 0.6703 0.25864 0.244 0.000 0.000 0.360 0.396
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.6480 0.22123 0.416 0.000 0.000 0.400 0.184
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.3480 0.68423 0.000 0.752 0.248 0.000 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.4557 -0.29134 0.404 0.000 0.012 0.000 0.584
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.1444 0.45524 0.012 0.000 0.040 0.000 0.948
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 5 0.3424 0.45354 0.240 0.000 0.000 0.000 0.760
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.5165 0.44405 0.512 0.000 0.040 0.000 0.448
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0162 0.88084 0.000 0.996 0.000 0.004 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.5109 0.04777 0.504 0.000 0.000 0.036 0.460
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.6281 0.07699 0.152 0.000 0.460 0.000 0.388
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.88225 0.000 1.000 0.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.5042 0.46382 0.508 0.000 0.032 0.000 0.460
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.7374 0.50961 0.272 0.000 0.456 0.044 0.228
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.1197 0.93925 0.000 0.000 0.000 0.952 0.048
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.1704 0.83109 0.068 0.928 0.000 0.004 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.88225 0.000 1.000 0.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 5 0.6697 0.25950 0.244 0.000 0.000 0.352 0.404
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.3684 0.45737 0.720 0.000 0.000 0.000 0.280
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.4116 0.59822 0.100 0.000 0.816 0.044 0.040
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.4748 0.58675 0.056 0.000 0.768 0.040 0.136
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.1270 0.93576 0.000 0.000 0.000 0.948 0.052
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.3857 0.41997 0.688 0.000 0.000 0.000 0.312
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0162 0.88084 0.000 0.996 0.000 0.004 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.7374 0.50961 0.272 0.000 0.456 0.044 0.228
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.7374 0.50961 0.272 0.000 0.456 0.044 0.228
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.7420 0.20523 0.288 0.000 0.440 0.044 0.228
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.3816 0.43114 0.696 0.000 0.000 0.000 0.304
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.6935 0.47976 0.000 0.128 0.196 0.584 0.092
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.7374 0.50961 0.272 0.000 0.456 0.044 0.228
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.1197 0.93925 0.000 0.000 0.000 0.952 0.048
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.88225 0.000 1.000 0.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.4235 0.42717 0.000 0.576 0.424 0.000 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 3 0.2233 0.55353 0.004 0.104 0.892 0.000 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.3707 0.45362 0.716 0.000 0.000 0.000 0.284
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0794 0.86857 0.000 0.972 0.028 0.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.88225 0.000 1.000 0.000 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.3837 0.38380 0.692 0.000 0.000 0.000 0.308
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.4060 0.48412 0.640 0.000 0.000 0.000 0.360
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.3395 0.33529 0.000 0.000 0.236 0.000 0.764
#> C2662596-6E2F-4924-B051-CEA1AC87B197 5 0.4930 0.21633 0.388 0.000 0.000 0.032 0.580
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 5 0.1121 0.45353 0.000 0.000 0.044 0.000 0.956
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 5 0.7356 0.20102 0.036 0.232 0.304 0.000 0.428
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.3242 0.35955 0.000 0.000 0.216 0.000 0.784
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 5 0.3395 0.45463 0.236 0.000 0.000 0.000 0.764
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 5 0.3424 0.45354 0.240 0.000 0.000 0.000 0.760
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.3607 0.68717 0.000 0.752 0.244 0.004 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.6183 -0.02021 0.456 0.000 0.000 0.136 0.408
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.1197 0.93925 0.000 0.000 0.000 0.952 0.048
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 3 0.0404 0.59372 0.000 0.012 0.988 0.000 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.88225 0.000 1.000 0.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.5825 0.34439 0.536 0.360 0.000 0.000 0.104
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0000 0.59660 0.000 0.000 1.000 0.000 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 5 0.5716 0.38936 0.240 0.000 0.000 0.144 0.616
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.6603 -0.35584 0.388 0.400 0.000 0.000 0.212
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 5 0.3424 0.45354 0.240 0.000 0.000 0.000 0.760
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.88225 0.000 1.000 0.000 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.6004 0.02909 0.120 0.000 0.508 0.000 0.372
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.1197 0.93925 0.000 0.000 0.000 0.952 0.048
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.7374 0.50961 0.272 0.000 0.456 0.044 0.228
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.2280 0.81060 0.000 0.880 0.120 0.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.6438 0.37363 0.496 0.000 0.292 0.000 0.212
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.0290 0.59753 0.008 0.000 0.992 0.000 0.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.5487 0.51479 0.072 0.600 0.324 0.004 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.4329 0.37644 0.672 0.000 0.016 0.000 0.312
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.5857 0.56045 0.096 0.000 0.668 0.040 0.196
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.7374 0.50961 0.272 0.000 0.456 0.044 0.228
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 3 0.4481 0.34359 0.000 0.232 0.720 0.048 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.3266 0.72834 0.000 0.796 0.200 0.004 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.88225 0.000 1.000 0.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.1410 0.44803 0.000 0.000 0.060 0.000 0.940
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.0290 0.59511 0.000 0.000 0.992 0.000 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.4720 0.00768 0.560 0.000 0.052 0.000 0.388 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 6 0.3435 0.63059 0.060 0.136 0.000 0.000 0.000 0.804
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.3660 0.75887 0.000 0.780 0.000 0.000 0.060 0.160
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.3708 0.72291 0.000 0.752 0.000 0.020 0.220 0.008
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.6753 0.45384 0.232 0.012 0.068 0.000 0.524 0.164
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 5 0.5463 0.50137 0.212 0.000 0.164 0.000 0.612 0.012
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 5 0.6692 0.22303 0.020 0.004 0.216 0.016 0.484 0.260
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 6 0.4081 0.63907 0.024 0.160 0.048 0.000 0.000 0.768
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0713 0.68583 0.000 0.000 0.972 0.000 0.028 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0508 0.49246 0.984 0.000 0.000 0.004 0.012 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.6257 0.37691 0.028 0.000 0.460 0.000 0.348 0.164
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 5 0.5513 0.27389 0.004 0.192 0.000 0.020 0.636 0.148
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 6 0.3578 0.54487 0.000 0.000 0.340 0.000 0.000 0.660
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.3765 0.21724 0.596 0.000 0.000 0.000 0.404 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0713 0.68583 0.000 0.000 0.972 0.000 0.028 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 6 0.5796 0.48430 0.000 0.292 0.108 0.016 0.012 0.572
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.3092 0.74781 0.000 0.840 0.028 0.000 0.012 0.120
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.4986 0.31843 0.612 0.000 0.000 0.000 0.284 0.104
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.2048 0.79139 0.000 0.880 0.000 0.000 0.120 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0547 0.88312 0.020 0.000 0.000 0.980 0.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.3092 0.74781 0.000 0.840 0.028 0.000 0.012 0.120
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 6 0.3684 0.55722 0.000 0.004 0.332 0.000 0.000 0.664
#> F325847E-F046-4B67-B01C-16919C401020 3 0.5112 0.04906 0.008 0.000 0.504 0.000 0.428 0.060
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.5098 0.24604 0.092 0.000 0.556 0.000 0.352 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.1910 0.79421 0.000 0.892 0.000 0.000 0.108 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0547 0.88312 0.020 0.000 0.000 0.980 0.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.3828 -0.09210 0.000 0.000 0.560 0.000 0.000 0.440
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 5 0.3806 0.54705 0.200 0.000 0.048 0.000 0.752 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 5 0.4330 0.53785 0.236 0.000 0.068 0.000 0.696 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.4975 0.29307 0.596 0.000 0.000 0.000 0.312 0.092
#> A8E48877-F8AB-44DD-A18B-194D87C44931 6 0.3937 0.64037 0.064 0.036 0.080 0.000 0.008 0.812
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.6664 0.40994 0.080 0.000 0.500 0.000 0.256 0.164
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.2378 0.78073 0.000 0.848 0.000 0.000 0.152 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0363 0.49196 0.988 0.000 0.000 0.000 0.012 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.4151 0.05112 0.576 0.000 0.004 0.000 0.412 0.008
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.3899 0.25059 0.592 0.000 0.000 0.404 0.000 0.004
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0547 0.88312 0.020 0.000 0.000 0.980 0.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.2170 0.66126 0.000 0.000 0.888 0.000 0.100 0.012
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 6 0.5406 0.53720 0.064 0.000 0.148 0.000 0.112 0.676
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.2963 0.77339 0.000 0.828 0.000 0.016 0.152 0.004
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0713 0.68583 0.000 0.000 0.972 0.000 0.028 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0547 0.88312 0.020 0.000 0.000 0.980 0.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 6 0.3578 0.54487 0.000 0.000 0.340 0.000 0.000 0.660
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.2562 0.74488 0.172 0.000 0.000 0.828 0.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.5147 0.34289 0.348 0.000 0.000 0.040 0.580 0.032
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 5 0.3835 0.56610 0.164 0.000 0.060 0.000 0.772 0.004
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 6 0.5023 0.63615 0.064 0.068 0.072 0.000 0.044 0.752
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.4161 0.60383 0.028 0.000 0.752 0.000 0.184 0.036
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.4552 0.02062 0.572 0.000 0.040 0.000 0.388 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 6 0.6689 0.36599 0.064 0.056 0.064 0.000 0.272 0.544
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.4284 0.16705 0.392 0.000 0.016 0.000 0.588 0.004
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.5003 0.31784 0.608 0.000 0.000 0.000 0.288 0.104
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 5 0.4830 0.50564 0.160 0.000 0.000 0.000 0.668 0.172
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.3312 0.54653 0.180 0.000 0.000 0.000 0.792 0.028
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 5 0.5197 -0.22993 0.052 0.452 0.000 0.016 0.480 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.3899 0.25380 0.592 0.000 0.000 0.404 0.000 0.004
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.7181 -0.15661 0.196 0.000 0.000 0.400 0.296 0.108
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.4640 0.39155 0.000 0.592 0.028 0.000 0.012 0.368
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.4271 0.47101 0.292 0.000 0.028 0.000 0.672 0.008
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.4109 0.05122 0.576 0.000 0.000 0.000 0.412 0.012
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0363 0.49196 0.988 0.000 0.000 0.000 0.012 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 5 0.3718 0.56482 0.164 0.000 0.052 0.000 0.780 0.004
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0665 0.80457 0.000 0.980 0.000 0.004 0.008 0.008
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.4633 0.39706 0.716 0.000 0.000 0.056 0.196 0.032
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.5841 0.43242 0.268 0.000 0.108 0.000 0.580 0.044
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.80410 0.000 1.000 0.000 0.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.3088 0.55070 0.172 0.000 0.000 0.000 0.808 0.020
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.3987 0.56418 0.024 0.000 0.728 0.000 0.236 0.012
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0547 0.88312 0.020 0.000 0.000 0.980 0.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.3623 0.73248 0.000 0.764 0.000 0.020 0.208 0.008
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.2260 0.78534 0.000 0.860 0.000 0.000 0.140 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.3807 0.32237 0.628 0.000 0.000 0.368 0.000 0.004
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.3765 0.21724 0.596 0.000 0.000 0.000 0.404 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.3802 0.26654 0.000 0.000 0.676 0.000 0.012 0.312
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.6114 0.01803 0.012 0.000 0.428 0.000 0.184 0.376
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0547 0.88312 0.020 0.000 0.000 0.980 0.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.6043 0.51165 0.172 0.000 0.172 0.000 0.596 0.060
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0653 0.80345 0.000 0.980 0.004 0.004 0.000 0.012
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0713 0.68583 0.000 0.000 0.972 0.000 0.028 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0713 0.68583 0.000 0.000 0.972 0.000 0.028 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.5587 0.41265 0.240 0.000 0.548 0.000 0.212 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.5051 0.30108 0.596 0.000 0.000 0.000 0.300 0.104
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.6562 0.37398 0.064 0.148 0.032 0.592 0.000 0.164
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0713 0.68583 0.000 0.000 0.972 0.000 0.028 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0547 0.88312 0.020 0.000 0.000 0.980 0.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.1616 0.79017 0.000 0.932 0.020 0.000 0.000 0.048
#> 322AF320-1379-4F51-AFDC-5292A060CD52 6 0.3601 0.40168 0.004 0.312 0.000 0.000 0.000 0.684
#> 53A96249-66D5-4C26-893B-ADC71481D261 6 0.4178 0.64269 0.000 0.120 0.104 0.000 0.012 0.764
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.5051 0.30108 0.596 0.000 0.000 0.000 0.300 0.104
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.3845 0.67356 0.000 0.756 0.028 0.000 0.012 0.204
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0146 0.80430 0.000 0.996 0.000 0.000 0.004 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 5 0.5837 0.52649 0.152 0.000 0.164 0.000 0.624 0.060
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.4534 -0.03756 0.476 0.000 0.032 0.000 0.492 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.4792 0.02892 0.548 0.000 0.032 0.000 0.408 0.012
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.2948 0.47366 0.848 0.000 0.000 0.060 0.092 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.4165 0.05326 0.568 0.000 0.004 0.000 0.420 0.008
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 6 0.8472 0.20847 0.108 0.256 0.100 0.000 0.224 0.312
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.6106 -0.10021 0.432 0.000 0.100 0.000 0.424 0.044
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.0790 0.48364 0.968 0.000 0.000 0.000 0.032 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0508 0.49277 0.984 0.000 0.000 0.004 0.012 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.4629 0.39260 0.000 0.596 0.028 0.000 0.012 0.364
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.6036 0.38719 0.616 0.000 0.000 0.140 0.148 0.096
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0547 0.88312 0.020 0.000 0.000 0.980 0.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 6 0.3615 0.59028 0.000 0.008 0.292 0.000 0.000 0.700
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.80410 0.000 1.000 0.000 0.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 5 0.5954 -0.00559 0.152 0.336 0.000 0.016 0.496 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 6 0.3464 0.57325 0.000 0.000 0.312 0.000 0.000 0.688
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.2362 0.47914 0.860 0.000 0.000 0.136 0.004 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.4700 0.25150 0.044 0.500 0.000 0.000 0.456 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0692 0.49024 0.976 0.000 0.000 0.004 0.020 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.2558 0.77849 0.000 0.840 0.000 0.004 0.156 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.7663 0.28423 0.264 0.016 0.124 0.000 0.384 0.212
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0547 0.88312 0.020 0.000 0.000 0.980 0.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0713 0.68583 0.000 0.000 0.972 0.000 0.028 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.4158 0.60541 0.000 0.708 0.028 0.000 0.012 0.252
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.3825 0.55440 0.160 0.000 0.000 0.000 0.768 0.072
#> 2629FEE3-A203-4411-8A70-02A796C9505C 6 0.3717 0.47963 0.000 0.000 0.384 0.000 0.000 0.616
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 6 0.6374 -0.23944 0.000 0.352 0.000 0.020 0.216 0.412
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 5 0.5793 0.52283 0.136 0.000 0.176 0.000 0.628 0.060
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.5781 0.25275 0.008 0.000 0.524 0.000 0.164 0.304
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0713 0.68583 0.000 0.000 0.972 0.000 0.028 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 6 0.5068 0.63142 0.016 0.164 0.060 0.000 0.044 0.716
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.4907 0.51455 0.000 0.640 0.028 0.016 0.016 0.300
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0603 0.80401 0.000 0.980 0.000 0.000 0.004 0.016
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.4136 0.04086 0.560 0.000 0.000 0.000 0.428 0.012
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 6 0.3916 0.57860 0.020 0.000 0.300 0.000 0.000 0.680
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "NMF"]
# you can also extract it by
# res = res_list["SD:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.966 0.933 0.973 0.4587 0.547 0.547
#> 3 3 0.746 0.830 0.929 0.4416 0.722 0.522
#> 4 4 0.664 0.746 0.851 0.0993 0.873 0.660
#> 5 5 0.742 0.771 0.860 0.0866 0.885 0.620
#> 6 6 0.786 0.689 0.819 0.0435 0.951 0.774
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.0000 0.971 1.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.0000 0.974 0.000 1.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.974 0.000 1.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0000 0.974 0.000 1.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.9087 0.531 0.676 0.324
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.0000 0.971 1.000 0.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 2 0.9635 0.348 0.388 0.612
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0000 0.974 0.000 1.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.0000 0.971 1.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.971 1.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.0000 0.971 1.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.0000 0.974 0.000 1.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.3733 0.907 0.072 0.928
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.971 1.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.0000 0.971 1.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.0000 0.974 0.000 1.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.974 0.000 1.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0000 0.971 1.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.974 0.000 1.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.0000 0.971 1.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.974 0.000 1.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 1 0.9866 0.240 0.568 0.432
#> F325847E-F046-4B67-B01C-16919C401020 1 0.0000 0.971 1.000 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.0000 0.971 1.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.974 0.000 1.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.0000 0.971 1.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 1 0.0376 0.968 0.996 0.004
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0000 0.971 1.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.0000 0.971 1.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.971 1.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.2236 0.944 0.036 0.964
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.0000 0.971 1.000 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.974 0.000 1.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.971 1.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.0000 0.971 1.000 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.971 1.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.0000 0.971 1.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.0000 0.971 1.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 1 0.9209 0.505 0.664 0.336
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.974 0.000 1.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.0000 0.971 1.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.0000 0.971 1.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.0938 0.965 0.012 0.988
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.0000 0.971 1.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.0000 0.971 1.000 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.0000 0.971 1.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.0000 0.974 0.000 1.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.0000 0.971 1.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.0000 0.971 1.000 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.0376 0.971 0.004 0.996
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0000 0.971 1.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0000 0.971 1.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.1184 0.957 0.984 0.016
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.0000 0.971 1.000 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.0000 0.974 0.000 1.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.971 1.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0000 0.971 1.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.974 0.000 1.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.0000 0.971 1.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0000 0.971 1.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.971 1.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.0000 0.971 1.000 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.974 0.000 1.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0000 0.971 1.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.0000 0.971 1.000 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.974 0.000 1.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.0000 0.971 1.000 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0000 0.971 1.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.0000 0.971 1.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.974 0.000 1.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.974 0.000 1.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.971 1.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.971 1.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.0000 0.971 1.000 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.0000 0.971 1.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.0000 0.971 1.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0000 0.971 1.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.974 0.000 1.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.0000 0.971 1.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.0000 0.971 1.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.971 1.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.971 1.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.0376 0.968 0.996 0.004
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.0000 0.971 1.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.0000 0.971 1.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.974 0.000 1.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0000 0.974 0.000 1.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0000 0.974 0.000 1.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.971 1.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.974 0.000 1.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.974 0.000 1.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0000 0.971 1.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0000 0.971 1.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.0000 0.971 1.000 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.971 1.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.0000 0.971 1.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.9608 0.391 0.616 0.384
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.0000 0.971 1.000 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.0000 0.971 1.000 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.971 1.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.974 0.000 1.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.971 1.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.0000 0.971 1.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.0000 0.974 0.000 1.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.974 0.000 1.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.0000 0.974 0.000 1.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 1 0.9248 0.493 0.660 0.340
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.971 1.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.1633 0.955 0.024 0.976
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.971 1.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.974 0.000 1.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.7950 0.685 0.760 0.240
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.0000 0.971 1.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.0000 0.971 1.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.974 0.000 1.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.7376 0.733 0.792 0.208
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.9954 0.134 0.460 0.540
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0000 0.974 0.000 1.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.0000 0.971 1.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.0000 0.971 1.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.0000 0.971 1.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0000 0.974 0.000 1.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.974 0.000 1.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.974 0.000 1.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.0000 0.971 1.000 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.3274 0.920 0.060 0.940
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.1289 0.8980 0.032 0.000 0.968
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.0000 0.9232 0.000 1.000 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.9232 0.000 1.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0000 0.9232 0.000 1.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.2959 0.8435 0.000 0.100 0.900
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.0237 0.9100 0.000 0.004 0.996
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.0237 0.9100 0.000 0.004 0.996
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0000 0.9232 0.000 1.000 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.9118 0.000 0.000 1.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.9307 1.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.6305 -0.0392 0.516 0.000 0.484
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.0892 0.9098 0.000 0.980 0.020
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.4452 0.7504 0.000 0.192 0.808
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.6008 0.3924 0.628 0.000 0.372
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.9118 0.000 0.000 1.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.5254 0.6306 0.000 0.736 0.264
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.9232 0.000 1.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0000 0.9307 1.000 0.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.9232 0.000 1.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.0237 0.9307 0.996 0.000 0.004
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.9232 0.000 1.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.7157 0.5731 0.056 0.276 0.668
#> F325847E-F046-4B67-B01C-16919C401020 3 0.0000 0.9118 0.000 0.000 1.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.9118 0.000 0.000 1.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.9232 0.000 1.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.0237 0.9307 0.996 0.000 0.004
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.0000 0.9118 0.000 0.000 1.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 3 0.3116 0.8371 0.108 0.000 0.892
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.0000 0.9118 0.000 0.000 1.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.4452 0.7279 0.808 0.000 0.192
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.3619 0.8004 0.000 0.864 0.136
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.0000 0.9118 0.000 0.000 1.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0747 0.9117 0.016 0.984 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.9307 1.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 3 0.4654 0.7385 0.208 0.000 0.792
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.9307 1.000 0.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.0237 0.9307 0.996 0.000 0.004
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0000 0.9118 0.000 0.000 1.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.3752 0.7982 0.000 0.144 0.856
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.9232 0.000 1.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0000 0.9118 0.000 0.000 1.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.0237 0.9307 0.996 0.000 0.004
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.2625 0.8634 0.000 0.084 0.916
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.0237 0.9307 0.996 0.000 0.004
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.0424 0.9281 0.992 0.000 0.008
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.0000 0.9118 0.000 0.000 1.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.5760 0.4849 0.000 0.672 0.328
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0237 0.9103 0.004 0.000 0.996
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.6309 0.0590 0.496 0.000 0.504
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.6192 0.2404 0.000 0.580 0.420
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 3 0.0000 0.9118 0.000 0.000 1.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0000 0.9307 1.000 0.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 3 0.0000 0.9118 0.000 0.000 1.000
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.0000 0.9118 0.000 0.000 1.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.0000 0.9232 0.000 1.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.9307 1.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0237 0.9307 0.996 0.000 0.004
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.9232 0.000 1.000 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.0000 0.9118 0.000 0.000 1.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.2796 0.8586 0.092 0.000 0.908
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.9307 1.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.0000 0.9118 0.000 0.000 1.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.9232 0.000 1.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0000 0.9307 1.000 0.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.0000 0.9118 0.000 0.000 1.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.9232 0.000 1.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 3 0.0000 0.9118 0.000 0.000 1.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.2261 0.8759 0.068 0.000 0.932
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.0237 0.9307 0.996 0.000 0.004
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.9232 0.000 1.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.9232 0.000 1.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.9307 1.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 3 0.6305 0.0351 0.484 0.000 0.516
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0000 0.9118 0.000 0.000 1.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.5465 0.6289 0.288 0.000 0.712
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.0237 0.9307 0.996 0.000 0.004
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 3 0.0000 0.9118 0.000 0.000 1.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.9232 0.000 1.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.9118 0.000 0.000 1.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0000 0.9118 0.000 0.000 1.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.5216 0.6509 0.260 0.000 0.740
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.9307 1.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.0237 0.9307 0.996 0.000 0.004
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.9118 0.000 0.000 1.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.0237 0.9307 0.996 0.000 0.004
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.9232 0.000 1.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0000 0.9232 0.000 1.000 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0000 0.9232 0.000 1.000 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.1289 0.9068 0.968 0.000 0.032
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.9232 0.000 1.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.9232 0.000 1.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.0000 0.9118 0.000 0.000 1.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 3 0.0000 0.9118 0.000 0.000 1.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.6168 0.2326 0.588 0.000 0.412
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.9307 1.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.2959 0.8545 0.100 0.000 0.900
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.6111 0.3091 0.604 0.396 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 3 0.4750 0.7283 0.216 0.000 0.784
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.4865 0.7710 0.832 0.136 0.032
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.9307 1.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.9232 0.000 1.000 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.9307 1.000 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.0237 0.9307 0.996 0.000 0.004
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.2537 0.8599 0.000 0.920 0.080
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.9232 0.000 1.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.2959 0.8388 0.100 0.900 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.6295 0.0655 0.000 0.528 0.472
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.9307 1.000 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.6008 0.3767 0.372 0.628 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.9307 1.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.9232 0.000 1.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.6663 0.7449 0.124 0.124 0.752
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.0237 0.9307 0.996 0.000 0.004
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.9118 0.000 0.000 1.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.9232 0.000 1.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 3 0.3816 0.7927 0.000 0.148 0.852
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.3267 0.8347 0.000 0.116 0.884
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0000 0.9232 0.000 1.000 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.0000 0.9118 0.000 0.000 1.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0000 0.9118 0.000 0.000 1.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.9118 0.000 0.000 1.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.6045 0.3555 0.380 0.620 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.9232 0.000 1.000 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.9232 0.000 1.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.3482 0.8275 0.128 0.000 0.872
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.5835 0.4996 0.000 0.340 0.660
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.6811 -0.1426 0.496 0.000 0.404 0.100
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.2781 0.8591 0.016 0.904 0.008 0.072
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.9308 0.000 1.000 0.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0469 0.9262 0.012 0.988 0.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.7904 0.6624 0.200 0.108 0.596 0.096
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.3471 0.8093 0.060 0.000 0.868 0.072
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.2075 0.8166 0.044 0.004 0.936 0.016
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.2894 0.8554 0.020 0.900 0.008 0.072
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.3009 0.8077 0.052 0.000 0.892 0.056
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.1004 0.7391 0.972 0.000 0.004 0.024
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.6022 0.5427 0.656 0.000 0.084 0.260
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.1585 0.8999 0.004 0.952 0.040 0.004
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.2197 0.8100 0.024 0.048 0.928 0.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.3143 0.6872 0.876 0.000 0.100 0.024
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.1767 0.8162 0.044 0.000 0.944 0.012
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.5000 -0.0131 0.000 0.504 0.496 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.9308 0.000 1.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.1118 0.7381 0.964 0.000 0.000 0.036
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0336 0.9282 0.008 0.992 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.2408 0.9222 0.104 0.000 0.000 0.896
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.9308 0.000 1.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.4180 0.7735 0.028 0.080 0.848 0.044
#> F325847E-F046-4B67-B01C-16919C401020 3 0.1677 0.8204 0.012 0.000 0.948 0.040
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0524 0.8144 0.008 0.000 0.988 0.004
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0188 0.9297 0.004 0.996 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.2408 0.9222 0.104 0.000 0.000 0.896
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.0817 0.8166 0.024 0.000 0.976 0.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.5506 -0.1246 0.512 0.000 0.472 0.016
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.1174 0.8129 0.020 0.000 0.968 0.012
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.1398 0.7351 0.956 0.000 0.004 0.040
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.6779 0.4841 0.028 0.648 0.232 0.092
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.1151 0.8179 0.024 0.000 0.968 0.008
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0672 0.9235 0.008 0.984 0.000 0.008
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0817 0.7282 0.976 0.000 0.000 0.024
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 3 0.6081 0.6684 0.088 0.000 0.652 0.260
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.4103 0.5868 0.744 0.000 0.000 0.256
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.2408 0.9222 0.104 0.000 0.000 0.896
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0524 0.8136 0.004 0.000 0.988 0.008
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.3725 0.6905 0.000 0.180 0.812 0.008
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.9308 0.000 1.000 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0524 0.8136 0.004 0.000 0.988 0.008
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.2408 0.9222 0.104 0.000 0.000 0.896
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.0524 0.8133 0.000 0.008 0.988 0.004
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.2345 0.9183 0.100 0.000 0.000 0.900
#> CB207A52-09AC-49D3-8240-5840CDFBB154 4 0.3837 0.6282 0.224 0.000 0.000 0.776
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.4212 0.7246 0.216 0.000 0.772 0.012
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 3 0.7485 0.3062 0.028 0.396 0.484 0.092
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0804 0.8145 0.012 0.000 0.980 0.008
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.8013 0.0808 0.388 0.004 0.332 0.276
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.6395 0.2167 0.000 0.460 0.476 0.064
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 3 0.6415 0.6389 0.288 0.000 0.612 0.100
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.4072 0.5874 0.748 0.000 0.000 0.252
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 3 0.5265 0.7600 0.160 0.000 0.748 0.092
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.6386 0.6831 0.236 0.000 0.640 0.124
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.0469 0.9264 0.012 0.988 0.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.3219 0.6752 0.836 0.000 0.000 0.164
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.4164 0.7073 0.264 0.000 0.000 0.736
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0336 0.9286 0.000 0.992 0.008 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.5280 0.7633 0.156 0.000 0.748 0.096
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.6036 0.6552 0.072 0.000 0.636 0.292
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0336 0.7379 0.992 0.000 0.000 0.008
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.4248 0.7209 0.220 0.000 0.768 0.012
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.9308 0.000 1.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.1474 0.7341 0.948 0.000 0.000 0.052
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.5102 0.7721 0.136 0.000 0.764 0.100
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.9308 0.000 1.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 3 0.6542 0.6566 0.252 0.000 0.620 0.128
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.7205 0.4131 0.532 0.000 0.296 0.172
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.2408 0.9222 0.104 0.000 0.000 0.896
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0188 0.9297 0.004 0.996 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0336 0.9282 0.008 0.992 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.4008 0.5986 0.756 0.000 0.000 0.244
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.3625 0.6560 0.828 0.000 0.160 0.012
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0524 0.8138 0.004 0.000 0.988 0.008
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.4290 0.6871 0.016 0.000 0.772 0.212
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.2408 0.9222 0.104 0.000 0.000 0.896
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 3 0.4488 0.7907 0.096 0.000 0.808 0.096
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.9308 0.000 1.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.1706 0.8182 0.036 0.000 0.948 0.016
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.2224 0.8156 0.032 0.000 0.928 0.040
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.5149 0.5135 0.648 0.000 0.336 0.016
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.1211 0.7338 0.960 0.000 0.000 0.040
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.2408 0.9222 0.104 0.000 0.000 0.896
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0188 0.8132 0.000 0.000 0.996 0.004
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.2408 0.9222 0.104 0.000 0.000 0.896
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.9308 0.000 1.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0188 0.9299 0.000 0.996 0.004 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0672 0.9250 0.008 0.984 0.008 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.1305 0.7349 0.960 0.000 0.004 0.036
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0336 0.9286 0.000 0.992 0.008 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.9308 0.000 1.000 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.1938 0.8112 0.052 0.000 0.936 0.012
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 3 0.6195 0.6809 0.252 0.000 0.648 0.100
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 4 0.4931 0.5352 0.092 0.000 0.132 0.776
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.1118 0.7352 0.964 0.000 0.000 0.036
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.5790 0.6310 0.044 0.000 0.616 0.340
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.6178 0.5541 0.660 0.112 0.000 0.228
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 3 0.6500 0.5309 0.080 0.000 0.544 0.376
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.3791 0.6745 0.796 0.000 0.004 0.200
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.1474 0.7335 0.948 0.000 0.000 0.052
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0336 0.9286 0.000 0.992 0.008 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.3266 0.6747 0.832 0.000 0.000 0.168
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.2408 0.9222 0.104 0.000 0.000 0.896
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.6810 0.5204 0.032 0.648 0.232 0.088
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.9308 0.000 1.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.3591 0.6240 0.824 0.168 0.000 0.008
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.4482 0.5661 0.000 0.264 0.728 0.008
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.4008 0.5986 0.756 0.000 0.000 0.244
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.4467 0.7108 0.040 0.788 0.000 0.172
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.1118 0.7372 0.964 0.000 0.000 0.036
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.9308 0.000 1.000 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.8681 0.5874 0.188 0.124 0.528 0.160
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.2408 0.9222 0.104 0.000 0.000 0.896
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0927 0.8131 0.016 0.000 0.976 0.008
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.9308 0.000 1.000 0.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 3 0.7043 0.6633 0.236 0.048 0.636 0.080
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.0376 0.8133 0.004 0.000 0.992 0.004
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0524 0.9268 0.000 0.988 0.004 0.008
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.2976 0.7903 0.120 0.000 0.872 0.008
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0469 0.8129 0.000 0.000 0.988 0.012
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0921 0.8164 0.028 0.000 0.972 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.5757 0.5328 0.076 0.684 0.000 0.240
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0188 0.9298 0.000 0.996 0.004 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.9308 0.000 1.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.6028 0.6619 0.076 0.000 0.644 0.280
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.4972 0.7817 0.040 0.060 0.808 0.092
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 5 0.4745 0.1515 0.424 0.000 0.012 0.004 0.560
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 5 0.4047 0.5924 0.000 0.320 0.004 0.000 0.676
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0404 0.9181 0.000 0.988 0.000 0.000 0.012
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.2110 0.8672 0.072 0.912 0.000 0.000 0.016
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.6272 0.7192 0.140 0.092 0.108 0.000 0.660
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.5047 -0.1063 0.024 0.004 0.504 0.000 0.468
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.3350 0.8092 0.004 0.040 0.844 0.000 0.112
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 5 0.4236 0.5782 0.000 0.328 0.004 0.004 0.664
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.4776 0.4743 0.020 0.000 0.612 0.004 0.364
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.2233 0.8326 0.892 0.000 0.000 0.004 0.104
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.5837 0.5962 0.600 0.000 0.092 0.012 0.296
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.2209 0.8739 0.000 0.912 0.032 0.000 0.056
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.3634 0.8063 0.000 0.076 0.832 0.004 0.088
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.4468 0.6641 0.756 0.000 0.172 0.004 0.068
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.3239 0.8019 0.012 0.000 0.828 0.004 0.156
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.4658 0.0314 0.000 0.504 0.484 0.000 0.012
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.9208 0.000 1.000 0.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0162 0.8539 0.996 0.000 0.000 0.000 0.004
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0451 0.9195 0.004 0.988 0.000 0.000 0.008
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0162 0.9495 0.004 0.000 0.000 0.996 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.9208 0.000 1.000 0.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.5236 0.5852 0.008 0.060 0.652 0.000 0.280
#> F325847E-F046-4B67-B01C-16919C401020 3 0.4166 0.4378 0.000 0.000 0.648 0.004 0.348
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0932 0.8564 0.004 0.000 0.972 0.004 0.020
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0566 0.9182 0.004 0.984 0.000 0.000 0.012
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0162 0.9495 0.004 0.000 0.000 0.996 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.2848 0.8122 0.000 0.000 0.840 0.004 0.156
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.5905 0.3224 0.556 0.000 0.336 0.004 0.104
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.0566 0.8503 0.000 0.000 0.984 0.004 0.012
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0880 0.8415 0.968 0.000 0.000 0.000 0.032
#> A8E48877-F8AB-44DD-A18B-194D87C44931 5 0.4456 0.6748 0.000 0.248 0.032 0.004 0.716
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.3123 0.7762 0.000 0.000 0.812 0.004 0.184
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.1087 0.9128 0.008 0.968 0.000 0.008 0.016
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0510 0.8540 0.984 0.000 0.000 0.000 0.016
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.2387 0.7179 0.048 0.000 0.040 0.004 0.908
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.2583 0.7983 0.864 0.000 0.000 0.132 0.004
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0162 0.9495 0.004 0.000 0.000 0.996 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0703 0.8549 0.000 0.000 0.976 0.000 0.024
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.1830 0.8317 0.000 0.068 0.924 0.000 0.008
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0162 0.9205 0.000 0.996 0.000 0.000 0.004
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0162 0.8543 0.000 0.000 0.996 0.000 0.004
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0162 0.9495 0.004 0.000 0.000 0.996 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.2632 0.8263 0.000 0.072 0.888 0.000 0.040
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0162 0.9495 0.004 0.000 0.000 0.996 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 4 0.6458 -0.1636 0.180 0.000 0.000 0.424 0.396
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.3260 0.7739 0.084 0.000 0.856 0.004 0.056
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 5 0.4863 0.7003 0.004 0.200 0.068 0.004 0.724
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0404 0.8554 0.000 0.000 0.988 0.000 0.012
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.2635 0.6980 0.088 0.016 0.008 0.000 0.888
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.4862 0.5353 0.000 0.364 0.032 0.000 0.604
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.4701 0.6728 0.236 0.000 0.060 0.000 0.704
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.3760 0.7803 0.784 0.000 0.000 0.028 0.188
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 5 0.4564 0.6901 0.072 0.000 0.176 0.004 0.748
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.4865 0.7223 0.160 0.000 0.088 0.012 0.740
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.1914 0.8785 0.060 0.924 0.000 0.000 0.016
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.1117 0.8554 0.964 0.000 0.000 0.020 0.016
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0290 0.9451 0.008 0.000 0.000 0.992 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0880 0.9081 0.000 0.968 0.000 0.000 0.032
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.5421 0.6080 0.096 0.000 0.276 0.000 0.628
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.2199 0.7303 0.016 0.000 0.060 0.008 0.916
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0794 0.8544 0.972 0.000 0.000 0.000 0.028
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.3142 0.7856 0.076 0.000 0.864 0.004 0.056
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0290 0.9200 0.000 0.992 0.000 0.000 0.008
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0579 0.8535 0.984 0.000 0.000 0.008 0.008
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.6166 0.4920 0.084 0.024 0.340 0.000 0.552
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0162 0.9206 0.000 0.996 0.000 0.000 0.004
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.4583 0.7130 0.192 0.000 0.064 0.004 0.740
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.4937 0.5662 0.604 0.000 0.028 0.004 0.364
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0162 0.9495 0.004 0.000 0.000 0.996 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0671 0.9166 0.004 0.980 0.000 0.000 0.016
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0451 0.9195 0.004 0.988 0.000 0.000 0.008
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.2304 0.8455 0.908 0.000 0.000 0.044 0.048
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.2747 0.7985 0.888 0.000 0.060 0.004 0.048
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0703 0.8574 0.000 0.000 0.976 0.000 0.024
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.2674 0.7969 0.000 0.000 0.868 0.120 0.012
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0162 0.9495 0.004 0.000 0.000 0.996 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.4687 0.6048 0.040 0.000 0.288 0.000 0.672
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0290 0.9195 0.000 0.992 0.000 0.000 0.008
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.2763 0.8079 0.000 0.000 0.848 0.004 0.148
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.3086 0.7796 0.000 0.000 0.816 0.004 0.180
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.4228 0.7503 0.748 0.000 0.032 0.004 0.216
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0693 0.8524 0.980 0.000 0.000 0.008 0.012
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0162 0.9495 0.004 0.000 0.000 0.996 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.1043 0.8558 0.000 0.000 0.960 0.000 0.040
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0162 0.9495 0.004 0.000 0.000 0.996 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0290 0.9195 0.000 0.992 0.000 0.000 0.008
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0162 0.9205 0.000 0.996 0.000 0.000 0.004
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.3463 0.7750 0.016 0.820 0.008 0.000 0.156
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0566 0.8514 0.984 0.000 0.000 0.004 0.012
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0404 0.9181 0.000 0.988 0.000 0.000 0.012
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0290 0.9200 0.000 0.992 0.000 0.000 0.008
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.1518 0.8378 0.004 0.000 0.944 0.004 0.048
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.5251 0.6972 0.184 0.000 0.136 0.000 0.680
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.4547 0.6519 0.056 0.000 0.044 0.112 0.788
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0579 0.8535 0.984 0.000 0.000 0.008 0.008
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 5 0.4840 0.6998 0.012 0.000 0.152 0.092 0.744
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.4318 0.7429 0.736 0.032 0.000 0.004 0.228
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.3442 0.7150 0.044 0.000 0.088 0.016 0.852
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.3612 0.7552 0.764 0.000 0.000 0.008 0.228
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.2773 0.8023 0.836 0.000 0.000 0.000 0.164
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0671 0.9158 0.000 0.980 0.004 0.000 0.016
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.1549 0.8494 0.944 0.000 0.000 0.040 0.016
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0162 0.9495 0.004 0.000 0.000 0.996 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 5 0.4651 0.6959 0.000 0.208 0.060 0.004 0.728
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0162 0.9206 0.000 0.996 0.000 0.000 0.004
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.3375 0.7517 0.840 0.104 0.000 0.000 0.056
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.1892 0.8239 0.000 0.080 0.916 0.000 0.004
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.1741 0.8512 0.936 0.000 0.000 0.040 0.024
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.4617 0.5182 0.008 0.660 0.000 0.316 0.016
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0579 0.8535 0.984 0.000 0.000 0.008 0.008
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0671 0.9175 0.004 0.980 0.000 0.000 0.016
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.2822 0.7411 0.064 0.012 0.036 0.000 0.888
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0162 0.9495 0.004 0.000 0.000 0.996 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.8534 0.000 0.000 1.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.9208 0.000 1.000 0.000 0.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.7769 0.5575 0.204 0.160 0.148 0.000 0.488
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.1894 0.8502 0.000 0.008 0.920 0.000 0.072
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.3774 0.5002 0.000 0.704 0.000 0.000 0.296
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.1701 0.8378 0.016 0.000 0.936 0.000 0.048
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0162 0.8543 0.000 0.000 0.996 0.000 0.004
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.2068 0.8410 0.000 0.000 0.904 0.004 0.092
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.5735 0.5608 0.088 0.652 0.000 0.024 0.236
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.1121 0.9001 0.000 0.956 0.000 0.000 0.044
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0162 0.9205 0.000 0.996 0.000 0.000 0.004
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.2125 0.7257 0.024 0.000 0.052 0.004 0.920
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 5 0.4141 0.6655 0.004 0.028 0.196 0.004 0.768
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 6 0.5666 0.2826 0.276 0.000 0.024 0.000 0.120 0.580
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 6 0.4769 0.3697 0.000 0.060 0.000 0.000 0.364 0.576
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.3221 0.7635 0.000 0.736 0.000 0.000 0.000 0.264
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.3389 0.6565 0.164 0.800 0.000 0.000 0.004 0.032
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.1026 0.7304 0.008 0.004 0.008 0.000 0.968 0.012
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 5 0.4823 0.3288 0.000 0.040 0.280 0.000 0.652 0.028
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.5098 0.5083 0.000 0.020 0.652 0.000 0.240 0.088
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 6 0.5170 0.3941 0.000 0.204 0.000 0.000 0.176 0.620
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.4960 0.5015 0.024 0.000 0.568 0.000 0.032 0.376
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0858 0.8490 0.968 0.000 0.000 0.000 0.004 0.028
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.5592 0.2862 0.484 0.000 0.112 0.000 0.008 0.396
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.5426 0.5060 0.000 0.628 0.016 0.000 0.188 0.168
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.4064 0.5773 0.000 0.020 0.644 0.000 0.000 0.336
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.5981 0.3697 0.536 0.000 0.324 0.000 0.072 0.068
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.3514 0.7407 0.004 0.000 0.768 0.000 0.020 0.208
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.5472 0.1372 0.000 0.364 0.504 0.000 0.000 0.132
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.2416 0.8051 0.000 0.844 0.000 0.000 0.000 0.156
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0603 0.8558 0.980 0.000 0.000 0.000 0.016 0.004
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0692 0.8180 0.004 0.976 0.000 0.000 0.000 0.020
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 0.9996 0.000 0.000 0.000 1.000 0.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.1267 0.8183 0.000 0.940 0.000 0.000 0.000 0.060
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.4736 0.4474 0.024 0.004 0.532 0.000 0.008 0.432
#> F325847E-F046-4B67-B01C-16919C401020 3 0.5648 0.2390 0.000 0.000 0.528 0.000 0.276 0.196
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0363 0.8319 0.000 0.000 0.988 0.000 0.000 0.012
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0363 0.8118 0.000 0.988 0.000 0.000 0.000 0.012
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 0.9996 0.000 0.000 0.000 1.000 0.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.4018 0.6209 0.000 0.000 0.656 0.000 0.020 0.324
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.7072 0.0437 0.360 0.000 0.336 0.000 0.228 0.076
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.1218 0.8240 0.004 0.000 0.956 0.000 0.012 0.028
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0777 0.8529 0.972 0.000 0.000 0.000 0.024 0.004
#> A8E48877-F8AB-44DD-A18B-194D87C44931 6 0.4097 0.1933 0.000 0.000 0.008 0.000 0.488 0.504
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.4039 0.7019 0.000 0.000 0.732 0.000 0.060 0.208
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.1121 0.8038 0.008 0.964 0.000 0.008 0.004 0.016
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0806 0.8556 0.972 0.000 0.000 0.000 0.020 0.008
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 6 0.4792 -0.0202 0.024 0.000 0.016 0.000 0.452 0.508
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.1232 0.8496 0.956 0.000 0.004 0.024 0.000 0.016
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 0.9996 0.000 0.000 0.000 1.000 0.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0547 0.8313 0.000 0.000 0.980 0.000 0.000 0.020
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.1616 0.8194 0.000 0.020 0.932 0.000 0.000 0.048
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.3221 0.7670 0.000 0.736 0.000 0.000 0.000 0.264
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0603 0.8303 0.000 0.000 0.980 0.000 0.016 0.004
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 0.9996 0.000 0.000 0.000 1.000 0.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.2163 0.8155 0.000 0.008 0.892 0.000 0.004 0.096
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 0.9996 0.000 0.000 0.000 1.000 0.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.2698 0.6668 0.008 0.008 0.000 0.120 0.860 0.004
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.2307 0.8046 0.032 0.000 0.904 0.000 0.016 0.048
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 6 0.4179 0.2375 0.000 0.000 0.012 0.000 0.472 0.516
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0909 0.8317 0.000 0.000 0.968 0.000 0.020 0.012
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.2477 0.7041 0.032 0.024 0.000 0.000 0.896 0.048
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.2197 0.6778 0.000 0.056 0.000 0.000 0.900 0.044
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.0912 0.7295 0.012 0.008 0.004 0.004 0.972 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.1364 0.8421 0.944 0.000 0.004 0.000 0.004 0.048
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 5 0.4115 0.2103 0.004 0.000 0.012 0.000 0.624 0.360
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.3809 0.4535 0.008 0.000 0.012 0.000 0.716 0.264
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.3853 0.6395 0.156 0.780 0.000 0.000 0.012 0.052
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.1059 0.8557 0.964 0.000 0.004 0.000 0.016 0.016
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0146 0.9958 0.000 0.000 0.000 0.996 0.000 0.004
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.3464 0.7263 0.000 0.688 0.000 0.000 0.000 0.312
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.1003 0.7318 0.004 0.000 0.028 0.000 0.964 0.004
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.3787 0.4670 0.008 0.000 0.012 0.000 0.720 0.260
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0914 0.8556 0.968 0.000 0.000 0.000 0.016 0.016
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.2213 0.8045 0.032 0.000 0.908 0.000 0.012 0.048
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.8148 0.000 1.000 0.000 0.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.1232 0.8544 0.956 0.000 0.004 0.000 0.024 0.016
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.1296 0.7219 0.004 0.004 0.044 0.000 0.948 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0547 0.8177 0.000 0.980 0.000 0.000 0.000 0.020
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.3863 0.5091 0.008 0.008 0.012 0.000 0.740 0.232
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 6 0.5291 -0.0441 0.372 0.000 0.068 0.000 0.016 0.544
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 0.9996 0.000 0.000 0.000 1.000 0.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0858 0.8038 0.004 0.968 0.000 0.000 0.000 0.028
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0891 0.8183 0.008 0.968 0.000 0.000 0.000 0.024
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0603 0.8534 0.980 0.000 0.004 0.000 0.000 0.016
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.3817 0.6987 0.784 0.000 0.152 0.000 0.012 0.052
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.1418 0.8313 0.000 0.000 0.944 0.000 0.024 0.032
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.1082 0.8259 0.000 0.000 0.956 0.040 0.004 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 0.9996 0.000 0.000 0.000 1.000 0.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.1138 0.7329 0.004 0.000 0.024 0.000 0.960 0.012
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.2996 0.7830 0.000 0.772 0.000 0.000 0.000 0.228
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.3053 0.7640 0.000 0.000 0.812 0.000 0.020 0.168
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.3592 0.7019 0.000 0.000 0.740 0.000 0.020 0.240
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.2994 0.7771 0.856 0.000 0.076 0.000 0.008 0.060
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.1036 0.8532 0.964 0.000 0.004 0.000 0.024 0.008
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0000 0.9996 0.000 0.000 0.000 1.000 0.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.1049 0.8294 0.000 0.000 0.960 0.000 0.008 0.032
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0000 0.9996 0.000 0.000 0.000 1.000 0.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.3288 0.7551 0.000 0.724 0.000 0.000 0.000 0.276
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.2912 0.7884 0.000 0.784 0.000 0.000 0.000 0.216
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.4415 0.5973 0.000 0.556 0.020 0.000 0.004 0.420
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0632 0.8537 0.976 0.000 0.000 0.000 0.024 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.3309 0.7521 0.000 0.720 0.000 0.000 0.000 0.280
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0146 0.8142 0.000 0.996 0.000 0.000 0.000 0.004
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.1390 0.8225 0.004 0.000 0.948 0.000 0.016 0.032
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.0806 0.7331 0.008 0.000 0.020 0.000 0.972 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.3529 0.6168 0.024 0.000 0.008 0.020 0.820 0.128
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0632 0.8537 0.976 0.000 0.000 0.000 0.024 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 5 0.3423 0.6195 0.008 0.000 0.012 0.016 0.812 0.152
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.3884 0.6704 0.736 0.020 0.000 0.000 0.012 0.232
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.4419 0.4010 0.024 0.000 0.024 0.000 0.684 0.268
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.3452 0.6683 0.736 0.004 0.000 0.000 0.004 0.256
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.1082 0.8452 0.956 0.000 0.000 0.000 0.004 0.040
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.3371 0.7423 0.000 0.708 0.000 0.000 0.000 0.292
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.1368 0.8513 0.956 0.012 0.004 0.008 0.004 0.016
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 0.9996 0.000 0.000 0.000 1.000 0.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 6 0.4370 0.3228 0.000 0.008 0.012 0.000 0.428 0.552
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0146 0.8142 0.000 0.996 0.000 0.000 0.000 0.004
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.4867 0.4413 0.600 0.340 0.000 0.000 0.012 0.048
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0622 0.8310 0.000 0.008 0.980 0.000 0.000 0.012
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.1138 0.8551 0.960 0.000 0.004 0.000 0.012 0.024
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.5082 0.4375 0.024 0.636 0.000 0.284 0.004 0.052
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0603 0.8559 0.980 0.000 0.000 0.000 0.016 0.004
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0717 0.8081 0.000 0.976 0.000 0.000 0.008 0.016
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.0717 0.7295 0.008 0.000 0.000 0.000 0.976 0.016
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 0.9996 0.000 0.000 0.000 1.000 0.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0725 0.8292 0.000 0.000 0.976 0.000 0.012 0.012
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0632 0.8185 0.000 0.976 0.000 0.000 0.000 0.024
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.3573 0.6243 0.012 0.080 0.024 0.000 0.836 0.048
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.2553 0.7960 0.000 0.000 0.848 0.000 0.008 0.144
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 6 0.5461 0.3387 0.000 0.140 0.000 0.000 0.332 0.528
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.1453 0.8220 0.008 0.000 0.944 0.000 0.008 0.040
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0291 0.8307 0.000 0.000 0.992 0.000 0.004 0.004
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.1745 0.8203 0.000 0.000 0.920 0.000 0.012 0.068
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 6 0.5374 -0.2715 0.068 0.344 0.012 0.000 0.008 0.568
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.3819 0.6861 0.000 0.652 0.000 0.000 0.008 0.340
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.2697 0.7974 0.000 0.812 0.000 0.000 0.000 0.188
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.4190 0.3599 0.016 0.000 0.012 0.000 0.668 0.304
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 6 0.4136 0.3206 0.000 0.000 0.012 0.000 0.428 0.560
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "hclust"]
# you can also extract it by
# res = res_list["CV:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.552 0.827 0.903 0.2626 0.689 0.689
#> 3 3 0.187 0.466 0.761 0.7563 0.850 0.786
#> 4 4 0.284 0.491 0.678 0.2741 0.728 0.542
#> 5 5 0.349 0.621 0.721 0.1067 0.829 0.593
#> 6 6 0.467 0.575 0.748 0.0656 1.000 0.999
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.0376 0.930 0.996 0.004
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 1 0.0000 0.934 1.000 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 1 0.0000 0.934 1.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.0000 0.934 1.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.0000 0.934 1.000 0.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.9608 -0.137 0.616 0.384
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.9608 -0.137 0.616 0.384
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 1 0.0000 0.934 1.000 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 2 0.9963 0.679 0.464 0.536
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.934 1.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.8955 0.234 0.688 0.312
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.7883 0.511 0.764 0.236
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 1 0.5408 0.761 0.876 0.124
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.6343 0.712 0.840 0.160
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 2 0.9954 0.686 0.460 0.540
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.9635 0.788 0.388 0.612
#> DC55EE78-203F-4092-9B83-14B1A529194B 1 0.3733 0.849 0.928 0.072
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0000 0.934 1.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 1 0.0000 0.934 1.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.0000 0.934 1.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 1 0.1184 0.917 0.984 0.016
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.9833 0.757 0.424 0.576
#> F325847E-F046-4B67-B01C-16919C401020 2 0.9608 0.807 0.384 0.616
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.9087 0.179 0.676 0.324
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 1 0.0000 0.934 1.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.0000 0.934 1.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 2 0.9608 0.807 0.384 0.616
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0000 0.934 1.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 2 0.5737 0.717 0.136 0.864
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.934 1.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 1 0.0000 0.934 1.000 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.8386 0.409 0.732 0.268
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 1 0.0000 0.934 1.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.934 1.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.0000 0.934 1.000 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.934 1.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.0000 0.934 1.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 2 0.4690 0.700 0.100 0.900
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 1 0.9996 -0.526 0.512 0.488
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 1 0.0672 0.925 0.992 0.008
#> 604C06E9-A00E-435E-847A-3992922A5C56 2 0.9833 0.757 0.424 0.576
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.0000 0.934 1.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.9580 0.810 0.380 0.620
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.0000 0.934 1.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.0000 0.934 1.000 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 2 0.9170 0.829 0.332 0.668
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 1 0.0000 0.934 1.000 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 2 0.9833 0.757 0.424 0.576
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.0000 0.934 1.000 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 1 0.1414 0.912 0.980 0.020
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0000 0.934 1.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0000 0.934 1.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.0000 0.934 1.000 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.0000 0.934 1.000 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.0000 0.934 1.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.934 1.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0000 0.934 1.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 1 0.0000 0.934 1.000 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.0000 0.934 1.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0000 0.934 1.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.934 1.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 2 0.8443 0.815 0.272 0.728
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 1 0.0000 0.934 1.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0672 0.926 0.992 0.008
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.0000 0.934 1.000 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 1 0.0000 0.934 1.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.0000 0.934 1.000 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.7950 0.543 0.760 0.240
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.0000 0.934 1.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 1 0.1414 0.912 0.980 0.020
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 1 0.0000 0.934 1.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.934 1.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.4298 0.827 0.912 0.088
#> 721CDBE6-FC85-4C30-B23E-28407340286F 2 0.7745 0.789 0.228 0.772
#> 392897E4-6009-422C-B461-649F4DDF260C 2 0.9087 0.829 0.324 0.676
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.0000 0.934 1.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0000 0.934 1.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 1 0.0000 0.934 1.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.7745 0.565 0.772 0.228
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.8909 0.317 0.692 0.308
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.4431 0.821 0.908 0.092
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.934 1.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.0000 0.934 1.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 2 0.9608 0.807 0.384 0.616
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.0000 0.934 1.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 1 0.0000 0.934 1.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.9087 0.829 0.324 0.676
#> 53A96249-66D5-4C26-893B-ADC71481D261 1 0.0376 0.930 0.996 0.004
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.934 1.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 1 0.7528 0.568 0.784 0.216
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 1 0.0000 0.934 1.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 2 0.7453 0.786 0.212 0.788
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0000 0.934 1.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.0000 0.934 1.000 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.934 1.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.8327 0.399 0.736 0.264
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.0000 0.934 1.000 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.0000 0.934 1.000 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.0000 0.934 1.000 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.934 1.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 1 0.0000 0.934 1.000 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.934 1.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.0000 0.934 1.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 1 0.0000 0.934 1.000 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 1 0.0000 0.934 1.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.0000 0.934 1.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.7376 0.780 0.208 0.792
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.934 1.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 1 0.0000 0.934 1.000 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.934 1.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 1 0.0000 0.934 1.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.0000 0.934 1.000 0.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.0000 0.934 1.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 2 0.0000 0.627 0.000 1.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 1 0.8144 0.498 0.748 0.252
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.0000 0.934 1.000 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.9580 0.810 0.380 0.620
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 1 0.0000 0.934 1.000 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 2 0.8608 0.809 0.284 0.716
#> B12A4446-2310-4139-897F-CA030478CBD5 2 0.9087 0.829 0.324 0.676
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 2 0.9963 0.679 0.464 0.536
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 1 0.0000 0.934 1.000 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 1 0.0000 0.934 1.000 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 1 0.0000 0.934 1.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.0000 0.934 1.000 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 1 0.0000 0.934 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.1129 0.7001 0.976 0.004 0.020
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 1 0.4810 0.5562 0.832 0.140 0.028
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 1 0.6295 -0.6584 0.528 0.472 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.6252 -0.5702 0.556 0.444 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.1182 0.6966 0.976 0.012 0.012
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.8979 0.3679 0.420 0.128 0.452
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.8979 0.3679 0.420 0.128 0.452
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 1 0.4810 0.5562 0.832 0.140 0.028
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.6584 0.6019 0.380 0.012 0.608
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0747 0.6984 0.984 0.000 0.016
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.6796 0.1012 0.612 0.020 0.368
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.8573 0.1158 0.584 0.136 0.280
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 1 0.7562 0.3324 0.692 0.148 0.160
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.5955 0.4851 0.772 0.048 0.180
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.6721 0.6018 0.380 0.016 0.604
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.8045 0.6812 0.272 0.104 0.624
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.7925 0.6728 0.344 0.584 0.072
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.1453 0.7003 0.968 0.008 0.024
#> F772EA39-E408-4908-BADD-C786D702BF9B 1 0.6215 -0.5182 0.572 0.428 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.4121 0.6308 0.876 0.084 0.040
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.6520 0.7377 0.488 0.508 0.004
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.6750 0.6548 0.336 0.024 0.640
#> F325847E-F046-4B67-B01C-16919C401020 3 0.7677 0.7232 0.204 0.120 0.676
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.6865 0.0335 0.596 0.020 0.384
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 1 0.6280 -0.6221 0.540 0.460 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.4121 0.6308 0.876 0.084 0.040
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.7677 0.7232 0.204 0.120 0.676
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0424 0.6996 0.992 0.000 0.008
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.7391 0.5819 0.056 0.308 0.636
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0747 0.6984 0.984 0.000 0.016
#> A8E48877-F8AB-44DD-A18B-194D87C44931 1 0.4810 0.5562 0.832 0.140 0.028
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.8362 0.0109 0.556 0.096 0.348
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.6680 0.7616 0.484 0.508 0.008
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0747 0.6984 0.984 0.000 0.016
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.0592 0.6993 0.988 0.000 0.012
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0892 0.6971 0.980 0.000 0.020
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.4121 0.6308 0.876 0.084 0.040
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.6424 0.6166 0.068 0.180 0.752
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.8872 0.5623 0.348 0.132 0.520
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 1 0.6688 -0.4839 0.580 0.408 0.012
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.6627 0.6574 0.336 0.020 0.644
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.4121 0.6308 0.876 0.084 0.040
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.7651 0.7256 0.196 0.124 0.680
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.4121 0.6308 0.876 0.084 0.040
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.0424 0.6996 0.992 0.000 0.008
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.7878 0.7259 0.172 0.160 0.668
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 1 0.4810 0.5562 0.832 0.140 0.028
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.6627 0.6574 0.336 0.020 0.644
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.1905 0.6882 0.956 0.028 0.016
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 1 0.3802 0.6329 0.888 0.080 0.032
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.1182 0.6966 0.976 0.012 0.012
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0424 0.6993 0.992 0.000 0.008
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.4810 0.5562 0.832 0.140 0.028
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.0592 0.6993 0.988 0.000 0.012
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.6680 0.7616 0.484 0.508 0.008
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0892 0.6971 0.980 0.000 0.020
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.4121 0.6308 0.876 0.084 0.040
#> A54731AE-FC40-407F-8D10-67DDC122237D 1 0.6931 -0.6201 0.528 0.456 0.016
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.1182 0.6966 0.976 0.012 0.012
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0592 0.6993 0.988 0.000 0.012
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0747 0.6984 0.984 0.000 0.016
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.8436 0.7166 0.160 0.224 0.616
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 1 0.6252 -0.5702 0.556 0.444 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0829 0.7002 0.984 0.004 0.012
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.1182 0.6966 0.976 0.012 0.012
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 1 0.6235 -0.5453 0.564 0.436 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.0592 0.6993 0.988 0.000 0.012
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.7044 0.4282 0.724 0.108 0.168
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.4121 0.6308 0.876 0.084 0.040
#> EE16D845-31F2-4178-800B-CA2C358841AD 1 0.6516 -0.6896 0.516 0.480 0.004
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 1 0.6045 -0.3464 0.620 0.380 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0892 0.6971 0.980 0.000 0.020
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.3295 0.6434 0.896 0.008 0.096
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.7108 0.6646 0.100 0.184 0.716
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.7917 0.7148 0.152 0.184 0.664
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.4121 0.6308 0.876 0.084 0.040
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.1182 0.6966 0.976 0.012 0.012
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 1 0.6295 -0.6594 0.528 0.472 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.6853 0.4125 0.712 0.064 0.224
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.7448 0.1716 0.616 0.052 0.332
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.3375 0.6392 0.892 0.008 0.100
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0892 0.6971 0.980 0.000 0.020
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.4121 0.6308 0.876 0.084 0.040
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.7677 0.7232 0.204 0.120 0.676
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.4121 0.6308 0.876 0.084 0.040
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 1 0.6291 -0.6505 0.532 0.468 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 3 0.7917 0.7148 0.152 0.184 0.664
#> 53A96249-66D5-4C26-893B-ADC71481D261 1 0.5803 0.2880 0.736 0.248 0.016
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0747 0.6984 0.984 0.000 0.016
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.7605 0.3571 0.192 0.684 0.124
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.6680 0.7616 0.484 0.508 0.008
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.5677 0.7038 0.124 0.072 0.804
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.1182 0.6966 0.976 0.012 0.012
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.1182 0.6966 0.976 0.012 0.012
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0747 0.6984 0.984 0.000 0.016
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.6126 0.3385 0.712 0.020 0.268
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.1905 0.6882 0.956 0.028 0.016
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.1182 0.6966 0.976 0.012 0.012
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.2031 0.6876 0.952 0.032 0.016
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0747 0.6984 0.984 0.000 0.016
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 1 0.6799 -0.6156 0.532 0.456 0.012
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0424 0.6993 0.992 0.000 0.008
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.4121 0.6308 0.876 0.084 0.040
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 1 0.4810 0.5562 0.832 0.140 0.028
#> 2D962371-EC83-490C-A663-478AF383BC1B 1 0.6252 -0.5702 0.556 0.444 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.5988 -0.2841 0.632 0.368 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.6793 0.6995 0.128 0.128 0.744
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0424 0.6993 0.992 0.000 0.008
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.6680 0.7616 0.484 0.508 0.008
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0747 0.6984 0.984 0.000 0.016
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.6680 0.7616 0.484 0.508 0.008
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.1182 0.6966 0.976 0.012 0.012
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.4121 0.6308 0.876 0.084 0.040
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.5058 0.5447 0.000 0.244 0.756
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.9616 0.3568 0.344 0.444 0.212
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.1182 0.6966 0.976 0.012 0.012
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.7651 0.7256 0.196 0.124 0.680
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 1 0.4810 0.5562 0.832 0.140 0.028
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.7635 0.7085 0.212 0.112 0.676
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.7917 0.7148 0.152 0.184 0.664
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.6584 0.6019 0.380 0.012 0.608
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 1 0.3965 0.5785 0.860 0.132 0.008
#> A608BCEB-2C27-4927-A308-E6975F641722 1 0.5681 0.3308 0.748 0.236 0.016
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 1 0.6008 -0.3129 0.628 0.372 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.0592 0.6993 0.988 0.000 0.012
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 1 0.4810 0.5562 0.832 0.140 0.028
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.430 0.71620 0.752 0.240 0.008 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.586 -0.07938 0.484 0.484 0.032 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.205 0.71558 0.072 0.924 0.000 0.004
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.253 0.72164 0.100 0.896 0.000 0.004
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.443 0.69254 0.720 0.276 0.004 0.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.747 0.41136 0.256 0.192 0.544 0.008
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.747 0.41136 0.256 0.192 0.544 0.008
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.586 -0.07938 0.484 0.484 0.032 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.716 0.34853 0.352 0.020 0.540 0.088
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.379 0.72460 0.796 0.200 0.004 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.715 0.09037 0.584 0.036 0.304 0.076
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 3 0.817 0.08882 0.348 0.292 0.352 0.008
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.787 0.12377 0.380 0.456 0.140 0.024
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.792 0.51604 0.584 0.200 0.152 0.064
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.721 0.34547 0.352 0.020 0.536 0.092
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.748 0.36260 0.144 0.116 0.644 0.096
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.434 0.43623 0.008 0.820 0.128 0.044
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.419 0.71429 0.764 0.228 0.008 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.253 0.71575 0.112 0.888 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.443 0.50565 0.820 0.032 0.020 0.128
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.201 0.68687 0.040 0.940 0.008 0.012
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.716 0.35164 0.304 0.028 0.580 0.088
#> F325847E-F046-4B67-B01C-16919C401020 3 0.515 0.44309 0.084 0.124 0.780 0.012
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.704 -0.00455 0.572 0.024 0.324 0.080
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.194 0.71908 0.076 0.924 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.443 0.50565 0.820 0.032 0.020 0.128
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.515 0.44309 0.084 0.124 0.780 0.012
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.410 0.70865 0.744 0.256 0.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 4 0.678 0.68922 0.032 0.068 0.272 0.628
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.379 0.72460 0.796 0.200 0.004 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.586 -0.07938 0.484 0.484 0.032 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.748 0.23084 0.420 0.152 0.424 0.004
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.117 0.67080 0.020 0.968 0.000 0.012
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.379 0.72460 0.796 0.200 0.004 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.431 0.70708 0.736 0.260 0.004 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.379 0.72239 0.796 0.200 0.004 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.443 0.50565 0.820 0.032 0.020 0.128
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 4 0.660 0.60014 0.080 0.000 0.436 0.484
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.768 0.40443 0.228 0.096 0.600 0.076
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.398 0.64875 0.192 0.796 0.000 0.012
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.704 0.34800 0.308 0.020 0.580 0.092
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.443 0.50565 0.820 0.032 0.020 0.128
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.498 0.43327 0.084 0.112 0.792 0.012
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.443 0.50565 0.820 0.032 0.020 0.128
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.410 0.70865 0.744 0.256 0.000 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.704 0.24734 0.088 0.048 0.644 0.220
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.586 -0.07938 0.484 0.484 0.032 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.704 0.34800 0.308 0.020 0.580 0.092
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.490 0.59945 0.660 0.332 0.008 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 1 0.572 0.44154 0.580 0.388 0.032 0.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.443 0.69254 0.720 0.276 0.004 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.384 0.72279 0.776 0.224 0.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 2 0.586 -0.07938 0.484 0.484 0.032 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.431 0.70708 0.736 0.260 0.004 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.117 0.67080 0.020 0.968 0.000 0.012
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.379 0.72239 0.796 0.200 0.004 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.443 0.50565 0.820 0.032 0.020 0.128
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.287 0.71411 0.104 0.884 0.012 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.443 0.69254 0.720 0.276 0.004 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.431 0.70708 0.736 0.260 0.004 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.379 0.72460 0.796 0.200 0.004 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.768 0.06633 0.052 0.088 0.540 0.320
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.253 0.72164 0.100 0.896 0.000 0.004
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.420 0.72412 0.768 0.224 0.004 0.004
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.443 0.69254 0.720 0.276 0.004 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.241 0.71942 0.104 0.896 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.431 0.70708 0.736 0.260 0.004 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.606 0.51723 0.712 0.032 0.060 0.196
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.443 0.50565 0.820 0.032 0.020 0.128
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.256 0.71153 0.068 0.912 0.004 0.016
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.340 0.66363 0.180 0.820 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.387 0.72388 0.788 0.208 0.004 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.650 0.64884 0.688 0.200 0.052 0.060
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.597 -0.09563 0.040 0.012 0.644 0.304
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.665 0.24839 0.060 0.060 0.680 0.200
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.443 0.50565 0.820 0.032 0.020 0.128
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.443 0.69254 0.720 0.276 0.004 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.205 0.71564 0.072 0.924 0.000 0.004
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.635 0.42731 0.692 0.016 0.164 0.128
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.786 0.11084 0.544 0.052 0.292 0.112
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.657 0.64484 0.684 0.200 0.052 0.064
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.379 0.72239 0.796 0.200 0.004 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.443 0.50565 0.820 0.032 0.020 0.128
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.515 0.44309 0.084 0.124 0.780 0.012
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.443 0.50565 0.820 0.032 0.020 0.128
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.213 0.71713 0.076 0.920 0.000 0.004
#> 322AF320-1379-4F51-AFDC-5292A060CD52 3 0.665 0.24839 0.060 0.060 0.680 0.200
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.516 0.31504 0.364 0.624 0.012 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.379 0.72460 0.796 0.200 0.004 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.660 0.18951 0.008 0.652 0.200 0.140
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.117 0.67080 0.020 0.968 0.000 0.012
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.600 -0.01405 0.088 0.008 0.696 0.208
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.443 0.69254 0.720 0.276 0.004 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.443 0.69254 0.720 0.276 0.004 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.379 0.72460 0.796 0.200 0.004 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.847 0.32684 0.452 0.276 0.236 0.036
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.490 0.59945 0.660 0.332 0.008 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.443 0.69254 0.720 0.276 0.004 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.494 0.58840 0.652 0.340 0.008 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.379 0.72460 0.796 0.200 0.004 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.274 0.71659 0.104 0.888 0.008 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.384 0.72279 0.776 0.224 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.443 0.50565 0.820 0.032 0.020 0.128
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.586 -0.07938 0.484 0.484 0.032 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.253 0.72164 0.100 0.896 0.000 0.004
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.367 0.65156 0.188 0.808 0.000 0.004
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.651 -0.14017 0.084 0.012 0.636 0.268
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.384 0.72279 0.776 0.224 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.117 0.67080 0.020 0.968 0.000 0.012
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.379 0.72460 0.796 0.200 0.004 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.117 0.67080 0.020 0.968 0.000 0.012
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.443 0.69254 0.720 0.276 0.004 0.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.443 0.50565 0.820 0.032 0.020 0.128
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 4 0.452 0.73303 0.000 0.000 0.320 0.680
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.748 0.05223 0.048 0.592 0.260 0.100
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.443 0.69254 0.720 0.276 0.004 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.498 0.43327 0.084 0.112 0.792 0.012
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 1 0.586 0.03677 0.484 0.484 0.032 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.781 0.10320 0.116 0.076 0.596 0.212
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.665 0.24839 0.060 0.060 0.680 0.200
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.716 0.34853 0.352 0.020 0.540 0.088
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 1 0.530 0.15210 0.504 0.488 0.008 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.542 0.25939 0.388 0.596 0.012 0.004
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.349 0.65487 0.188 0.812 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.431 0.70708 0.736 0.260 0.004 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.586 -0.07938 0.484 0.484 0.032 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.1914 0.78128 0.928 0.056 0.008 0.008 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 1 0.4904 0.48892 0.644 0.316 0.036 0.004 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.3167 0.85062 0.172 0.820 0.004 0.004 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.3143 0.85010 0.204 0.796 0.000 0.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.2249 0.78251 0.896 0.096 0.008 0.000 0.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.5810 0.40628 0.212 0.176 0.612 0.000 0.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.5810 0.40628 0.212 0.176 0.612 0.000 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 1 0.4904 0.48892 0.644 0.316 0.036 0.004 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.7459 0.29896 0.316 0.056 0.504 0.092 0.032
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0727 0.75156 0.980 0.012 0.004 0.004 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.6920 0.10082 0.580 0.060 0.268 0.064 0.028
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 3 0.6697 0.16469 0.352 0.244 0.404 0.000 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 1 0.6937 0.26206 0.512 0.312 0.144 0.020 0.012
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.5840 0.51928 0.708 0.044 0.164 0.056 0.028
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.7526 0.29591 0.316 0.056 0.500 0.092 0.036
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.6636 0.38582 0.116 0.112 0.656 0.016 0.100
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.4612 0.58000 0.044 0.788 0.124 0.008 0.036
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.1717 0.76158 0.936 0.052 0.008 0.004 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.3430 0.83777 0.220 0.776 0.000 0.004 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.4213 1.00000 0.308 0.000 0.012 0.680 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.3377 0.83009 0.136 0.836 0.020 0.004 0.004
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.7500 0.31439 0.272 0.056 0.540 0.064 0.068
#> F325847E-F046-4B67-B01C-16919C401020 3 0.3117 0.45240 0.036 0.100 0.860 0.004 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.7078 0.02363 0.556 0.056 0.288 0.068 0.032
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.3246 0.85231 0.184 0.808 0.000 0.008 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.4213 1.00000 0.308 0.000 0.012 0.680 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.3117 0.45240 0.036 0.100 0.860 0.004 0.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.1544 0.78196 0.932 0.068 0.000 0.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 5 0.5684 0.66645 0.024 0.044 0.072 0.132 0.728
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0727 0.75156 0.980 0.012 0.004 0.004 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 1 0.4904 0.48892 0.644 0.316 0.036 0.004 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.6284 0.27898 0.372 0.136 0.488 0.004 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.3279 0.80696 0.116 0.852 0.012 0.016 0.004
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0727 0.75156 0.980 0.012 0.004 0.004 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.1991 0.78282 0.916 0.076 0.004 0.004 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0960 0.74790 0.972 0.016 0.008 0.004 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.4213 1.00000 0.308 0.000 0.012 0.680 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 5 0.6978 0.52760 0.056 0.016 0.292 0.084 0.552
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.6959 0.38802 0.172 0.092 0.624 0.024 0.088
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.4469 0.72428 0.320 0.664 0.004 0.004 0.008
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.7653 0.30392 0.272 0.056 0.528 0.072 0.072
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.4213 1.00000 0.308 0.000 0.012 0.680 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.2775 0.44089 0.020 0.100 0.876 0.004 0.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.4213 1.00000 0.308 0.000 0.012 0.680 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.1544 0.78196 0.932 0.068 0.000 0.000 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.6472 0.25867 0.032 0.068 0.624 0.032 0.244
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 1 0.4904 0.48892 0.644 0.316 0.036 0.004 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.7653 0.30392 0.272 0.056 0.528 0.072 0.072
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.3123 0.73920 0.828 0.160 0.012 0.000 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 1 0.4054 0.67421 0.760 0.204 0.036 0.000 0.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.2179 0.78204 0.896 0.100 0.004 0.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0880 0.76776 0.968 0.032 0.000 0.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.4904 0.48892 0.644 0.316 0.036 0.004 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.1991 0.78282 0.916 0.076 0.004 0.004 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.3279 0.80696 0.116 0.852 0.012 0.016 0.004
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0960 0.74790 0.972 0.016 0.008 0.004 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.4213 1.00000 0.308 0.000 0.012 0.680 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.4313 0.78202 0.260 0.716 0.016 0.008 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.2249 0.78251 0.896 0.096 0.008 0.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.1991 0.78282 0.916 0.076 0.004 0.004 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0727 0.75156 0.980 0.012 0.004 0.004 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.7031 0.16727 0.032 0.088 0.488 0.024 0.368
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.3143 0.85010 0.204 0.796 0.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.1365 0.77102 0.952 0.040 0.004 0.000 0.004
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.2249 0.78251 0.896 0.096 0.008 0.000 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.3366 0.84437 0.212 0.784 0.000 0.004 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.1991 0.78282 0.916 0.076 0.004 0.004 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.5460 0.42626 0.724 0.032 0.020 0.052 0.172
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.4213 1.00000 0.308 0.000 0.012 0.680 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.3437 0.85049 0.176 0.808 0.004 0.000 0.012
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.4122 0.72004 0.304 0.688 0.004 0.004 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.1153 0.75463 0.964 0.024 0.008 0.004 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.3770 0.67358 0.852 0.024 0.048 0.060 0.016
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.5900 -0.02560 0.000 0.036 0.560 0.044 0.360
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.5730 0.25736 0.008 0.080 0.648 0.012 0.252
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.4213 1.00000 0.308 0.000 0.012 0.680 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.2179 0.78204 0.896 0.100 0.004 0.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.3243 0.85194 0.180 0.812 0.004 0.004 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.6404 0.37716 0.664 0.020 0.164 0.052 0.100
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.7636 0.06285 0.516 0.056 0.288 0.052 0.088
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.3865 0.66986 0.848 0.024 0.048 0.060 0.020
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0960 0.74790 0.972 0.016 0.008 0.004 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.4213 1.00000 0.308 0.000 0.012 0.680 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.3117 0.45240 0.036 0.100 0.860 0.004 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.4213 1.00000 0.308 0.000 0.012 0.680 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.3317 0.85182 0.188 0.804 0.004 0.004 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 3 0.5730 0.25736 0.008 0.080 0.648 0.012 0.252
#> 53A96249-66D5-4C26-893B-ADC71481D261 1 0.4997 -0.00449 0.508 0.468 0.016 0.008 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0727 0.75156 0.980 0.012 0.004 0.004 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.6950 0.31783 0.044 0.620 0.196 0.048 0.092
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.3279 0.80696 0.116 0.852 0.012 0.016 0.004
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.6572 0.03308 0.028 0.036 0.640 0.104 0.192
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.2179 0.78204 0.896 0.100 0.004 0.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.2124 0.78242 0.900 0.096 0.004 0.000 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0727 0.75156 0.980 0.012 0.004 0.004 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.6184 0.52300 0.636 0.092 0.228 0.040 0.004
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.3163 0.73645 0.824 0.164 0.012 0.000 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.2249 0.78251 0.896 0.096 0.008 0.000 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.3242 0.73074 0.816 0.172 0.012 0.000 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0727 0.75156 0.980 0.012 0.004 0.004 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.4164 0.79679 0.252 0.728 0.012 0.008 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0880 0.76776 0.968 0.032 0.000 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.4213 1.00000 0.308 0.000 0.012 0.680 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 1 0.4904 0.48892 0.644 0.316 0.036 0.004 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.3177 0.84811 0.208 0.792 0.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.3774 0.73312 0.296 0.704 0.000 0.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.6975 -0.04351 0.056 0.040 0.528 0.040 0.336
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0880 0.76776 0.968 0.032 0.000 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.3279 0.80696 0.116 0.852 0.012 0.016 0.004
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0727 0.75156 0.980 0.012 0.004 0.004 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.3279 0.80696 0.116 0.852 0.012 0.016 0.004
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.2179 0.78204 0.896 0.100 0.004 0.000 0.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.4213 1.00000 0.308 0.000 0.012 0.680 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 5 0.4123 0.71335 0.000 0.004 0.132 0.072 0.792
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.6762 0.24952 0.040 0.576 0.256 0.008 0.120
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.2249 0.78251 0.896 0.096 0.008 0.000 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.2775 0.44089 0.020 0.100 0.876 0.004 0.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 1 0.4904 0.48892 0.644 0.316 0.036 0.004 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.7458 0.18870 0.100 0.080 0.544 0.024 0.252
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.5730 0.25736 0.008 0.080 0.648 0.012 0.252
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.7459 0.29896 0.316 0.056 0.504 0.092 0.032
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 1 0.4591 0.48420 0.648 0.332 0.012 0.008 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 1 0.4713 0.15407 0.544 0.440 0.016 0.000 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.4162 0.70336 0.312 0.680 0.004 0.004 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.1991 0.78282 0.916 0.076 0.004 0.004 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 1 0.4904 0.48892 0.644 0.316 0.036 0.004 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.151 0.7899 0.948 0.016 0.004 0.012 0.020 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 1 0.424 0.5269 0.704 0.244 0.048 0.000 0.004 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.363 0.8011 0.196 0.772 0.020 0.000 0.012 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.343 0.8014 0.220 0.764 0.012 0.000 0.004 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.115 0.7828 0.956 0.032 0.012 0.000 0.000 0.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.495 0.3428 0.240 0.108 0.648 0.000 0.004 0.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.495 0.3428 0.240 0.108 0.648 0.000 0.004 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 1 0.424 0.5269 0.704 0.244 0.048 0.000 0.004 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.603 0.0941 0.248 0.000 0.396 0.000 0.356 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.232 0.7670 0.896 0.000 0.004 0.036 0.064 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.618 0.0354 0.488 0.000 0.196 0.020 0.296 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 3 0.597 0.1767 0.380 0.172 0.440 0.000 0.008 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 1 0.623 0.3247 0.556 0.240 0.144 0.000 0.060 0.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.582 0.5095 0.640 0.004 0.172 0.028 0.144 0.012
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.603 0.0898 0.248 0.000 0.392 0.000 0.360 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.604 0.2416 0.144 0.040 0.652 0.000 0.116 0.048
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.358 0.5394 0.004 0.816 0.124 0.000 0.016 0.040
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.329 0.7726 0.856 0.040 0.008 0.036 0.060 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.339 0.7946 0.236 0.752 0.012 0.000 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.127 1.0000 0.060 0.000 0.000 0.940 0.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.366 0.7749 0.144 0.804 0.032 0.000 0.012 0.008
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.652 0.0879 0.212 0.004 0.432 0.000 0.328 0.024
#> F325847E-F046-4B67-B01C-16919C401020 3 0.209 0.3585 0.068 0.028 0.904 0.000 0.000 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.627 -0.0359 0.464 0.000 0.208 0.020 0.308 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.368 0.8001 0.192 0.772 0.012 0.000 0.024 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.127 1.0000 0.060 0.000 0.000 0.940 0.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.209 0.3585 0.068 0.028 0.904 0.000 0.000 0.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.209 0.7876 0.920 0.028 0.004 0.016 0.032 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 6 0.383 0.4228 0.000 0.020 0.004 0.024 0.172 0.780
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.232 0.7670 0.896 0.000 0.004 0.036 0.064 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 1 0.424 0.5269 0.704 0.244 0.048 0.000 0.004 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.547 0.2517 0.396 0.076 0.512 0.004 0.012 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.352 0.6215 0.056 0.820 0.000 0.016 0.108 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.232 0.7670 0.896 0.000 0.004 0.036 0.064 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.105 0.7870 0.960 0.032 0.000 0.008 0.000 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.258 0.7630 0.884 0.004 0.004 0.036 0.072 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.127 1.0000 0.060 0.000 0.000 0.940 0.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 5 0.631 0.0000 0.024 0.000 0.196 0.000 0.460 0.320
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.654 0.3080 0.180 0.052 0.600 0.000 0.052 0.116
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.513 0.6964 0.268 0.656 0.012 0.020 0.036 0.008
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.642 0.0710 0.212 0.000 0.416 0.000 0.348 0.024
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.127 1.0000 0.060 0.000 0.000 0.940 0.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.184 0.3471 0.052 0.028 0.920 0.000 0.000 0.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.127 1.0000 0.060 0.000 0.000 0.940 0.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.209 0.7876 0.920 0.028 0.004 0.016 0.032 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.617 0.1687 0.036 0.032 0.568 0.000 0.076 0.288
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 1 0.424 0.5269 0.704 0.244 0.048 0.000 0.004 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.642 0.0710 0.212 0.000 0.416 0.000 0.348 0.024
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.221 0.7444 0.892 0.092 0.012 0.000 0.004 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 1 0.339 0.6853 0.812 0.136 0.048 0.000 0.004 0.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.112 0.7815 0.956 0.036 0.008 0.000 0.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.199 0.7802 0.920 0.004 0.004 0.024 0.048 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.424 0.5269 0.704 0.244 0.048 0.000 0.004 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.105 0.7870 0.960 0.032 0.000 0.008 0.000 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.352 0.6215 0.056 0.820 0.000 0.016 0.108 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.258 0.7630 0.884 0.004 0.004 0.036 0.072 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.127 1.0000 0.060 0.000 0.000 0.940 0.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.443 0.6996 0.312 0.648 0.032 0.000 0.008 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.115 0.7828 0.956 0.032 0.012 0.000 0.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.105 0.7870 0.960 0.032 0.000 0.008 0.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.232 0.7670 0.896 0.000 0.004 0.036 0.064 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.690 0.0968 0.060 0.024 0.472 0.000 0.124 0.320
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.343 0.8014 0.220 0.764 0.012 0.000 0.004 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.246 0.7820 0.904 0.012 0.008 0.024 0.048 0.004
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.115 0.7828 0.956 0.032 0.012 0.000 0.000 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.334 0.7986 0.228 0.760 0.012 0.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.105 0.7870 0.960 0.032 0.000 0.008 0.000 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.542 0.4570 0.628 0.000 0.004 0.032 0.260 0.076
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.127 1.0000 0.060 0.000 0.000 0.940 0.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.371 0.7986 0.188 0.776 0.016 0.000 0.004 0.016
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.388 0.6847 0.332 0.656 0.012 0.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.245 0.7683 0.892 0.004 0.004 0.032 0.068 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.418 0.6785 0.768 0.000 0.032 0.028 0.164 0.008
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.664 -0.0857 0.000 0.028 0.464 0.020 0.156 0.332
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.564 0.1752 0.016 0.036 0.604 0.004 0.044 0.296
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.127 1.0000 0.060 0.000 0.000 0.940 0.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.112 0.7815 0.956 0.036 0.008 0.000 0.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.363 0.8006 0.196 0.772 0.020 0.000 0.012 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.628 0.3712 0.600 0.000 0.140 0.024 0.192 0.044
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.668 0.0655 0.508 0.008 0.264 0.012 0.180 0.028
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.427 0.6746 0.764 0.000 0.032 0.028 0.164 0.012
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.258 0.7630 0.884 0.004 0.004 0.036 0.072 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.127 1.0000 0.060 0.000 0.000 0.940 0.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.209 0.3585 0.068 0.028 0.904 0.000 0.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.127 1.0000 0.060 0.000 0.000 0.940 0.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.372 0.8000 0.208 0.760 0.020 0.000 0.012 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 3 0.564 0.1752 0.016 0.036 0.604 0.004 0.044 0.296
#> 53A96249-66D5-4C26-893B-ADC71481D261 1 0.470 0.0568 0.552 0.408 0.032 0.000 0.008 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.232 0.7670 0.896 0.000 0.004 0.036 0.064 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.584 0.2755 0.004 0.652 0.196 0.020 0.048 0.080
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.352 0.6215 0.056 0.820 0.000 0.016 0.108 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.558 -0.3385 0.000 0.008 0.456 0.000 0.428 0.108
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.112 0.7815 0.956 0.036 0.008 0.000 0.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.105 0.7825 0.960 0.032 0.008 0.000 0.000 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.232 0.7670 0.896 0.000 0.004 0.036 0.064 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.516 0.5407 0.692 0.032 0.192 0.000 0.072 0.012
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.242 0.7412 0.880 0.100 0.012 0.000 0.008 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.115 0.7828 0.956 0.032 0.012 0.000 0.000 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.252 0.7361 0.872 0.108 0.012 0.000 0.008 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.232 0.7670 0.896 0.000 0.004 0.036 0.064 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.429 0.7269 0.296 0.668 0.028 0.000 0.008 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.199 0.7802 0.920 0.004 0.004 0.024 0.048 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.127 1.0000 0.060 0.000 0.000 0.940 0.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 1 0.424 0.5269 0.704 0.244 0.048 0.000 0.004 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.345 0.8005 0.224 0.760 0.012 0.000 0.004 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.404 0.7132 0.312 0.668 0.012 0.000 0.008 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.714 -0.3210 0.032 0.016 0.420 0.008 0.284 0.240
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.199 0.7802 0.920 0.004 0.004 0.024 0.048 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.352 0.6215 0.056 0.820 0.000 0.016 0.108 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.232 0.7670 0.896 0.000 0.004 0.036 0.064 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.352 0.6215 0.056 0.820 0.000 0.016 0.108 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.112 0.7815 0.956 0.036 0.008 0.000 0.000 0.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.127 1.0000 0.060 0.000 0.000 0.940 0.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 6 0.527 0.1653 0.000 0.008 0.068 0.004 0.380 0.540
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.701 0.2597 0.064 0.508 0.284 0.000 0.068 0.076
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.115 0.7828 0.956 0.032 0.012 0.000 0.000 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.184 0.3471 0.052 0.028 0.920 0.000 0.000 0.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 1 0.424 0.5269 0.704 0.244 0.048 0.000 0.004 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.693 -0.0285 0.124 0.008 0.524 0.000 0.184 0.160
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.564 0.1752 0.016 0.036 0.604 0.004 0.044 0.296
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.603 0.0941 0.248 0.000 0.396 0.000 0.356 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 1 0.426 0.5169 0.692 0.272 0.016 0.004 0.016 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 1 0.455 0.2215 0.596 0.368 0.028 0.000 0.008 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.391 0.6690 0.340 0.648 0.012 0.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.105 0.7870 0.960 0.032 0.000 0.008 0.000 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 1 0.424 0.5269 0.704 0.244 0.048 0.000 0.004 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "kmeans"]
# you can also extract it by
# res = res_list["CV:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.325 0.755 0.835 0.4493 0.552 0.552
#> 3 3 0.537 0.807 0.818 0.3866 0.724 0.527
#> 4 4 0.568 0.712 0.813 0.1424 0.905 0.734
#> 5 5 0.652 0.660 0.742 0.0762 0.869 0.579
#> 6 6 0.683 0.549 0.757 0.0490 0.960 0.826
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 2 0.7883 0.772 0.236 0.764
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.4161 0.799 0.084 0.916
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.2778 0.790 0.048 0.952
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.2778 0.790 0.048 0.952
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 2 0.3274 0.808 0.060 0.940
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.5946 0.814 0.856 0.144
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.8499 0.672 0.724 0.276
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.4161 0.799 0.084 0.916
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.4161 0.847 0.916 0.084
#> 5482053D-9F48-4773-B68A-302B3A612503 2 0.8267 0.740 0.260 0.740
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.7815 0.676 0.768 0.232
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.3584 0.788 0.068 0.932
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 1 0.9427 0.556 0.640 0.360
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.9209 0.445 0.664 0.336
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.3274 0.855 0.940 0.060
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 1 0.7883 0.697 0.764 0.236
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.9922 -0.169 0.448 0.552
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 2 0.4815 0.805 0.104 0.896
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.1843 0.795 0.028 0.972
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 2 0.9552 0.614 0.376 0.624
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.2778 0.790 0.048 0.952
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 1 0.3274 0.855 0.940 0.060
#> F325847E-F046-4B67-B01C-16919C401020 1 0.6343 0.784 0.840 0.160
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.3274 0.855 0.940 0.060
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.2778 0.790 0.048 0.952
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 2 0.9954 0.422 0.460 0.540
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 1 0.6801 0.788 0.820 0.180
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 2 0.8016 0.767 0.244 0.756
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.1843 0.868 0.972 0.028
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 2 0.7674 0.770 0.224 0.776
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.3879 0.802 0.076 0.924
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.4562 0.844 0.904 0.096
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.3733 0.780 0.072 0.928
#> 91BA5F90-9174-4533-A050-39A28E34A94D 2 0.6887 0.790 0.184 0.816
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 2 0.7883 0.772 0.236 0.764
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 2 0.8267 0.743 0.260 0.740
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 2 0.9358 0.654 0.352 0.648
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.1843 0.868 0.972 0.028
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 1 0.2423 0.865 0.960 0.040
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.2778 0.790 0.048 0.952
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.1843 0.868 0.972 0.028
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 2 0.9427 0.641 0.360 0.640
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 1 0.4431 0.836 0.908 0.092
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.5294 0.793 0.880 0.120
#> CB207A52-09AC-49D3-8240-5840CDFBB154 2 0.7219 0.787 0.200 0.800
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.1843 0.868 0.972 0.028
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.3879 0.802 0.076 0.924
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.1843 0.868 0.972 0.028
#> 5E343116-414B-41F2-AAEE-A3225450135A 2 0.2778 0.808 0.048 0.952
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.4298 0.797 0.088 0.912
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 2 0.2948 0.809 0.052 0.948
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 2 0.6887 0.790 0.184 0.816
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 2 0.2948 0.806 0.052 0.948
#> AD294665-6F90-459C-90D5-3058F210225D 2 0.7883 0.772 0.236 0.764
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.2778 0.790 0.048 0.952
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 2 0.8327 0.739 0.264 0.736
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.8861 0.470 0.696 0.304
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.3431 0.786 0.064 0.936
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 2 0.9933 0.381 0.452 0.548
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 2 0.7883 0.772 0.236 0.764
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 2 0.6887 0.790 0.184 0.816
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.1843 0.868 0.972 0.028
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.2778 0.790 0.048 0.952
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 2 0.7674 0.770 0.224 0.776
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 2 0.5946 0.804 0.144 0.856
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.2778 0.790 0.048 0.952
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 2 0.7883 0.772 0.236 0.764
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.4298 0.838 0.912 0.088
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 2 0.9393 0.648 0.356 0.644
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.2778 0.790 0.048 0.952
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.1843 0.795 0.028 0.972
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 2 0.8327 0.739 0.264 0.736
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 2 0.8327 0.736 0.264 0.736
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.1843 0.868 0.972 0.028
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.1843 0.868 0.972 0.028
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.5294 0.793 0.880 0.120
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 2 0.8499 0.737 0.276 0.724
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.2778 0.790 0.048 0.952
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.3431 0.852 0.936 0.064
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.2778 0.861 0.952 0.048
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.9522 0.392 0.628 0.372
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 2 0.8327 0.739 0.264 0.736
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.8443 0.750 0.272 0.728
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.3879 0.849 0.924 0.076
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 2 0.8207 0.762 0.256 0.744
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.2778 0.790 0.048 0.952
#> 322AF320-1379-4F51-AFDC-5292A060CD52 1 0.6973 0.744 0.812 0.188
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.3431 0.786 0.064 0.936
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 2 0.6887 0.790 0.184 0.816
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 1 0.9850 0.480 0.572 0.428
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.2778 0.790 0.048 0.952
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.2423 0.864 0.960 0.040
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 2 0.6973 0.792 0.188 0.812
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 2 0.8443 0.742 0.272 0.728
#> C2662596-6E2F-4924-B051-CEA1AC87B197 2 0.8267 0.740 0.260 0.740
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 2 0.9922 0.399 0.448 0.552
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.2236 0.807 0.036 0.964
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 2 0.7299 0.788 0.204 0.796
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 2 0.2236 0.807 0.036 0.964
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 2 0.6973 0.788 0.188 0.812
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.6343 0.688 0.160 0.840
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 2 0.7056 0.787 0.192 0.808
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 2 0.9427 0.641 0.360 0.640
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.4161 0.799 0.084 0.916
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.2778 0.790 0.048 0.952
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.0000 0.798 0.000 1.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 1 0.2603 0.862 0.956 0.044
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 2 0.8267 0.743 0.260 0.740
#> A60DC925-7343-496E-900D-0DD81D5C8123 1 0.9209 0.530 0.664 0.336
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 2 0.8267 0.740 0.260 0.740
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.2778 0.790 0.048 0.952
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 2 0.2778 0.808 0.048 0.952
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 2 0.9393 0.648 0.356 0.644
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.1843 0.868 0.972 0.028
#> D47D0433-2313-4A2F-B268-5AD293D7534E 1 0.9635 0.528 0.612 0.388
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 2 0.4022 0.807 0.080 0.920
#> 2629FEE3-A203-4411-8A70-02A796C9505C 1 0.4431 0.836 0.908 0.092
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.4161 0.799 0.084 0.916
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.1843 0.868 0.972 0.028
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.1843 0.868 0.972 0.028
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.1843 0.868 0.972 0.028
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0672 0.800 0.008 0.992
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.3431 0.786 0.064 0.936
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.2778 0.790 0.048 0.952
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 2 0.7883 0.772 0.236 0.764
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.4022 0.800 0.080 0.920
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.371 0.8207 0.892 0.032 0.076
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.677 0.8479 0.304 0.664 0.032
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.531 0.9158 0.216 0.772 0.012
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.522 0.9180 0.208 0.780 0.012
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.452 0.7556 0.852 0.116 0.032
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.448 0.8482 0.072 0.064 0.864
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.453 0.8444 0.052 0.088 0.860
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.654 0.8546 0.304 0.672 0.024
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.220 0.8903 0.056 0.004 0.940
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.325 0.8304 0.912 0.052 0.036
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.631 0.0599 0.504 0.000 0.496
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.638 0.8953 0.244 0.720 0.036
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.456 0.8355 0.064 0.076 0.860
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.541 0.7457 0.796 0.032 0.172
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.165 0.8991 0.036 0.004 0.960
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.188 0.8954 0.004 0.044 0.952
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.547 0.8985 0.176 0.792 0.032
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.333 0.8238 0.904 0.076 0.020
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.480 0.9128 0.220 0.780 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.663 0.6650 0.728 0.212 0.060
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.512 0.9160 0.200 0.788 0.012
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.140 0.9031 0.004 0.028 0.968
#> F325847E-F046-4B67-B01C-16919C401020 3 0.504 0.8108 0.120 0.048 0.832
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.141 0.8986 0.036 0.000 0.964
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.522 0.9180 0.208 0.780 0.012
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.672 0.6584 0.720 0.220 0.060
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.498 0.8197 0.096 0.064 0.840
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.371 0.8207 0.892 0.032 0.076
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.164 0.9080 0.016 0.020 0.964
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.313 0.8304 0.916 0.052 0.032
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.698 0.8081 0.336 0.632 0.032
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.378 0.8725 0.064 0.044 0.892
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.527 0.9144 0.200 0.784 0.016
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.313 0.8304 0.916 0.052 0.032
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.390 0.8196 0.888 0.060 0.052
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.325 0.8304 0.912 0.052 0.036
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.663 0.6650 0.728 0.212 0.060
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.134 0.9079 0.016 0.012 0.972
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.175 0.9061 0.012 0.028 0.960
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.522 0.9180 0.208 0.780 0.012
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.134 0.9079 0.016 0.012 0.972
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.663 0.6650 0.728 0.212 0.060
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.199 0.8985 0.004 0.048 0.948
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 3 0.967 0.1236 0.376 0.212 0.412
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.315 0.8215 0.916 0.048 0.036
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.164 0.9080 0.016 0.020 0.964
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.698 0.8081 0.336 0.632 0.032
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.134 0.9079 0.016 0.012 0.972
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.372 0.7955 0.888 0.088 0.024
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.704 0.8212 0.328 0.636 0.036
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.328 0.8086 0.908 0.068 0.024
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.328 0.8267 0.908 0.068 0.024
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.718 -0.1715 0.564 0.408 0.028
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.390 0.8196 0.888 0.060 0.052
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.522 0.9180 0.208 0.780 0.012
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.325 0.8304 0.912 0.052 0.036
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.681 0.6541 0.720 0.212 0.068
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.623 0.8942 0.252 0.720 0.028
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.427 0.8126 0.872 0.052 0.076
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.390 0.8196 0.888 0.060 0.052
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.313 0.8304 0.916 0.052 0.032
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.164 0.9080 0.016 0.020 0.964
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.522 0.9180 0.208 0.780 0.012
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.313 0.8304 0.916 0.052 0.032
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.460 0.7663 0.852 0.108 0.040
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.522 0.9180 0.208 0.780 0.012
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.390 0.8196 0.888 0.060 0.052
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.626 0.0864 0.448 0.000 0.552
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.663 0.6650 0.728 0.212 0.060
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.517 0.9163 0.204 0.784 0.012
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.480 0.9128 0.220 0.780 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.325 0.8304 0.912 0.052 0.036
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.325 0.8304 0.912 0.052 0.036
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.164 0.9080 0.016 0.020 0.964
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.164 0.9080 0.016 0.020 0.964
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 3 0.964 0.1841 0.356 0.212 0.432
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.359 0.8222 0.900 0.048 0.052
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.517 0.9175 0.204 0.784 0.012
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.165 0.8991 0.036 0.004 0.960
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.153 0.9016 0.032 0.004 0.964
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.729 0.4263 0.356 0.040 0.604
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.325 0.8304 0.912 0.052 0.036
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.645 0.6697 0.736 0.212 0.052
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.260 0.8949 0.052 0.016 0.932
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.635 0.6706 0.740 0.212 0.048
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.517 0.9175 0.204 0.784 0.012
#> 322AF320-1379-4F51-AFDC-5292A060CD52 3 0.230 0.8856 0.004 0.060 0.936
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.598 0.9059 0.228 0.744 0.028
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.313 0.8304 0.916 0.052 0.032
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.616 0.7932 0.092 0.780 0.128
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.522 0.9180 0.208 0.780 0.012
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.117 0.9071 0.016 0.008 0.976
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.326 0.8200 0.912 0.048 0.040
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.380 0.8166 0.892 0.052 0.056
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.325 0.8304 0.912 0.052 0.036
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.466 0.8052 0.852 0.048 0.100
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.429 0.7673 0.840 0.152 0.008
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.359 0.8169 0.900 0.052 0.048
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.377 0.8008 0.876 0.112 0.012
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.313 0.8304 0.916 0.052 0.032
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.601 0.9076 0.220 0.748 0.032
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.313 0.8304 0.916 0.052 0.032
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.663 0.6650 0.728 0.212 0.060
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.677 0.8479 0.304 0.664 0.032
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.522 0.9180 0.208 0.780 0.012
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.520 0.8995 0.236 0.760 0.004
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.153 0.9026 0.004 0.032 0.964
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.324 0.8305 0.912 0.056 0.032
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.649 0.7864 0.096 0.760 0.144
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.325 0.8304 0.912 0.052 0.036
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.522 0.9180 0.208 0.780 0.012
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.412 0.7692 0.868 0.108 0.024
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.663 0.6650 0.728 0.212 0.060
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.149 0.9081 0.016 0.016 0.968
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.628 0.6418 0.040 0.736 0.224
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.466 0.7458 0.844 0.124 0.032
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.199 0.8985 0.004 0.048 0.948
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.651 0.8583 0.300 0.676 0.024
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.134 0.9079 0.016 0.012 0.972
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.164 0.9080 0.016 0.020 0.964
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.149 0.9076 0.016 0.016 0.968
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.617 0.7587 0.360 0.636 0.004
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.614 0.8934 0.256 0.720 0.024
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.522 0.9180 0.208 0.780 0.012
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.390 0.8196 0.888 0.060 0.052
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.698 0.8081 0.336 0.632 0.032
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.2715 0.7149 0.916 0.036 0.032 0.016
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.7186 0.4854 0.356 0.520 0.008 0.116
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.3877 0.7829 0.124 0.840 0.004 0.032
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.2334 0.8056 0.088 0.908 0.000 0.004
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.4356 0.6881 0.828 0.092 0.008 0.072
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.6314 0.6606 0.204 0.044 0.696 0.056
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.7038 0.6774 0.160 0.076 0.672 0.092
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.7186 0.4854 0.356 0.520 0.008 0.116
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.1229 0.8509 0.020 0.004 0.968 0.008
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.5231 0.6437 0.764 0.052 0.016 0.168
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.6229 0.0567 0.440 0.004 0.512 0.044
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.5912 0.6644 0.276 0.664 0.008 0.052
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.5371 0.7418 0.108 0.064 0.784 0.044
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.5390 0.6309 0.760 0.040 0.032 0.168
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0804 0.8518 0.008 0.000 0.980 0.012
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.3076 0.8397 0.016 0.036 0.900 0.048
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.1911 0.7796 0.032 0.944 0.004 0.020
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.3962 0.6812 0.844 0.052 0.004 0.100
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.2401 0.8047 0.092 0.904 0.000 0.004
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.4134 0.9411 0.188 0.008 0.008 0.796
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.1635 0.7931 0.044 0.948 0.000 0.008
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.0927 0.8536 0.008 0.000 0.976 0.016
#> F325847E-F046-4B67-B01C-16919C401020 3 0.6246 0.5936 0.256 0.044 0.668 0.032
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0779 0.8516 0.004 0.000 0.980 0.016
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.2949 0.8018 0.088 0.888 0.000 0.024
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.3823 0.9219 0.160 0.008 0.008 0.824
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.5063 0.7342 0.140 0.040 0.788 0.032
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.3065 0.7128 0.900 0.032 0.052 0.016
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.3215 0.8405 0.000 0.032 0.876 0.092
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.5072 0.6498 0.772 0.052 0.012 0.164
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.7279 0.3808 0.404 0.472 0.008 0.116
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.5382 0.7023 0.184 0.032 0.752 0.032
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.2224 0.7762 0.040 0.928 0.000 0.032
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.4837 0.6618 0.792 0.052 0.012 0.144
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.4419 0.6941 0.828 0.072 0.012 0.088
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.5231 0.6437 0.764 0.052 0.016 0.168
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.4134 0.9411 0.188 0.008 0.008 0.796
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.1004 0.8531 0.000 0.004 0.972 0.024
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.3818 0.8377 0.004 0.048 0.852 0.096
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.2714 0.7876 0.112 0.884 0.000 0.004
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0469 0.8529 0.000 0.000 0.988 0.012
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.4134 0.9411 0.188 0.008 0.008 0.796
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.3081 0.8488 0.016 0.040 0.900 0.044
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.3338 0.8138 0.052 0.008 0.056 0.884
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.3127 0.7152 0.892 0.032 0.008 0.068
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.3399 0.8379 0.000 0.040 0.868 0.092
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.7279 0.3808 0.404 0.472 0.008 0.116
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0707 0.8521 0.000 0.000 0.980 0.020
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.3862 0.7038 0.852 0.084 0.004 0.060
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 1 0.7085 -0.2927 0.468 0.428 0.008 0.096
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.3658 0.7068 0.864 0.068 0.004 0.064
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.4141 0.6764 0.832 0.052 0.004 0.112
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.7081 0.1027 0.552 0.324 0.008 0.116
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.4489 0.6924 0.824 0.076 0.012 0.088
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.2773 0.7991 0.072 0.900 0.000 0.028
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.5231 0.6437 0.764 0.052 0.016 0.168
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.3523 0.9029 0.136 0.008 0.008 0.848
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.5466 0.7182 0.200 0.732 0.008 0.060
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.4071 0.7007 0.852 0.052 0.020 0.076
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.4419 0.6941 0.828 0.072 0.012 0.088
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.5072 0.6517 0.772 0.052 0.012 0.164
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.3550 0.8361 0.000 0.044 0.860 0.096
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.2334 0.8056 0.088 0.908 0.000 0.004
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.5187 0.6469 0.768 0.052 0.016 0.164
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.4226 0.6934 0.836 0.084 0.008 0.072
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.2334 0.8056 0.088 0.908 0.000 0.004
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.4489 0.6924 0.824 0.076 0.012 0.088
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.6138 0.3625 0.332 0.008 0.612 0.048
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.4134 0.9411 0.188 0.008 0.008 0.796
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.2342 0.8042 0.080 0.912 0.000 0.008
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.2401 0.8047 0.092 0.904 0.000 0.004
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.5231 0.6437 0.764 0.052 0.016 0.168
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.5231 0.6437 0.764 0.052 0.016 0.168
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.3399 0.8379 0.000 0.040 0.868 0.092
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.3308 0.8391 0.000 0.036 0.872 0.092
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.3325 0.8034 0.044 0.008 0.064 0.884
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.3089 0.7137 0.896 0.044 0.008 0.052
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.2197 0.8053 0.080 0.916 0.000 0.004
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0804 0.8518 0.008 0.000 0.980 0.012
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0992 0.8512 0.012 0.004 0.976 0.008
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.7110 0.4005 0.284 0.036 0.600 0.080
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.5231 0.6437 0.764 0.052 0.016 0.168
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.4134 0.9411 0.188 0.008 0.008 0.796
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.2101 0.8338 0.060 0.012 0.928 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.4134 0.9411 0.188 0.008 0.008 0.796
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.2197 0.8053 0.080 0.916 0.000 0.004
#> 322AF320-1379-4F51-AFDC-5292A060CD52 3 0.3996 0.8306 0.000 0.060 0.836 0.104
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.5673 0.6910 0.252 0.692 0.008 0.048
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.5116 0.6491 0.768 0.052 0.012 0.168
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.2807 0.7547 0.024 0.912 0.020 0.044
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.2385 0.7871 0.052 0.920 0.000 0.028
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.1637 0.8504 0.000 0.000 0.940 0.060
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.3272 0.7133 0.884 0.052 0.004 0.060
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.3529 0.7104 0.876 0.044 0.012 0.068
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.5231 0.6437 0.764 0.052 0.016 0.168
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.5870 0.6392 0.752 0.040 0.100 0.108
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.2773 0.7081 0.880 0.116 0.000 0.004
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.3876 0.7036 0.856 0.068 0.008 0.068
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.2271 0.7165 0.916 0.076 0.000 0.008
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.5072 0.6498 0.772 0.052 0.012 0.164
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.4867 0.7496 0.164 0.780 0.008 0.048
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.5231 0.6437 0.764 0.052 0.016 0.168
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.4134 0.9411 0.188 0.008 0.008 0.796
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.7186 0.4854 0.356 0.520 0.008 0.116
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.2334 0.8056 0.088 0.908 0.000 0.004
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.4936 0.3475 0.372 0.624 0.000 0.004
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.3550 0.8361 0.000 0.044 0.860 0.096
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.5231 0.6437 0.764 0.052 0.016 0.168
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.3881 0.7180 0.028 0.860 0.028 0.084
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.5231 0.6437 0.764 0.052 0.016 0.168
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.2773 0.7991 0.072 0.900 0.000 0.028
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.4213 0.6912 0.832 0.092 0.004 0.072
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.4134 0.9411 0.188 0.008 0.008 0.796
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.2542 0.8452 0.000 0.012 0.904 0.084
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.3134 0.7015 0.004 0.884 0.088 0.024
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.4292 0.6911 0.832 0.088 0.008 0.072
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.3081 0.8488 0.016 0.040 0.900 0.044
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.7186 0.4854 0.356 0.520 0.008 0.116
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.0937 0.8526 0.000 0.012 0.976 0.012
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.3463 0.8375 0.000 0.040 0.864 0.096
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0804 0.8515 0.012 0.000 0.980 0.008
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 1 0.5039 0.1002 0.592 0.404 0.000 0.004
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.5922 0.6714 0.256 0.676 0.008 0.060
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.2334 0.8056 0.088 0.908 0.000 0.004
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.4419 0.6941 0.828 0.072 0.012 0.088
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.7279 0.3808 0.404 0.472 0.008 0.116
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.5233 -0.3415 0.512 0.016 0.004 0.012 0.456
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 5 0.5667 0.4575 0.060 0.292 0.008 0.012 0.628
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.4479 0.7600 0.080 0.780 0.000 0.016 0.124
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.2069 0.8763 0.076 0.912 0.000 0.000 0.012
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.5935 0.6040 0.340 0.060 0.000 0.028 0.572
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 5 0.5675 -0.2840 0.016 0.012 0.436 0.024 0.512
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 5 0.5977 -0.3745 0.000 0.036 0.460 0.040 0.464
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 5 0.5667 0.4575 0.060 0.292 0.008 0.012 0.628
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.2358 0.7588 0.000 0.000 0.888 0.008 0.104
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0486 0.8533 0.988 0.004 0.004 0.004 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.6027 0.1330 0.440 0.000 0.464 0.008 0.088
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 5 0.5499 0.2261 0.028 0.396 0.008 0.012 0.556
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.5838 0.4449 0.012 0.028 0.548 0.024 0.388
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.1362 0.8279 0.960 0.016 0.004 0.012 0.008
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.2358 0.7588 0.000 0.000 0.888 0.008 0.104
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.4338 0.7391 0.000 0.028 0.764 0.020 0.188
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.1574 0.8391 0.012 0.952 0.004 0.020 0.012
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.1202 0.8255 0.960 0.004 0.004 0.000 0.032
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.2069 0.8763 0.076 0.912 0.000 0.000 0.012
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.4028 0.9622 0.176 0.000 0.000 0.776 0.048
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.2053 0.8646 0.040 0.928 0.000 0.016 0.016
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.1725 0.7732 0.000 0.000 0.936 0.020 0.044
#> F325847E-F046-4B67-B01C-16919C401020 3 0.5348 0.3687 0.012 0.008 0.500 0.016 0.464
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.2448 0.7616 0.000 0.000 0.892 0.020 0.088
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.3011 0.8689 0.076 0.876 0.000 0.012 0.036
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.3914 0.9559 0.164 0.000 0.000 0.788 0.048
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.4994 0.4965 0.000 0.012 0.576 0.016 0.396
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.5667 -0.3119 0.520 0.024 0.012 0.016 0.428
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.4871 0.7482 0.000 0.056 0.764 0.128 0.052
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0162 0.8534 0.996 0.000 0.004 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 5 0.5789 0.4897 0.076 0.272 0.008 0.012 0.632
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.5023 0.4097 0.004 0.004 0.520 0.016 0.456
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.2927 0.8491 0.048 0.888 0.000 0.036 0.028
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0613 0.8500 0.984 0.004 0.004 0.000 0.008
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.5985 0.5705 0.344 0.044 0.000 0.044 0.568
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0775 0.8530 0.980 0.004 0.004 0.004 0.008
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.3885 0.9626 0.176 0.000 0.000 0.784 0.040
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.1872 0.7756 0.000 0.000 0.928 0.052 0.020
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.5522 0.7398 0.000 0.068 0.720 0.132 0.080
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.2804 0.8667 0.092 0.880 0.000 0.016 0.012
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.1582 0.7748 0.000 0.000 0.944 0.028 0.028
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.3885 0.9626 0.176 0.000 0.000 0.784 0.040
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.5107 0.7513 0.000 0.048 0.748 0.076 0.128
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.2871 0.8776 0.088 0.000 0.004 0.876 0.032
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.5740 0.5382 0.412 0.036 0.000 0.028 0.524
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.4935 0.7467 0.000 0.060 0.760 0.128 0.052
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 5 0.5789 0.4897 0.076 0.272 0.008 0.012 0.632
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.1750 0.7727 0.000 0.000 0.936 0.028 0.036
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.5615 0.5752 0.388 0.052 0.000 0.012 0.548
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.5679 0.5952 0.136 0.200 0.004 0.004 0.656
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.5870 0.5789 0.380 0.048 0.000 0.028 0.544
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0955 0.8377 0.968 0.000 0.004 0.000 0.028
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 5 0.5636 0.5896 0.132 0.148 0.008 0.016 0.696
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.6035 0.5715 0.340 0.048 0.000 0.044 0.568
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.3411 0.8633 0.072 0.860 0.000 0.032 0.036
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0775 0.8530 0.980 0.004 0.004 0.004 0.008
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.3551 0.9342 0.136 0.000 0.000 0.820 0.044
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.5967 0.1595 0.036 0.508 0.004 0.032 0.420
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.5983 0.5849 0.372 0.052 0.000 0.032 0.544
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.5985 0.5705 0.344 0.044 0.000 0.044 0.568
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0324 0.8540 0.992 0.004 0.004 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.4935 0.7472 0.000 0.060 0.760 0.128 0.052
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.2069 0.8763 0.076 0.912 0.000 0.000 0.012
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0451 0.8493 0.988 0.000 0.004 0.000 0.008
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.5891 0.6028 0.344 0.056 0.000 0.028 0.572
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.2069 0.8763 0.076 0.912 0.000 0.000 0.012
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.6035 0.5715 0.340 0.048 0.000 0.044 0.568
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.6249 0.3259 0.368 0.000 0.520 0.020 0.092
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.3885 0.9626 0.176 0.000 0.000 0.784 0.040
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.2069 0.8763 0.076 0.912 0.000 0.000 0.012
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.2069 0.8763 0.076 0.912 0.000 0.000 0.012
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0613 0.8533 0.984 0.000 0.004 0.004 0.008
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0566 0.8478 0.984 0.000 0.012 0.004 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.4997 0.7456 0.000 0.064 0.756 0.128 0.052
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.4930 0.7467 0.000 0.064 0.760 0.128 0.048
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.3065 0.8551 0.080 0.004 0.012 0.876 0.028
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.5452 0.5271 0.416 0.020 0.000 0.028 0.536
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.2555 0.8661 0.052 0.904 0.000 0.016 0.028
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.2777 0.7551 0.000 0.000 0.864 0.016 0.120
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.2563 0.7558 0.000 0.000 0.872 0.008 0.120
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.5675 0.2005 0.468 0.000 0.472 0.016 0.044
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0775 0.8530 0.980 0.004 0.004 0.004 0.008
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.3958 0.9624 0.176 0.000 0.000 0.780 0.044
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.3759 0.7072 0.000 0.000 0.764 0.016 0.220
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.3995 0.9576 0.180 0.000 0.000 0.776 0.044
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.2555 0.8661 0.052 0.904 0.000 0.016 0.028
#> 322AF320-1379-4F51-AFDC-5292A060CD52 3 0.5724 0.7302 0.000 0.072 0.700 0.152 0.076
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.6155 0.0404 0.040 0.484 0.008 0.032 0.436
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0324 0.8540 0.992 0.004 0.004 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.2142 0.8270 0.012 0.928 0.004 0.028 0.028
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.3138 0.8617 0.060 0.876 0.000 0.032 0.032
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.2331 0.7739 0.000 0.000 0.900 0.080 0.020
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.5511 0.5491 0.404 0.024 0.000 0.028 0.544
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.6148 0.5465 0.400 0.036 0.012 0.032 0.520
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0324 0.8537 0.992 0.000 0.004 0.004 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 5 0.7421 0.4304 0.364 0.028 0.108 0.040 0.460
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.5246 -0.2129 0.564 0.052 0.000 0.000 0.384
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.5911 0.5855 0.372 0.052 0.000 0.028 0.548
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 5 0.5297 0.4394 0.476 0.048 0.000 0.000 0.476
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0324 0.8540 0.992 0.004 0.004 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.5442 0.4586 0.024 0.616 0.004 0.028 0.328
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0775 0.8530 0.980 0.004 0.004 0.004 0.008
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.3958 0.9624 0.176 0.000 0.000 0.780 0.044
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 5 0.5667 0.4575 0.060 0.292 0.008 0.012 0.628
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.2069 0.8763 0.076 0.912 0.000 0.000 0.012
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.3916 0.6441 0.256 0.732 0.000 0.000 0.012
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.4890 0.7479 0.000 0.060 0.764 0.124 0.052
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0613 0.8533 0.984 0.000 0.004 0.004 0.008
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.4295 0.7649 0.028 0.828 0.036 0.056 0.052
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0486 0.8533 0.988 0.004 0.004 0.004 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.3426 0.8637 0.068 0.860 0.000 0.032 0.040
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.5880 0.5947 0.360 0.052 0.000 0.028 0.560
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.3958 0.9624 0.176 0.000 0.000 0.780 0.044
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.4206 0.7544 0.000 0.024 0.800 0.128 0.048
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.2330 0.8121 0.004 0.920 0.036 0.020 0.020
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.5935 0.6040 0.340 0.060 0.000 0.028 0.572
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.5107 0.7513 0.000 0.048 0.748 0.076 0.128
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 5 0.5667 0.4575 0.060 0.292 0.008 0.012 0.628
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.2353 0.7786 0.000 0.004 0.908 0.028 0.060
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.5063 0.7453 0.000 0.064 0.752 0.128 0.056
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.2233 0.7599 0.000 0.000 0.892 0.004 0.104
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 1 0.6754 -0.4198 0.380 0.264 0.000 0.000 0.356
#> A608BCEB-2C27-4927-A308-E6975F641722 5 0.5519 0.2348 0.036 0.392 0.004 0.012 0.556
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.2228 0.8758 0.076 0.908 0.000 0.004 0.012
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.5985 0.5705 0.344 0.044 0.000 0.044 0.568
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 5 0.5789 0.4897 0.076 0.272 0.008 0.012 0.632
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 5 0.5161 0.52539 0.400 0.004 0.016 0.008 0.544 0.028
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 5 0.6488 0.46024 0.040 0.196 0.000 0.020 0.560 0.184
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.3282 0.77245 0.036 0.844 0.000 0.004 0.096 0.020
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0993 0.87357 0.024 0.964 0.000 0.000 0.012 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.4073 0.67916 0.236 0.012 0.000 0.016 0.728 0.008
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 5 0.6770 -0.21715 0.008 0.004 0.336 0.024 0.412 0.216
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 5 0.7085 -0.35041 0.000 0.020 0.304 0.028 0.344 0.304
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 5 0.6488 0.46024 0.040 0.196 0.000 0.020 0.560 0.184
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0363 0.33757 0.000 0.000 0.988 0.000 0.000 0.012
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.95890 1.000 0.000 0.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.3916 0.17547 0.300 0.000 0.680 0.000 0.000 0.020
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 5 0.6001 0.23147 0.004 0.324 0.000 0.016 0.512 0.144
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.6637 0.10168 0.008 0.028 0.528 0.016 0.252 0.168
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0405 0.95644 0.988 0.004 0.000 0.000 0.000 0.008
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0146 0.33688 0.000 0.000 0.996 0.000 0.000 0.004
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.5849 -0.00964 0.000 0.008 0.572 0.028 0.100 0.292
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0692 0.86208 0.000 0.976 0.000 0.000 0.004 0.020
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0547 0.94004 0.980 0.000 0.000 0.000 0.020 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.1151 0.87128 0.032 0.956 0.000 0.000 0.012 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.2294 0.97357 0.076 0.000 0.000 0.896 0.020 0.008
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0767 0.87062 0.012 0.976 0.000 0.000 0.004 0.008
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.1858 0.27371 0.000 0.000 0.904 0.004 0.000 0.092
#> F325847E-F046-4B67-B01C-16919C401020 3 0.6354 0.10981 0.008 0.000 0.444 0.020 0.368 0.160
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0260 0.33582 0.000 0.000 0.992 0.000 0.000 0.008
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.2698 0.85304 0.024 0.892 0.000 0.012 0.032 0.040
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.2782 0.96196 0.068 0.000 0.000 0.876 0.024 0.032
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.6009 0.12699 0.000 0.000 0.544 0.024 0.252 0.180
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 5 0.4918 0.58006 0.356 0.008 0.016 0.004 0.596 0.020
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.5108 -0.55124 0.000 0.016 0.512 0.020 0.016 0.436
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.95890 1.000 0.000 0.000 0.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 5 0.6523 0.46620 0.044 0.192 0.000 0.020 0.560 0.184
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.6143 0.11279 0.000 0.000 0.456 0.020 0.360 0.164
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.3817 0.80557 0.012 0.808 0.000 0.024 0.032 0.124
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0146 0.95638 0.996 0.000 0.000 0.000 0.004 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.5700 0.65952 0.216 0.008 0.008 0.032 0.644 0.092
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0458 0.95635 0.984 0.000 0.000 0.000 0.000 0.016
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.2122 0.97330 0.076 0.000 0.000 0.900 0.024 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.3616 0.04118 0.000 0.000 0.748 0.008 0.012 0.232
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 6 0.5338 0.91446 0.000 0.020 0.412 0.012 0.036 0.520
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.1218 0.87177 0.028 0.956 0.000 0.000 0.004 0.012
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.2053 0.25819 0.000 0.000 0.888 0.004 0.000 0.108
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.2039 0.97347 0.076 0.000 0.000 0.904 0.020 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.5698 -0.33884 0.000 0.004 0.500 0.024 0.076 0.396
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.2655 0.93436 0.036 0.000 0.000 0.884 0.020 0.060
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.4326 0.64213 0.304 0.004 0.000 0.012 0.664 0.016
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.4787 -0.59978 0.000 0.020 0.520 0.020 0.000 0.440
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 5 0.6523 0.46620 0.044 0.192 0.000 0.020 0.560 0.184
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.2006 0.26255 0.000 0.000 0.892 0.004 0.000 0.104
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.3767 0.66719 0.276 0.012 0.000 0.004 0.708 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.4319 0.66153 0.100 0.072 0.016 0.004 0.788 0.020
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.3919 0.66930 0.268 0.008 0.000 0.016 0.708 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0820 0.94593 0.972 0.000 0.000 0.000 0.012 0.016
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 5 0.6047 0.54373 0.052 0.112 0.000 0.020 0.628 0.188
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.5675 0.66008 0.212 0.008 0.008 0.032 0.648 0.092
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.4142 0.81196 0.024 0.792 0.000 0.024 0.036 0.124
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0260 0.95793 0.992 0.000 0.000 0.000 0.000 0.008
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.2725 0.95400 0.060 0.000 0.000 0.880 0.020 0.040
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.6573 0.09938 0.020 0.444 0.000 0.016 0.344 0.176
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.4292 0.67469 0.244 0.008 0.000 0.020 0.712 0.016
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.5700 0.65952 0.216 0.008 0.008 0.032 0.644 0.092
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.95890 1.000 0.000 0.000 0.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.5028 -0.55218 0.000 0.016 0.516 0.020 0.012 0.436
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0993 0.87357 0.024 0.964 0.000 0.000 0.012 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0260 0.95781 0.992 0.000 0.000 0.000 0.000 0.008
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.4073 0.67916 0.236 0.012 0.000 0.016 0.728 0.008
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0993 0.87357 0.024 0.964 0.000 0.000 0.012 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.5675 0.66008 0.212 0.008 0.008 0.032 0.648 0.092
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.4748 0.17256 0.272 0.000 0.656 0.004 0.004 0.064
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.2122 0.97330 0.076 0.000 0.000 0.900 0.024 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.1232 0.87335 0.024 0.956 0.000 0.000 0.016 0.004
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.1074 0.87288 0.028 0.960 0.000 0.000 0.012 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0458 0.95635 0.984 0.000 0.000 0.000 0.000 0.016
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.95890 1.000 0.000 0.000 0.000 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.4787 -0.59978 0.000 0.020 0.520 0.020 0.000 0.440
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.4787 -0.59978 0.000 0.020 0.520 0.020 0.000 0.440
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.2649 0.91871 0.028 0.000 0.000 0.880 0.016 0.076
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.4439 0.64758 0.296 0.004 0.008 0.008 0.668 0.016
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0767 0.86997 0.012 0.976 0.000 0.000 0.008 0.004
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.1701 0.32853 0.000 0.000 0.920 0.008 0.000 0.072
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.1643 0.32970 0.000 0.000 0.924 0.008 0.000 0.068
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.5088 0.03505 0.516 0.000 0.412 0.004 0.000 0.068
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0458 0.95635 0.984 0.000 0.000 0.000 0.000 0.016
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.2478 0.97301 0.076 0.000 0.000 0.888 0.024 0.012
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.5402 0.15117 0.000 0.000 0.644 0.024 0.152 0.180
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.2478 0.97301 0.076 0.000 0.000 0.888 0.024 0.012
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0767 0.86997 0.012 0.976 0.000 0.000 0.008 0.004
#> 322AF320-1379-4F51-AFDC-5292A060CD52 6 0.5373 0.91499 0.000 0.024 0.412 0.020 0.024 0.520
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.6288 0.09127 0.012 0.472 0.004 0.016 0.368 0.128
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.95890 1.000 0.000 0.000 0.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.1285 0.84917 0.000 0.944 0.000 0.000 0.004 0.052
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.4142 0.81196 0.024 0.792 0.000 0.024 0.036 0.124
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.3292 0.05805 0.000 0.000 0.784 0.008 0.008 0.200
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.3851 0.66087 0.284 0.004 0.000 0.008 0.700 0.004
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.4504 0.65981 0.272 0.004 0.008 0.016 0.684 0.016
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.95890 1.000 0.000 0.000 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 5 0.6908 0.53939 0.248 0.004 0.152 0.016 0.516 0.064
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 5 0.4218 0.49504 0.428 0.016 0.000 0.000 0.556 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.4146 0.67456 0.248 0.008 0.000 0.016 0.716 0.012
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 5 0.3789 0.62885 0.332 0.008 0.000 0.000 0.660 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.95890 1.000 0.000 0.000 0.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.5401 0.48568 0.008 0.632 0.000 0.012 0.236 0.112
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0458 0.95635 0.984 0.000 0.000 0.000 0.000 0.016
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.2478 0.97301 0.076 0.000 0.000 0.888 0.024 0.012
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 5 0.6488 0.46024 0.040 0.196 0.000 0.020 0.560 0.184
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0993 0.87357 0.024 0.964 0.000 0.000 0.012 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.2912 0.73060 0.172 0.816 0.000 0.000 0.012 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.4845 -0.55548 0.000 0.016 0.540 0.016 0.008 0.420
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0458 0.95635 0.984 0.000 0.000 0.000 0.000 0.016
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.4365 0.74543 0.008 0.744 0.000 0.024 0.036 0.188
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.95890 1.000 0.000 0.000 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.4142 0.81196 0.024 0.792 0.000 0.024 0.036 0.124
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.3992 0.67123 0.264 0.012 0.000 0.016 0.708 0.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.2478 0.97301 0.076 0.000 0.000 0.888 0.024 0.012
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.4820 -0.49934 0.000 0.004 0.540 0.020 0.016 0.420
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.1845 0.83376 0.000 0.916 0.004 0.000 0.008 0.072
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.4073 0.67916 0.236 0.012 0.000 0.016 0.728 0.008
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.5698 -0.33884 0.000 0.004 0.500 0.024 0.076 0.396
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 5 0.6488 0.46024 0.040 0.196 0.000 0.020 0.560 0.184
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.3510 0.08067 0.000 0.000 0.772 0.008 0.016 0.204
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.4797 -0.64037 0.000 0.020 0.508 0.020 0.000 0.452
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0405 0.33512 0.000 0.000 0.988 0.004 0.000 0.008
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 5 0.6385 0.48262 0.292 0.272 0.000 0.000 0.420 0.016
#> A608BCEB-2C27-4927-A308-E6975F641722 5 0.6573 0.21712 0.020 0.320 0.000 0.016 0.460 0.184
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0891 0.87337 0.024 0.968 0.000 0.000 0.008 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.5700 0.65952 0.216 0.008 0.008 0.032 0.644 0.092
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 5 0.6523 0.46620 0.044 0.192 0.000 0.020 0.560 0.184
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "skmeans"]
# you can also extract it by
# res = res_list["CV:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.647 0.805 0.914 0.4977 0.511 0.511
#> 3 3 0.800 0.848 0.913 0.3433 0.676 0.445
#> 4 4 0.768 0.754 0.863 0.1216 0.839 0.565
#> 5 5 0.917 0.876 0.939 0.0544 0.930 0.732
#> 6 6 0.906 0.861 0.920 0.0407 0.946 0.756
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 5
There is also optional best \(k\) = 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 2 0.7219 0.7456 0.200 0.800
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.0000 0.8770 0.000 1.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.8770 0.000 1.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0000 0.8770 0.000 1.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 2 0.0000 0.8770 0.000 1.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.3114 0.9033 0.944 0.056
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.4562 0.8666 0.904 0.096
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0000 0.8770 0.000 1.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.0000 0.9357 1.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 2 0.9795 0.3887 0.416 0.584
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.0000 0.9357 1.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.0000 0.8770 0.000 1.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.9970 0.0915 0.468 0.532
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.9357 1.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.0000 0.9357 1.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 1 0.4690 0.8626 0.900 0.100
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.9909 0.1670 0.444 0.556
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 2 0.2423 0.8634 0.040 0.960
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.8770 0.000 1.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.0376 0.9340 0.996 0.004
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0376 0.8755 0.004 0.996
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 1 0.2778 0.9077 0.952 0.048
#> F325847E-F046-4B67-B01C-16919C401020 1 0.0938 0.9295 0.988 0.012
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.0000 0.9357 1.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.8770 0.000 1.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.0376 0.9340 0.996 0.004
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 1 0.3733 0.8949 0.928 0.072
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 2 0.9732 0.4196 0.404 0.596
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.0000 0.9357 1.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 2 0.9286 0.5312 0.344 0.656
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.0000 0.8770 0.000 1.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.0000 0.9357 1.000 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.9686 0.3209 0.396 0.604
#> 91BA5F90-9174-4533-A050-39A28E34A94D 2 0.2778 0.8599 0.048 0.952
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 2 0.3431 0.8535 0.064 0.936
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 2 0.9795 0.3887 0.416 0.584
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.7376 0.6847 0.792 0.208
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.0000 0.9357 1.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 1 0.2778 0.9077 0.952 0.048
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.8770 0.000 1.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.0000 0.9357 1.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.0376 0.9340 0.996 0.004
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 1 0.2778 0.9077 0.952 0.048
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.0000 0.9357 1.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 2 0.2778 0.8599 0.048 0.952
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.0000 0.9357 1.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.0000 0.8770 0.000 1.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.0000 0.9357 1.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 2 0.0000 0.8770 0.000 1.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.0000 0.8770 0.000 1.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 2 0.0000 0.8770 0.000 1.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 2 0.2778 0.8599 0.048 0.952
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 2 0.0000 0.8770 0.000 1.000
#> AD294665-6F90-459C-90D5-3058F210225D 2 0.9000 0.5859 0.316 0.684
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.0938 0.8736 0.012 0.988
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 2 0.9795 0.3887 0.416 0.584
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0000 0.9357 1.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.8770 0.000 1.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.0000 0.9357 1.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 2 0.3431 0.8535 0.064 0.936
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 2 0.2778 0.8599 0.048 0.952
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.0000 0.9357 1.000 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.8770 0.000 1.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 2 0.9393 0.5096 0.356 0.644
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 2 0.5059 0.7963 0.112 0.888
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.8770 0.000 1.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 2 0.7453 0.7275 0.212 0.788
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0000 0.9357 1.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.8016 0.6146 0.756 0.244
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0376 0.8755 0.004 0.996
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.8770 0.000 1.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 2 0.9795 0.3887 0.416 0.584
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.8661 0.5278 0.712 0.288
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.0000 0.9357 1.000 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.0000 0.9357 1.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.0000 0.9357 1.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 2 0.9795 0.3887 0.416 0.584
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.8770 0.000 1.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.0000 0.9357 1.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.0000 0.9357 1.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.7219 0.7024 0.800 0.200
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 2 0.9795 0.3887 0.416 0.584
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.7602 0.7244 0.220 0.780
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.0000 0.9357 1.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 2 0.6623 0.7711 0.172 0.828
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.8770 0.000 1.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 1 0.2778 0.9077 0.952 0.048
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0000 0.8770 0.000 1.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 2 0.2778 0.8599 0.048 0.952
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 1 0.9866 0.2165 0.568 0.432
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.8770 0.000 1.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0000 0.9357 1.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 2 0.2603 0.8617 0.044 0.956
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.4431 0.8545 0.908 0.092
#> C2662596-6E2F-4924-B051-CEA1AC87B197 2 0.9795 0.3887 0.416 0.584
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.0672 0.9320 0.992 0.008
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.0000 0.8770 0.000 1.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 2 0.0938 0.8736 0.012 0.988
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 2 0.0000 0.8770 0.000 1.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 2 0.2778 0.8599 0.048 0.952
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0938 0.8720 0.012 0.988
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 2 0.4431 0.8344 0.092 0.908
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.0376 0.9340 0.996 0.004
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.0000 0.8770 0.000 1.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.8770 0.000 1.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.0000 0.8770 0.000 1.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 1 0.0938 0.9301 0.988 0.012
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 2 0.9795 0.3887 0.416 0.584
#> A60DC925-7343-496E-900D-0DD81D5C8123 1 0.1843 0.9210 0.972 0.028
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 2 0.9795 0.3887 0.416 0.584
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.8770 0.000 1.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 2 0.0000 0.8770 0.000 1.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.7299 0.6918 0.796 0.204
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.0000 0.9357 1.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 1 0.9795 0.2577 0.584 0.416
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 2 0.0938 0.8725 0.012 0.988
#> 2629FEE3-A203-4411-8A70-02A796C9505C 1 0.2778 0.9077 0.952 0.048
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0000 0.8770 0.000 1.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.0000 0.9357 1.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.0000 0.9357 1.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.0000 0.9357 1.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0000 0.8770 0.000 1.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.8770 0.000 1.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.8770 0.000 1.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 2 0.3431 0.8535 0.064 0.936
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.0000 0.8770 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.2261 0.843 0.932 0.000 0.068
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.2878 0.900 0.096 0.904 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.920 0.000 1.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0000 0.920 0.000 1.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 2 0.6045 0.493 0.380 0.620 0.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.2796 0.891 0.092 0.000 0.908
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.2796 0.891 0.092 0.000 0.908
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.2878 0.900 0.096 0.904 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.1643 0.922 0.044 0.000 0.956
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.2878 0.876 0.904 0.096 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.2878 0.868 0.096 0.000 0.904
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.2878 0.900 0.096 0.904 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.4399 0.859 0.092 0.044 0.864
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.3192 0.847 0.888 0.000 0.112
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.945 0.000 0.000 1.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.0000 0.945 0.000 0.000 1.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.920 0.000 1.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.2878 0.876 0.904 0.096 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.920 0.000 1.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.2796 0.858 0.908 0.000 0.092
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.920 0.000 1.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.0000 0.945 0.000 0.000 1.000
#> F325847E-F046-4B67-B01C-16919C401020 3 0.2796 0.891 0.092 0.000 0.908
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.945 0.000 0.000 1.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.920 0.000 1.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.2796 0.858 0.908 0.000 0.092
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.2796 0.891 0.092 0.000 0.908
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.4555 0.778 0.800 0.000 0.200
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.0000 0.945 0.000 0.000 1.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.2878 0.876 0.904 0.096 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.2878 0.900 0.096 0.904 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.2796 0.891 0.092 0.000 0.908
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0424 0.917 0.000 0.992 0.008
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.2878 0.876 0.904 0.096 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.1989 0.845 0.948 0.048 0.004
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.2878 0.876 0.904 0.096 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.2796 0.858 0.908 0.000 0.092
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0000 0.945 0.000 0.000 1.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.0000 0.945 0.000 0.000 1.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.1529 0.886 0.040 0.960 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0000 0.945 0.000 0.000 1.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.2796 0.858 0.908 0.000 0.092
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.0237 0.943 0.004 0.000 0.996
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 3 0.5138 0.661 0.252 0.000 0.748
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.0000 0.857 1.000 0.000 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.0000 0.945 0.000 0.000 1.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.2878 0.900 0.096 0.904 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0000 0.945 0.000 0.000 1.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.5529 0.513 0.704 0.296 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.2878 0.900 0.096 0.904 0.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.3116 0.803 0.892 0.108 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.2878 0.876 0.904 0.096 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 2 0.2878 0.900 0.096 0.904 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.1989 0.845 0.948 0.048 0.004
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.0000 0.920 0.000 1.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.2878 0.876 0.904 0.096 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.3619 0.830 0.864 0.000 0.136
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.2878 0.900 0.096 0.904 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.6698 0.545 0.684 0.036 0.280
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.1989 0.845 0.948 0.048 0.004
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.2878 0.876 0.904 0.096 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.0000 0.945 0.000 0.000 1.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.920 0.000 1.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.2878 0.876 0.904 0.096 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 2 0.6617 0.459 0.388 0.600 0.012
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.920 0.000 1.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.1989 0.845 0.948 0.048 0.004
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.3116 0.852 0.108 0.000 0.892
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.2796 0.858 0.908 0.000 0.092
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.920 0.000 1.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.920 0.000 1.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.2878 0.876 0.904 0.096 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.2878 0.876 0.904 0.096 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0000 0.945 0.000 0.000 1.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0000 0.945 0.000 0.000 1.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 3 0.5098 0.668 0.248 0.000 0.752
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.1964 0.841 0.944 0.056 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.920 0.000 1.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.945 0.000 0.000 1.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0000 0.945 0.000 0.000 1.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.8065 0.450 0.304 0.092 0.604
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.2878 0.876 0.904 0.096 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.2796 0.858 0.908 0.000 0.092
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.2796 0.891 0.092 0.000 0.908
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.2796 0.858 0.908 0.000 0.092
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.920 0.000 1.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 3 0.0000 0.945 0.000 0.000 1.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.2796 0.901 0.092 0.908 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.2878 0.876 0.904 0.096 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0892 0.913 0.000 0.980 0.020
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.920 0.000 1.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.0000 0.945 0.000 0.000 1.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.3116 0.803 0.892 0.108 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.3116 0.806 0.892 0.000 0.108
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.2878 0.876 0.904 0.096 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.6308 -0.182 0.508 0.000 0.492
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.6180 0.460 0.584 0.416 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.3116 0.803 0.892 0.108 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.5058 0.685 0.756 0.244 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.2878 0.876 0.904 0.096 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.2796 0.901 0.092 0.908 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.2878 0.876 0.904 0.096 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.2796 0.858 0.908 0.000 0.092
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.2878 0.900 0.096 0.904 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.920 0.000 1.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.0000 0.920 0.000 1.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0000 0.945 0.000 0.000 1.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.2878 0.876 0.904 0.096 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 3 0.1964 0.910 0.000 0.056 0.944
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.2878 0.876 0.904 0.096 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.920 0.000 1.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.6308 -0.129 0.508 0.492 0.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.2796 0.858 0.908 0.000 0.092
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.945 0.000 0.000 1.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.5497 0.593 0.000 0.708 0.292
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 2 0.6062 0.484 0.384 0.616 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.0237 0.943 0.004 0.000 0.996
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.2878 0.900 0.096 0.904 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.0000 0.945 0.000 0.000 1.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0000 0.945 0.000 0.000 1.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.945 0.000 0.000 1.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0000 0.920 0.000 1.000 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.2878 0.900 0.096 0.904 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.920 0.000 1.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.1989 0.845 0.948 0.048 0.004
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.2878 0.900 0.096 0.904 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 4 0.7279 -0.0309 0.408 0.000 0.148 0.444
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 1 0.4907 0.4463 0.580 0.420 0.000 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0657 0.9187 0.004 0.984 0.000 0.012
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0592 0.9202 0.000 0.984 0.000 0.016
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.3280 0.6937 0.860 0.124 0.000 0.016
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.0707 0.9459 0.020 0.000 0.980 0.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.0707 0.9459 0.020 0.000 0.980 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 1 0.4907 0.4463 0.580 0.420 0.000 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 4 0.3149 0.7574 0.088 0.032 0.000 0.880
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.4356 0.6019 0.000 0.000 0.708 0.292
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.3356 0.7233 0.176 0.824 0.000 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.0188 0.9586 0.004 0.000 0.996 0.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 4 0.3236 0.7451 0.088 0.004 0.028 0.880
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.9172 0.000 1.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 4 0.4057 0.7031 0.152 0.032 0.000 0.816
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0707 0.9180 0.000 0.980 0.000 0.020
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.5245 0.6191 0.320 0.016 0.004 0.660
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.9172 0.000 1.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> F325847E-F046-4B67-B01C-16919C401020 3 0.2469 0.8530 0.108 0.000 0.892 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0592 0.9202 0.000 0.984 0.000 0.016
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.5245 0.6191 0.320 0.016 0.004 0.660
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.2281 0.8670 0.096 0.000 0.904 0.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 4 0.7159 0.3467 0.244 0.000 0.200 0.556
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 4 0.3342 0.7527 0.100 0.032 0.000 0.868
#> A8E48877-F8AB-44DD-A18B-194D87C44931 1 0.4898 0.4532 0.584 0.416 0.000 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.0188 0.9586 0.004 0.000 0.996 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.9172 0.000 1.000 0.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 4 0.3342 0.7527 0.100 0.032 0.000 0.868
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.0817 0.6764 0.976 0.000 0.000 0.024
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 4 0.2699 0.7597 0.068 0.028 0.000 0.904
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.5245 0.6191 0.320 0.016 0.004 0.660
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0707 0.9179 0.000 0.980 0.000 0.020
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.5245 0.6191 0.320 0.016 0.004 0.660
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.5613 0.6086 0.320 0.016 0.016 0.648
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.3801 0.5950 0.780 0.000 0.000 0.220
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 1 0.4898 0.4532 0.584 0.416 0.000 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.4483 0.5412 0.712 0.004 0.000 0.284
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 1 0.4941 0.4092 0.564 0.436 0.000 0.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.3157 0.6478 0.852 0.004 0.000 0.144
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 4 0.3367 0.7488 0.108 0.028 0.000 0.864
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.4804 0.4948 0.616 0.384 0.000 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.0336 0.6711 0.992 0.000 0.000 0.008
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.0188 0.9178 0.000 0.996 0.000 0.004
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 4 0.2699 0.7597 0.068 0.028 0.000 0.904
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.5245 0.6191 0.320 0.016 0.004 0.660
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.3444 0.7097 0.184 0.816 0.000 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.0469 0.6729 0.988 0.000 0.000 0.012
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0817 0.6764 0.976 0.000 0.000 0.024
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 4 0.3342 0.7527 0.100 0.032 0.000 0.868
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0592 0.9202 0.000 0.984 0.000 0.016
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 4 0.3117 0.7571 0.092 0.028 0.000 0.880
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.1406 0.6862 0.960 0.024 0.000 0.016
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0592 0.9202 0.000 0.984 0.000 0.016
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.0336 0.6711 0.992 0.000 0.000 0.008
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.4134 0.6512 0.000 0.000 0.740 0.260
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.5245 0.6191 0.320 0.016 0.004 0.660
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0592 0.9202 0.000 0.984 0.000 0.016
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0592 0.9202 0.000 0.984 0.000 0.016
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 4 0.2699 0.7597 0.068 0.028 0.000 0.904
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 4 0.3149 0.7574 0.088 0.032 0.000 0.880
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.5613 0.6086 0.320 0.016 0.016 0.648
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.4134 0.5622 0.740 0.000 0.000 0.260
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0469 0.9199 0.000 0.988 0.000 0.012
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.5699 0.3244 0.000 0.032 0.588 0.380
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 4 0.2699 0.7597 0.068 0.028 0.000 0.904
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.5245 0.6191 0.320 0.016 0.004 0.660
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0188 0.9586 0.004 0.000 0.996 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.5245 0.6191 0.320 0.016 0.004 0.660
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0469 0.9199 0.000 0.988 0.000 0.012
#> 322AF320-1379-4F51-AFDC-5292A060CD52 3 0.0188 0.9585 0.000 0.004 0.996 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.1807 0.8819 0.052 0.940 0.000 0.008
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 4 0.3342 0.7527 0.100 0.032 0.000 0.868
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0707 0.9021 0.000 0.980 0.020 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.9172 0.000 1.000 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.3801 0.5993 0.780 0.000 0.000 0.220
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.1411 0.6687 0.960 0.000 0.020 0.020
#> C2662596-6E2F-4924-B051-CEA1AC87B197 4 0.3051 0.7580 0.088 0.028 0.000 0.884
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.7050 0.2372 0.564 0.000 0.264 0.172
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.7734 0.2184 0.420 0.236 0.000 0.344
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.1209 0.6790 0.964 0.004 0.000 0.032
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.5331 0.4543 0.644 0.024 0.000 0.332
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 4 0.3342 0.7527 0.100 0.032 0.000 0.868
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.1637 0.8727 0.060 0.940 0.000 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 4 0.2699 0.7597 0.068 0.028 0.000 0.904
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.5245 0.6191 0.320 0.016 0.004 0.660
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 1 0.4907 0.4463 0.580 0.420 0.000 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0592 0.9202 0.000 0.984 0.000 0.016
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.5790 0.4950 0.080 0.684 0.000 0.236
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 4 0.2699 0.7597 0.068 0.028 0.000 0.904
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.6203 0.5865 0.016 0.700 0.180 0.104
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 4 0.3149 0.7574 0.088 0.032 0.000 0.880
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.9172 0.000 1.000 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.3638 0.6681 0.848 0.032 0.000 0.120
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.5245 0.6191 0.320 0.016 0.004 0.660
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.1867 0.8570 0.000 0.928 0.072 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.1284 0.6855 0.964 0.024 0.000 0.012
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 1 0.4907 0.4463 0.580 0.420 0.000 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.9612 0.000 0.000 1.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.3448 0.7387 0.004 0.828 0.000 0.168
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.3837 0.6338 0.224 0.776 0.000 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0592 0.9202 0.000 0.984 0.000 0.016
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.0817 0.6764 0.976 0.000 0.000 0.024
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 1 0.4898 0.4532 0.584 0.416 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.3401 0.80015 0.840 0.000 0.096 0.000 0.064
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 5 0.2069 0.85815 0.000 0.076 0.000 0.012 0.912
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0404 0.93276 0.000 0.988 0.000 0.000 0.012
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0000 0.93935 0.000 1.000 0.000 0.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.1195 0.88661 0.028 0.000 0.000 0.012 0.960
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.0898 0.95605 0.000 0.000 0.972 0.008 0.020
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.1041 0.94874 0.000 0.000 0.964 0.004 0.032
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 5 0.2189 0.85209 0.000 0.084 0.000 0.012 0.904
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.97127 0.000 0.000 1.000 0.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.91682 1.000 0.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.3730 0.62086 0.712 0.000 0.288 0.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.4276 0.40762 0.000 0.616 0.000 0.004 0.380
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.0609 0.95814 0.000 0.000 0.980 0.000 0.020
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0162 0.91601 0.996 0.000 0.000 0.004 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.97127 0.000 0.000 1.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.0324 0.96971 0.000 0.000 0.992 0.004 0.004
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.93935 0.000 1.000 0.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0162 0.91470 0.996 0.000 0.000 0.000 0.004
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.93935 0.000 1.000 0.000 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0510 0.95992 0.016 0.000 0.000 0.984 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.93935 0.000 1.000 0.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.0000 0.97127 0.000 0.000 1.000 0.000 0.000
#> F325847E-F046-4B67-B01C-16919C401020 3 0.2329 0.84142 0.000 0.000 0.876 0.000 0.124
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.97127 0.000 0.000 1.000 0.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.93935 0.000 1.000 0.000 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0510 0.95992 0.016 0.000 0.000 0.984 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.1908 0.87836 0.000 0.000 0.908 0.000 0.092
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.4195 0.71144 0.768 0.000 0.188 0.008 0.036
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.0000 0.97127 0.000 0.000 1.000 0.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.91682 1.000 0.000 0.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 5 0.2006 0.86061 0.000 0.072 0.000 0.012 0.916
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.0324 0.96971 0.000 0.000 0.992 0.004 0.004
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.93935 0.000 1.000 0.000 0.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.91682 1.000 0.000 0.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.2735 0.86450 0.036 0.000 0.000 0.084 0.880
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0609 0.90959 0.980 0.000 0.000 0.020 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0510 0.95992 0.016 0.000 0.000 0.984 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0000 0.97127 0.000 0.000 1.000 0.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.0324 0.96971 0.000 0.000 0.992 0.004 0.004
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.93935 0.000 1.000 0.000 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0000 0.97127 0.000 0.000 1.000 0.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0510 0.95992 0.016 0.000 0.000 0.984 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.0324 0.96971 0.000 0.000 0.992 0.004 0.004
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0404 0.95670 0.012 0.000 0.000 0.988 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.3779 0.76254 0.200 0.000 0.000 0.024 0.776
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.0162 0.97077 0.000 0.000 0.996 0.004 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 5 0.2006 0.86061 0.000 0.072 0.000 0.012 0.916
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0000 0.97127 0.000 0.000 1.000 0.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.2077 0.87544 0.084 0.000 0.000 0.008 0.908
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.1251 0.88003 0.000 0.036 0.000 0.008 0.956
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.2171 0.88178 0.064 0.000 0.000 0.024 0.912
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0000 0.91682 1.000 0.000 0.000 0.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 5 0.1444 0.87263 0.000 0.040 0.000 0.012 0.948
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.2769 0.86063 0.032 0.000 0.000 0.092 0.876
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.0000 0.93935 0.000 1.000 0.000 0.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0609 0.90959 0.980 0.000 0.000 0.020 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0510 0.95992 0.016 0.000 0.000 0.984 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.4251 0.42230 0.000 0.624 0.000 0.004 0.372
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.2139 0.88295 0.032 0.000 0.000 0.052 0.916
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.2735 0.86450 0.036 0.000 0.000 0.084 0.880
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.91682 1.000 0.000 0.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.0162 0.97077 0.000 0.000 0.996 0.004 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.93935 0.000 1.000 0.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0000 0.91682 1.000 0.000 0.000 0.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.1493 0.88727 0.028 0.000 0.000 0.024 0.948
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.93935 0.000 1.000 0.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.2793 0.86202 0.036 0.000 0.000 0.088 0.876
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.4015 0.50596 0.652 0.000 0.348 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0510 0.95992 0.016 0.000 0.000 0.984 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.93935 0.000 1.000 0.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.93935 0.000 1.000 0.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0609 0.90959 0.980 0.000 0.000 0.020 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0162 0.91558 0.996 0.000 0.004 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0162 0.97077 0.000 0.000 0.996 0.004 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0162 0.97077 0.000 0.000 0.996 0.004 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0404 0.95670 0.012 0.000 0.000 0.988 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.2771 0.84413 0.128 0.000 0.000 0.012 0.860
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.93935 0.000 1.000 0.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.97127 0.000 0.000 1.000 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0000 0.97127 0.000 0.000 1.000 0.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.4297 0.07839 0.472 0.000 0.528 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0609 0.90959 0.980 0.000 0.000 0.020 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0510 0.95992 0.016 0.000 0.000 0.984 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.97127 0.000 0.000 1.000 0.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0510 0.95992 0.016 0.000 0.000 0.984 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.93935 0.000 1.000 0.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 3 0.0324 0.96971 0.000 0.000 0.992 0.004 0.004
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.2583 0.83271 0.000 0.864 0.000 0.004 0.132
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.91682 1.000 0.000 0.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0162 0.93672 0.000 0.996 0.000 0.004 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.93935 0.000 1.000 0.000 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.0000 0.97127 0.000 0.000 1.000 0.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.2293 0.87450 0.084 0.000 0.000 0.016 0.900
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.3817 0.84099 0.056 0.000 0.012 0.108 0.824
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.91682 1.000 0.000 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 4 0.6516 0.28948 0.004 0.000 0.200 0.504 0.292
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.1243 0.89133 0.960 0.004 0.000 0.008 0.028
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.1753 0.88679 0.032 0.000 0.000 0.032 0.936
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.4549 -0.00218 0.528 0.000 0.000 0.008 0.464
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.91682 1.000 0.000 0.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.1892 0.87979 0.000 0.916 0.000 0.004 0.080
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0609 0.90959 0.980 0.000 0.000 0.020 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0510 0.95992 0.016 0.000 0.000 0.984 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 5 0.2069 0.85815 0.000 0.076 0.000 0.012 0.912
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.93935 0.000 1.000 0.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.1270 0.89550 0.052 0.948 0.000 0.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0162 0.97077 0.000 0.000 0.996 0.004 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0609 0.90959 0.980 0.000 0.000 0.020 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.1469 0.89794 0.000 0.948 0.016 0.036 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0162 0.91608 0.996 0.000 0.000 0.004 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.93935 0.000 1.000 0.000 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.1364 0.88751 0.036 0.000 0.000 0.012 0.952
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0510 0.95992 0.016 0.000 0.000 0.984 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.97127 0.000 0.000 1.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0404 0.92926 0.000 0.988 0.012 0.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.1493 0.88727 0.028 0.000 0.000 0.024 0.948
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.0324 0.96971 0.000 0.000 0.992 0.004 0.004
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 5 0.2189 0.85209 0.000 0.084 0.000 0.012 0.904
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.0000 0.97127 0.000 0.000 1.000 0.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0162 0.97077 0.000 0.000 0.996 0.004 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.97127 0.000 0.000 1.000 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.4789 0.39809 0.368 0.608 0.000 0.004 0.020
#> A608BCEB-2C27-4927-A308-E6975F641722 5 0.4450 -0.05357 0.000 0.488 0.000 0.004 0.508
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.93935 0.000 1.000 0.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.2735 0.86450 0.036 0.000 0.000 0.084 0.880
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 5 0.2006 0.86061 0.000 0.072 0.000 0.012 0.916
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.4507 0.6991 0.756 0.000 0.076 0.000 0.048 0.120
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 6 0.2266 0.8897 0.000 0.012 0.000 0.000 0.108 0.880
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.1610 0.8872 0.000 0.916 0.000 0.000 0.000 0.084
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0260 0.9589 0.000 0.992 0.000 0.000 0.000 0.008
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.0547 0.8632 0.000 0.000 0.000 0.000 0.980 0.020
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.2706 0.8697 0.000 0.000 0.852 0.000 0.024 0.124
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.3109 0.7793 0.000 0.000 0.772 0.000 0.004 0.224
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 6 0.2266 0.8897 0.000 0.012 0.000 0.000 0.108 0.880
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.1333 0.9345 0.000 0.000 0.944 0.000 0.008 0.048
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.8880 1.000 0.000 0.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.4897 0.4800 0.604 0.000 0.332 0.000 0.012 0.052
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 6 0.3487 0.7749 0.000 0.168 0.000 0.000 0.044 0.788
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.3175 0.7272 0.000 0.000 0.744 0.000 0.000 0.256
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.1003 0.8817 0.964 0.000 0.004 0.000 0.004 0.028
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.1219 0.9357 0.000 0.000 0.948 0.000 0.004 0.048
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.1556 0.9227 0.000 0.000 0.920 0.000 0.000 0.080
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0260 0.9586 0.000 0.992 0.000 0.000 0.000 0.008
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0000 0.8880 1.000 0.000 0.000 0.000 0.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0260 0.9589 0.000 0.992 0.000 0.000 0.000 0.008
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0260 0.9586 0.000 0.992 0.000 0.000 0.000 0.008
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.0632 0.9397 0.000 0.000 0.976 0.000 0.000 0.024
#> F325847E-F046-4B67-B01C-16919C401020 3 0.4671 0.6990 0.000 0.000 0.688 0.000 0.156 0.156
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0865 0.9374 0.000 0.000 0.964 0.000 0.000 0.036
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0260 0.9579 0.000 0.992 0.000 0.000 0.000 0.008
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.3734 0.7203 0.000 0.000 0.716 0.000 0.020 0.264
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.6172 0.2311 0.468 0.000 0.176 0.000 0.336 0.020
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.0458 0.9404 0.000 0.000 0.984 0.000 0.000 0.016
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.8880 1.000 0.000 0.000 0.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 6 0.2266 0.8897 0.000 0.012 0.000 0.000 0.108 0.880
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.2350 0.9092 0.000 0.000 0.880 0.000 0.020 0.100
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0260 0.9562 0.000 0.992 0.000 0.000 0.000 0.008
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.8880 1.000 0.000 0.000 0.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.2805 0.7775 0.000 0.000 0.000 0.012 0.828 0.160
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0692 0.8860 0.976 0.000 0.000 0.000 0.004 0.020
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0363 0.9413 0.000 0.000 0.988 0.000 0.000 0.012
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.0713 0.9386 0.000 0.000 0.972 0.000 0.000 0.028
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0508 0.9543 0.000 0.984 0.000 0.000 0.004 0.012
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0547 0.9404 0.000 0.000 0.980 0.000 0.000 0.020
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.1444 0.9236 0.000 0.000 0.928 0.000 0.000 0.072
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.1167 0.8444 0.012 0.000 0.000 0.008 0.960 0.020
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.0363 0.9407 0.000 0.000 0.988 0.000 0.000 0.012
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 6 0.2266 0.8897 0.000 0.012 0.000 0.000 0.108 0.880
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0713 0.9389 0.000 0.000 0.972 0.000 0.000 0.028
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.0717 0.8616 0.008 0.000 0.000 0.000 0.976 0.016
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.3189 0.6263 0.000 0.004 0.000 0.000 0.760 0.236
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.0547 0.8632 0.000 0.000 0.000 0.000 0.980 0.020
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0692 0.8860 0.976 0.000 0.000 0.000 0.004 0.020
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 6 0.2191 0.8737 0.000 0.004 0.000 0.000 0.120 0.876
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.2805 0.7775 0.000 0.000 0.000 0.012 0.828 0.160
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.0260 0.9562 0.000 0.992 0.000 0.000 0.000 0.008
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0260 0.8876 0.992 0.000 0.000 0.000 0.000 0.008
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 6 0.2860 0.8417 0.000 0.100 0.000 0.000 0.048 0.852
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.0858 0.8595 0.000 0.000 0.004 0.000 0.968 0.028
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.2805 0.7775 0.000 0.000 0.000 0.012 0.828 0.160
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.8880 1.000 0.000 0.000 0.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.0363 0.9407 0.000 0.000 0.988 0.000 0.000 0.012
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0260 0.9589 0.000 0.992 0.000 0.000 0.000 0.008
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0692 0.8860 0.976 0.000 0.000 0.000 0.004 0.020
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.0547 0.8632 0.000 0.000 0.000 0.000 0.980 0.020
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0260 0.9589 0.000 0.992 0.000 0.000 0.000 0.008
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.2805 0.7775 0.000 0.000 0.000 0.012 0.828 0.160
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.5170 0.3475 0.544 0.000 0.384 0.000 0.016 0.056
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.9582 0.000 1.000 0.000 0.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0260 0.9589 0.000 0.992 0.000 0.000 0.000 0.008
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0692 0.8860 0.976 0.000 0.000 0.000 0.004 0.020
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.8880 1.000 0.000 0.000 0.000 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0363 0.9407 0.000 0.000 0.988 0.000 0.000 0.012
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0363 0.9407 0.000 0.000 0.988 0.000 0.000 0.012
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.0458 0.8589 0.000 0.000 0.000 0.000 0.984 0.016
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0363 0.9582 0.000 0.988 0.000 0.000 0.000 0.012
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.1333 0.9357 0.000 0.000 0.944 0.000 0.008 0.048
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.1398 0.9341 0.000 0.000 0.940 0.000 0.008 0.052
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.4305 0.1804 0.544 0.000 0.436 0.000 0.000 0.020
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0692 0.8860 0.976 0.000 0.000 0.000 0.004 0.020
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.1643 0.9308 0.000 0.000 0.924 0.000 0.008 0.068
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0363 0.9582 0.000 0.988 0.000 0.000 0.000 0.012
#> 322AF320-1379-4F51-AFDC-5292A060CD52 3 0.0858 0.9380 0.000 0.004 0.968 0.000 0.000 0.028
#> 53A96249-66D5-4C26-893B-ADC71481D261 6 0.3290 0.6726 0.000 0.252 0.000 0.000 0.004 0.744
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.8880 1.000 0.000 0.000 0.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0260 0.9586 0.000 0.992 0.000 0.000 0.000 0.008
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0260 0.9562 0.000 0.992 0.000 0.000 0.000 0.008
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.0363 0.9413 0.000 0.000 0.988 0.000 0.000 0.012
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.0458 0.8628 0.000 0.000 0.000 0.000 0.984 0.016
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.0951 0.8465 0.000 0.000 0.008 0.020 0.968 0.004
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.8880 1.000 0.000 0.000 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 5 0.6662 0.2924 0.004 0.004 0.232 0.236 0.488 0.036
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.3163 0.6656 0.780 0.004 0.000 0.000 0.212 0.004
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.0547 0.8632 0.000 0.000 0.000 0.000 0.980 0.020
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 5 0.4089 0.0889 0.468 0.000 0.000 0.000 0.524 0.008
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.8880 1.000 0.000 0.000 0.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 6 0.3728 0.5177 0.000 0.344 0.000 0.000 0.004 0.652
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0692 0.8860 0.976 0.000 0.000 0.000 0.004 0.020
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 6 0.2266 0.8897 0.000 0.012 0.000 0.000 0.108 0.880
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0260 0.9589 0.000 0.992 0.000 0.000 0.000 0.008
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.1082 0.9266 0.040 0.956 0.000 0.000 0.000 0.004
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0363 0.9407 0.000 0.000 0.988 0.000 0.000 0.012
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0692 0.8860 0.976 0.000 0.000 0.000 0.004 0.020
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.1003 0.9338 0.000 0.964 0.016 0.000 0.000 0.020
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.8880 1.000 0.000 0.000 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0260 0.9562 0.000 0.992 0.000 0.000 0.000 0.008
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.0547 0.8632 0.000 0.000 0.000 0.000 0.980 0.020
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0363 0.9407 0.000 0.000 0.988 0.000 0.000 0.012
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0806 0.9504 0.000 0.972 0.008 0.000 0.000 0.020
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.0547 0.8632 0.000 0.000 0.000 0.000 0.980 0.020
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.1444 0.9236 0.000 0.000 0.928 0.000 0.000 0.072
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 6 0.2266 0.8897 0.000 0.012 0.000 0.000 0.108 0.880
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.0363 0.9420 0.000 0.000 0.988 0.000 0.000 0.012
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0363 0.9407 0.000 0.000 0.988 0.000 0.000 0.012
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.1007 0.9376 0.000 0.000 0.956 0.000 0.000 0.044
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.6363 0.0275 0.344 0.404 0.000 0.000 0.016 0.236
#> A608BCEB-2C27-4927-A308-E6975F641722 6 0.2442 0.8734 0.000 0.048 0.000 0.000 0.068 0.884
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0363 0.9582 0.000 0.988 0.000 0.000 0.000 0.012
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.2805 0.7775 0.000 0.000 0.000 0.012 0.828 0.160
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 6 0.2266 0.8897 0.000 0.012 0.000 0.000 0.108 0.880
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "pam"]
# you can also extract it by
# res = res_list["CV:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.307 0.713 0.844 0.4452 0.547 0.547
#> 3 3 0.324 0.563 0.796 0.2870 0.756 0.602
#> 4 4 0.439 0.672 0.820 0.1907 0.814 0.603
#> 5 5 0.642 0.705 0.841 0.1089 0.912 0.732
#> 6 6 0.783 0.791 0.894 0.0846 0.915 0.672
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.7453 0.6505 0.788 0.212
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 1 0.5842 0.7909 0.860 0.140
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 1 0.9209 0.5462 0.664 0.336
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.8386 0.6225 0.732 0.268
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.5519 0.7978 0.872 0.128
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.9795 0.0232 0.584 0.416
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 2 0.8267 0.7317 0.260 0.740
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 1 0.5946 0.7889 0.856 0.144
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.1414 0.8294 0.980 0.020
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.8330 1.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.0000 0.8330 1.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.3274 0.7789 0.060 0.940
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.9922 0.5305 0.448 0.552
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 2 0.9710 0.6344 0.400 0.600
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.5629 0.7379 0.868 0.132
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.3879 0.7821 0.076 0.924
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.3879 0.7747 0.076 0.924
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0000 0.8330 1.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 1 0.8267 0.6306 0.740 0.260
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.4022 0.8067 0.920 0.080
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.5519 0.7449 0.128 0.872
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.8207 0.7370 0.256 0.744
#> F325847E-F046-4B67-B01C-16919C401020 2 0.9909 0.4858 0.444 0.556
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 2 0.9393 0.6777 0.356 0.644
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 1 0.9129 0.5828 0.672 0.328
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 2 0.9970 0.2457 0.468 0.532
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 2 0.8661 0.7184 0.288 0.712
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.5178 0.7851 0.884 0.116
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 2 0.4815 0.7843 0.104 0.896
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.8330 1.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 1 0.4431 0.8118 0.908 0.092
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.7376 0.7167 0.792 0.208
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 1 0.9522 0.5332 0.628 0.372
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.8330 1.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.0376 0.8328 0.996 0.004
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.8330 1.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.5059 0.7974 0.888 0.112
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 2 0.9087 0.6966 0.324 0.676
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.3114 0.7785 0.056 0.944
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.9393 0.3449 0.356 0.644
#> 604C06E9-A00E-435E-847A-3992922A5C56 2 0.9522 0.6633 0.372 0.628
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.4161 0.7964 0.916 0.084
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.3114 0.7785 0.056 0.944
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 2 0.8081 0.7128 0.248 0.752
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.0000 0.8330 1.000 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 2 0.5059 0.7854 0.112 0.888
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 1 0.4022 0.8172 0.920 0.080
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 2 0.9286 0.6867 0.344 0.656
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.0672 0.8331 0.992 0.008
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 1 0.5842 0.7909 0.860 0.140
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0672 0.8331 0.992 0.008
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0000 0.8330 1.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.3879 0.8155 0.924 0.076
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.5737 0.7688 0.864 0.136
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.8207 0.6657 0.744 0.256
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.8330 1.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 2 0.8661 0.6794 0.288 0.712
#> A54731AE-FC40-407F-8D10-67DDC122237D 1 0.9881 0.3808 0.564 0.436
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.8713 0.5196 0.708 0.292
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0938 0.8326 0.988 0.012
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.8330 1.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 2 0.4562 0.7846 0.096 0.904
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 1 0.9248 0.5711 0.660 0.340
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0000 0.8330 1.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 2 0.9922 0.5252 0.448 0.552
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 1 0.8861 0.6026 0.696 0.304
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.4298 0.8097 0.912 0.088
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.1843 0.8270 0.972 0.028
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.3114 0.8010 0.944 0.056
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.6148 0.7499 0.152 0.848
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 1 0.6801 0.7293 0.820 0.180
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.8330 1.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.9635 -0.0873 0.612 0.388
#> 721CDBE6-FC85-4C30-B23E-28407340286F 2 0.2423 0.7727 0.040 0.960
#> 392897E4-6009-422C-B461-649F4DDF260C 2 0.4562 0.7847 0.096 0.904
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 2 0.7299 0.7370 0.204 0.796
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0672 0.8324 0.992 0.008
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.9963 -0.0459 0.464 0.536
#> E5557F52-015D-49DC-9E23-989FC259976F 2 0.9170 0.6927 0.332 0.668
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.5842 0.7712 0.860 0.140
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.9988 -0.4298 0.520 0.480
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.8330 1.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.3431 0.8033 0.936 0.064
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 2 0.9286 0.6867 0.344 0.656
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.3114 0.8010 0.944 0.056
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 1 0.9087 0.5866 0.676 0.324
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.2948 0.7776 0.052 0.948
#> 53A96249-66D5-4C26-893B-ADC71481D261 1 0.9286 0.5733 0.656 0.344
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.8330 1.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.3114 0.7785 0.056 0.944
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 1 0.9286 0.5694 0.656 0.344
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 2 0.9248 0.6881 0.340 0.660
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0000 0.8330 1.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.1843 0.8308 0.972 0.028
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.3584 0.7941 0.932 0.068
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.5946 0.7648 0.856 0.144
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.0938 0.8326 0.988 0.012
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.1414 0.8319 0.980 0.020
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.0672 0.8331 0.992 0.008
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.8330 1.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.3879 0.7747 0.076 0.924
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.8330 1.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.3114 0.8010 0.944 0.056
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 1 0.5737 0.7933 0.864 0.136
#> 2D962371-EC83-490C-A663-478AF383BC1B 1 0.9170 0.5796 0.668 0.332
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.3584 0.8159 0.932 0.068
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.2948 0.7776 0.052 0.948
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0376 0.8327 0.996 0.004
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.3274 0.7783 0.060 0.940
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.3584 0.7941 0.932 0.068
#> 6969B6B2-7616-4664-9696-C4DACD10537B 1 0.6623 0.7710 0.828 0.172
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.2236 0.8315 0.964 0.036
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.3114 0.8010 0.944 0.056
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 2 0.7602 0.7596 0.220 0.780
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.3879 0.7747 0.076 0.924
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.5629 0.7966 0.868 0.132
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.3114 0.7785 0.056 0.944
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 1 0.9000 0.6125 0.684 0.316
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 2 0.5178 0.7837 0.116 0.884
#> B12A4446-2310-4139-897F-CA030478CBD5 2 0.3584 0.7821 0.068 0.932
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 2 0.9427 0.6675 0.360 0.640
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 1 0.3584 0.8185 0.932 0.068
#> A608BCEB-2C27-4927-A308-E6975F641722 1 0.9323 0.5669 0.652 0.348
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 1 0.8386 0.6225 0.732 0.268
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.0938 0.8326 0.988 0.012
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 1 0.3431 0.8233 0.936 0.064
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.5835 0.6304 0.784 0.052 0.164
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 1 0.4452 0.6834 0.808 0.000 0.192
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 3 0.6274 -0.0198 0.456 0.000 0.544
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.6244 0.2281 0.560 0.000 0.440
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.3619 0.7225 0.864 0.000 0.136
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.7958 0.1534 0.544 0.064 0.392
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.7338 0.4366 0.288 0.060 0.652
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 1 0.4452 0.6834 0.808 0.000 0.192
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.1860 0.7470 0.948 0.000 0.052
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.2878 0.7433 0.904 0.000 0.096
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.1267 0.7586 0.972 0.024 0.004
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 3 0.2651 0.6169 0.012 0.060 0.928
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 1 0.7990 -0.1021 0.532 0.064 0.404
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 3 0.8808 0.3194 0.400 0.116 0.484
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.5650 0.6605 0.808 0.108 0.084
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.3572 0.6259 0.040 0.060 0.900
#> DC55EE78-203F-4092-9B83-14B1A529194B 3 0.1964 0.6012 0.056 0.000 0.944
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.2878 0.7433 0.904 0.000 0.096
#> F772EA39-E408-4908-BADD-C786D702BF9B 1 0.6204 0.2569 0.576 0.000 0.424
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 2 0.3192 0.9523 0.112 0.888 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 3 0.3038 0.5847 0.104 0.000 0.896
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.8172 0.4750 0.272 0.112 0.616
#> F325847E-F046-4B67-B01C-16919C401020 1 0.8069 -0.1576 0.476 0.064 0.460
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.8768 0.3150 0.408 0.112 0.480
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 1 0.6309 0.1426 0.500 0.000 0.500
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 2 0.2165 0.9224 0.064 0.936 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.8285 0.4480 0.288 0.112 0.600
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.3879 0.7019 0.848 0.000 0.152
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.5538 0.5998 0.060 0.132 0.808
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.2878 0.7433 0.904 0.000 0.096
#> A8E48877-F8AB-44DD-A18B-194D87C44931 1 0.3686 0.7220 0.860 0.000 0.140
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.6258 0.6512 0.752 0.052 0.196
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 3 0.6252 -0.0805 0.444 0.000 0.556
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.2878 0.7433 0.904 0.000 0.096
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.0747 0.7588 0.984 0.000 0.016
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.3459 0.7420 0.892 0.012 0.096
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 2 0.3192 0.9523 0.112 0.888 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.9108 0.4081 0.316 0.164 0.520
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.2400 0.6135 0.004 0.064 0.932
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 3 0.5058 0.4237 0.244 0.000 0.756
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.9162 0.3645 0.368 0.152 0.480
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 2 0.3192 0.9523 0.112 0.888 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.3921 0.6094 0.016 0.112 0.872
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 2 0.0000 0.8662 0.000 1.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.0000 0.7588 1.000 0.000 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.5891 0.5857 0.052 0.168 0.780
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 1 0.3551 0.7265 0.868 0.000 0.132
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.8747 0.3296 0.396 0.112 0.492
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.0000 0.7588 1.000 0.000 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 1 0.3879 0.7146 0.848 0.000 0.152
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0237 0.7599 0.996 0.000 0.004
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0000 0.7588 1.000 0.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.3551 0.7256 0.868 0.000 0.132
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.4002 0.6881 0.840 0.000 0.160
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.6126 0.3518 0.600 0.000 0.400
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.2878 0.7433 0.904 0.000 0.096
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 2 0.2031 0.8792 0.016 0.952 0.032
#> A54731AE-FC40-407F-8D10-67DDC122237D 3 0.6252 0.1002 0.444 0.000 0.556
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.5363 0.5506 0.724 0.000 0.276
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.1267 0.7580 0.972 0.004 0.024
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.2878 0.7433 0.904 0.000 0.096
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.4316 0.6197 0.044 0.088 0.868
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 3 0.6267 -0.0712 0.452 0.000 0.548
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.2625 0.7476 0.916 0.000 0.084
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.6516 -0.1816 0.516 0.004 0.480
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 3 0.6302 -0.1195 0.480 0.000 0.520
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.3412 0.7289 0.876 0.000 0.124
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.2550 0.7407 0.932 0.012 0.056
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 2 0.3192 0.9523 0.112 0.888 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 3 0.4047 0.5794 0.148 0.004 0.848
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 1 0.5733 0.4790 0.676 0.000 0.324
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0747 0.7567 0.984 0.016 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.6314 0.2188 0.604 0.004 0.392
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.4235 0.5757 0.000 0.176 0.824
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.5637 0.5828 0.040 0.172 0.788
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 2 0.0000 0.8662 0.000 1.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0892 0.7582 0.980 0.000 0.020
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 3 0.5178 0.3949 0.256 0.000 0.744
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.8721 0.3409 0.384 0.112 0.504
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.5047 0.6999 0.824 0.036 0.140
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.8635 -0.1187 0.532 0.112 0.356
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.2959 0.7410 0.900 0.000 0.100
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.3192 0.9523 0.112 0.888 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.8739 0.3370 0.392 0.112 0.496
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 2 0.3192 0.9523 0.112 0.888 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 3 0.6274 -0.0778 0.456 0.000 0.544
#> 322AF320-1379-4F51-AFDC-5292A060CD52 3 0.2796 0.6085 0.000 0.092 0.908
#> 53A96249-66D5-4C26-893B-ADC71481D261 1 0.6260 0.2016 0.552 0.000 0.448
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.2878 0.7433 0.904 0.000 0.096
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 3 0.0000 0.6080 0.000 0.000 1.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 3 0.6260 -0.0690 0.448 0.000 0.552
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.9098 0.3279 0.148 0.360 0.492
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0000 0.7588 1.000 0.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.1031 0.7612 0.976 0.000 0.024
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.2878 0.7433 0.904 0.000 0.096
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.4346 0.6732 0.816 0.000 0.184
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.0237 0.7596 0.996 0.000 0.004
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.1289 0.7581 0.968 0.000 0.032
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.0000 0.7588 1.000 0.000 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.2796 0.7450 0.908 0.000 0.092
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 3 0.1964 0.6042 0.056 0.000 0.944
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.2878 0.7433 0.904 0.000 0.096
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 2 0.3192 0.9523 0.112 0.888 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 1 0.4399 0.6873 0.812 0.000 0.188
#> 2D962371-EC83-490C-A663-478AF383BC1B 3 0.6280 -0.0840 0.460 0.000 0.540
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.4452 0.6664 0.808 0.000 0.192
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.3192 0.6039 0.000 0.112 0.888
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0237 0.7597 0.996 0.004 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 3 0.1765 0.6197 0.040 0.004 0.956
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.2878 0.7433 0.904 0.000 0.096
#> 6969B6B2-7616-4664-9696-C4DACD10537B 1 0.5706 0.5839 0.680 0.000 0.320
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.1289 0.7614 0.968 0.000 0.032
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 2 0.3192 0.9523 0.112 0.888 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.8827 0.2967 0.120 0.384 0.496
#> D47D0433-2313-4A2F-B268-5AD293D7534E 3 0.2496 0.5988 0.068 0.004 0.928
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.3752 0.7158 0.856 0.000 0.144
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.3607 0.6073 0.008 0.112 0.880
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 1 0.6111 0.3304 0.604 0.000 0.396
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.5815 0.6151 0.096 0.104 0.800
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.3918 0.5946 0.004 0.140 0.856
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.8774 0.3004 0.412 0.112 0.476
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 1 0.3038 0.7259 0.896 0.000 0.104
#> A608BCEB-2C27-4927-A308-E6975F641722 1 0.6280 0.1768 0.540 0.000 0.460
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 1 0.6252 0.2202 0.556 0.000 0.444
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.1031 0.7584 0.976 0.000 0.024
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 1 0.3482 0.7293 0.872 0.000 0.128
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.2737 0.72491 0.888 0.008 0.104 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 1 0.6875 0.13411 0.504 0.388 0.108 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.2011 0.73546 0.080 0.920 0.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.4790 0.35191 0.380 0.620 0.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.4182 0.66301 0.796 0.180 0.024 0.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.6162 0.59347 0.676 0.168 0.156 0.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 2 0.6974 0.34643 0.284 0.564 0.152 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 1 0.6875 0.13411 0.504 0.388 0.108 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.0707 0.74304 0.980 0.000 0.020 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.3907 0.64600 0.768 0.232 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.3978 0.65352 0.796 0.012 0.192 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.2796 0.69644 0.016 0.892 0.092 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 1 0.4969 0.66093 0.772 0.088 0.140 0.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 3 0.9649 0.25409 0.260 0.152 0.368 0.220
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.3837 0.75131 0.224 0.000 0.776 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.3471 0.69664 0.072 0.868 0.060 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.2843 0.71526 0.020 0.892 0.088 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.3873 0.64959 0.772 0.228 0.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.4907 0.26180 0.420 0.580 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.1724 0.73988 0.032 0.948 0.020 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.2266 0.82754 0.004 0.084 0.912 0.000
#> F325847E-F046-4B67-B01C-16919C401020 1 0.4508 0.64852 0.780 0.036 0.184 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.2408 0.84772 0.104 0.000 0.896 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.4936 0.52031 0.280 0.700 0.020 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.4574 0.57813 0.220 0.024 0.756 0.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.3037 0.73865 0.888 0.036 0.076 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.4839 0.72618 0.044 0.200 0.756 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.3907 0.64600 0.768 0.232 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 1 0.6875 0.13411 0.504 0.388 0.108 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.4318 0.67350 0.816 0.116 0.068 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.4579 0.63118 0.200 0.768 0.032 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.3873 0.64959 0.772 0.228 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.1022 0.73669 0.968 0.032 0.000 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.4464 0.64765 0.768 0.208 0.000 0.024
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.2281 0.84796 0.096 0.000 0.904 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.3808 0.64266 0.012 0.812 0.176 0.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.3004 0.73475 0.060 0.892 0.048 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.2530 0.84313 0.112 0.000 0.888 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.3311 0.77386 0.000 0.172 0.828 0.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.1211 0.74745 0.960 0.040 0.000 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.2845 0.83948 0.028 0.076 0.896 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 1 0.6875 0.13411 0.504 0.388 0.108 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.2408 0.84772 0.104 0.000 0.896 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.1211 0.74745 0.960 0.040 0.000 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 1 0.4244 0.67389 0.804 0.160 0.036 0.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.1474 0.74810 0.948 0.052 0.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.1211 0.74745 0.960 0.040 0.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.6875 0.13411 0.504 0.388 0.108 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.2224 0.73258 0.928 0.032 0.040 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.5435 0.22386 0.420 0.564 0.016 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.3907 0.64600 0.768 0.232 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.6157 0.45962 0.232 0.660 0.108 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.4599 0.68390 0.800 0.112 0.088 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.1182 0.74047 0.968 0.016 0.016 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.3907 0.64600 0.768 0.232 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.4262 0.69198 0.008 0.236 0.756 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.1724 0.73988 0.032 0.948 0.020 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.3649 0.66931 0.796 0.204 0.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.4817 0.66650 0.784 0.088 0.128 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.1724 0.73988 0.032 0.948 0.020 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.3004 0.71784 0.892 0.060 0.048 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.1940 0.73063 0.924 0.000 0.076 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.3833 0.70610 0.072 0.848 0.080 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.4996 0.00566 0.484 0.516 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.2142 0.74277 0.928 0.016 0.000 0.056
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.5938 0.56844 0.676 0.232 0.092 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.2408 0.82237 0.000 0.104 0.896 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.2737 0.82437 0.008 0.104 0.888 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0804 0.74190 0.980 0.012 0.008 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.1724 0.73988 0.032 0.948 0.020 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.2469 0.83229 0.108 0.000 0.892 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.3836 0.67235 0.816 0.016 0.168 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.4507 0.74734 0.224 0.020 0.756 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.3907 0.64600 0.768 0.232 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.2814 0.83600 0.132 0.000 0.868 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.1624 0.73929 0.028 0.952 0.020 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.4643 0.36154 0.000 0.656 0.344 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.5085 0.55093 0.260 0.708 0.032 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.3907 0.64600 0.768 0.232 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.2125 0.70842 0.004 0.920 0.076 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.1733 0.73920 0.028 0.948 0.024 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.2530 0.84923 0.100 0.004 0.896 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0188 0.74549 0.996 0.004 0.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.1913 0.74991 0.940 0.040 0.020 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.3907 0.64600 0.768 0.232 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.3279 0.72264 0.872 0.032 0.096 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.1302 0.74757 0.956 0.044 0.000 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.1520 0.74791 0.956 0.020 0.024 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.1211 0.74745 0.960 0.040 0.000 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.3873 0.64959 0.772 0.228 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.1452 0.73018 0.008 0.956 0.036 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.3907 0.64600 0.768 0.232 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 1 0.6875 0.13411 0.504 0.388 0.108 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.1724 0.73988 0.032 0.948 0.020 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.4477 0.52419 0.688 0.312 0.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.2408 0.82237 0.000 0.104 0.896 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.1211 0.74745 0.960 0.040 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.6703 0.50728 0.232 0.612 0.156 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.3907 0.64600 0.768 0.232 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.5699 0.19402 0.380 0.588 0.032 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.1488 0.74585 0.956 0.032 0.012 0.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 1.00000 0.000 0.000 0.000 1.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.2408 0.84772 0.104 0.000 0.896 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.3278 0.69652 0.020 0.864 0.116 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.4744 0.58770 0.736 0.240 0.024 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.1302 0.80300 0.000 0.044 0.956 0.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.6578 0.36541 0.300 0.592 0.108 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.4004 0.78044 0.024 0.164 0.812 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.2530 0.82536 0.004 0.100 0.896 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.2654 0.84806 0.108 0.004 0.888 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 1 0.3400 0.66821 0.820 0.180 0.000 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.6296 0.47924 0.224 0.652 0.124 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.4164 0.56441 0.264 0.736 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.0779 0.74377 0.980 0.004 0.016 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 1 0.6875 0.13411 0.504 0.388 0.108 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.1043 0.696724 0.960 0.000 0.040 0.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 5 0.0000 0.892512 0.000 0.000 0.000 0.000 1.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.3043 0.771158 0.056 0.864 0.000 0.000 0.080
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0000 0.867479 0.000 1.000 0.000 0.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.5493 0.564889 0.664 0.220 0.008 0.000 0.108
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.7207 0.504639 0.564 0.120 0.148 0.000 0.168
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 2 0.8170 -0.056513 0.316 0.368 0.156 0.000 0.160
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 5 0.0000 0.892512 0.000 0.000 0.000 0.000 1.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.0955 0.691484 0.968 0.000 0.004 0.000 0.028
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.4015 0.494783 0.652 0.348 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.3684 0.506606 0.720 0.000 0.280 0.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.5052 0.697008 0.076 0.748 0.136 0.000 0.040
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 1 0.5992 0.527815 0.632 0.152 0.200 0.000 0.016
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 3 0.8488 -0.049930 0.272 0.268 0.292 0.168 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.2471 0.739884 0.136 0.000 0.864 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 5 0.6156 0.394893 0.008 0.308 0.128 0.000 0.556
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.867479 0.000 1.000 0.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.4015 0.494783 0.652 0.348 0.000 0.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.1121 0.839813 0.044 0.956 0.000 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 1.000000 0.000 0.000 0.000 1.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.867479 0.000 1.000 0.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.0162 0.850761 0.000 0.004 0.996 0.000 0.000
#> F325847E-F046-4B67-B01C-16919C401020 1 0.6614 0.433138 0.576 0.036 0.152 0.000 0.236
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.852396 0.000 0.000 1.000 0.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.867479 0.000 1.000 0.000 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 1.000000 0.000 0.000 0.000 1.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.4909 0.321793 0.032 0.000 0.588 0.000 0.380
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.3693 0.688640 0.836 0.036 0.024 0.000 0.104
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.3612 0.557874 0.000 0.268 0.732 0.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.4015 0.494783 0.652 0.348 0.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 5 0.0000 0.892512 0.000 0.000 0.000 0.000 1.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.7858 0.251086 0.448 0.160 0.124 0.000 0.268
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0451 0.863268 0.008 0.988 0.004 0.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.4015 0.494783 0.652 0.348 0.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.3561 0.597068 0.740 0.000 0.000 0.000 0.260
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.4401 0.507530 0.656 0.328 0.000 0.016 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 1.000000 0.000 0.000 0.000 1.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0000 0.852396 0.000 0.000 1.000 0.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.5603 0.597271 0.032 0.676 0.216 0.000 0.076
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0609 0.861097 0.020 0.980 0.000 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0000 0.852396 0.000 0.000 1.000 0.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 1.000000 0.000 0.000 0.000 1.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.3697 0.671216 0.008 0.180 0.796 0.000 0.016
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 1.000000 0.000 0.000 0.000 1.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.2625 0.696205 0.876 0.016 0.000 0.000 0.108
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.0000 0.852396 0.000 0.000 1.000 0.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 5 0.0000 0.892512 0.000 0.000 0.000 0.000 1.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0000 0.852396 0.000 0.000 1.000 0.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.2669 0.698503 0.876 0.020 0.000 0.000 0.104
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 1 0.5541 0.555516 0.668 0.212 0.012 0.000 0.108
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.3141 0.695102 0.852 0.040 0.000 0.000 0.108
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.2761 0.698745 0.872 0.024 0.000 0.000 0.104
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 5 0.0000 0.892512 0.000 0.000 0.000 0.000 1.000
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.2771 0.685872 0.860 0.000 0.012 0.000 0.128
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.1270 0.841938 0.052 0.948 0.000 0.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.4015 0.494783 0.652 0.348 0.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0000 1.000000 0.000 0.000 0.000 1.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 5 0.2690 0.772168 0.000 0.156 0.000 0.000 0.844
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.5232 0.621376 0.732 0.128 0.032 0.000 0.108
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.2424 0.687330 0.868 0.000 0.000 0.000 0.132
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.4015 0.494783 0.652 0.348 0.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.3707 0.522562 0.000 0.284 0.716 0.000 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.867479 0.000 1.000 0.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.3707 0.559227 0.716 0.284 0.000 0.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.4874 0.655385 0.760 0.108 0.028 0.000 0.104
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.867479 0.000 1.000 0.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.3482 0.669229 0.812 0.008 0.012 0.000 0.168
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.2974 0.659313 0.868 0.000 0.052 0.000 0.080
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 1.000000 0.000 0.000 0.000 1.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.867479 0.000 1.000 0.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.1671 0.810640 0.076 0.924 0.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.2519 0.668958 0.884 0.016 0.000 0.100 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.4735 0.224243 0.524 0.460 0.016 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0000 0.852396 0.000 0.000 1.000 0.000 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0404 0.846275 0.000 0.012 0.988 0.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 1.000000 0.000 0.000 0.000 1.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.2280 0.691462 0.880 0.000 0.000 0.000 0.120
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.867479 0.000 1.000 0.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0963 0.830492 0.036 0.000 0.964 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 5 0.5901 0.510016 0.268 0.000 0.148 0.000 0.584
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.4467 0.475857 0.344 0.016 0.640 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.4201 0.375839 0.592 0.408 0.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0000 1.000000 0.000 0.000 0.000 1.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.1704 0.810320 0.068 0.004 0.928 0.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0000 1.000000 0.000 0.000 0.000 1.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.867479 0.000 1.000 0.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.6068 0.000989 0.008 0.460 0.440 0.000 0.092
#> 53A96249-66D5-4C26-893B-ADC71481D261 1 0.5953 0.359038 0.540 0.336 0.000 0.000 0.124
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.4015 0.494783 0.652 0.348 0.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0693 0.859860 0.008 0.980 0.012 0.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0451 0.864428 0.004 0.988 0.008 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.0000 0.852396 0.000 0.000 1.000 0.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.2358 0.696234 0.888 0.008 0.000 0.000 0.104
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.2753 0.697197 0.876 0.012 0.008 0.000 0.104
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.4015 0.494783 0.652 0.348 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.4238 0.671529 0.788 0.024 0.024 0.004 0.160
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.0703 0.698276 0.976 0.024 0.000 0.000 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.2497 0.693273 0.880 0.004 0.004 0.000 0.112
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.1281 0.700296 0.956 0.012 0.000 0.000 0.032
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.3999 0.499681 0.656 0.344 0.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.5318 0.360370 0.008 0.616 0.052 0.000 0.324
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.4088 0.475652 0.632 0.368 0.000 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 1.000000 0.000 0.000 0.000 1.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 5 0.0000 0.892512 0.000 0.000 0.000 0.000 1.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.867479 0.000 1.000 0.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.3837 0.363510 0.308 0.692 0.000 0.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0000 0.852396 0.000 0.000 1.000 0.000 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0960 0.696013 0.972 0.016 0.004 0.000 0.008
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.2017 0.822394 0.008 0.912 0.080 0.000 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.4015 0.494783 0.652 0.348 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.2621 0.763213 0.112 0.876 0.008 0.000 0.004
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.3080 0.692032 0.852 0.020 0.004 0.000 0.124
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 1.000000 0.000 0.000 0.000 1.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.852396 0.000 0.000 1.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.1478 0.836800 0.000 0.936 0.064 0.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.5585 0.541071 0.644 0.232 0.004 0.000 0.120
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.1924 0.811755 0.008 0.004 0.924 0.000 0.064
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 5 0.0000 0.892512 0.000 0.000 0.000 0.000 1.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.2471 0.744885 0.000 0.136 0.864 0.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0000 0.852396 0.000 0.000 1.000 0.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0162 0.851081 0.004 0.000 0.996 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 1 0.5090 0.596554 0.688 0.208 0.000 0.000 0.104
#> A608BCEB-2C27-4927-A308-E6975F641722 5 0.1591 0.847642 0.004 0.052 0.004 0.000 0.940
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.867479 0.000 1.000 0.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.2280 0.690955 0.880 0.000 0.000 0.000 0.120
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 5 0.0000 0.892512 0.000 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.4595 0.496 0.608 0.012 0.028 0.000 0.352 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 6 0.0000 0.922 0.000 0.000 0.000 0.000 0.000 1.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.3210 0.769 0.012 0.832 0.000 0.000 0.124 0.032
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0363 0.880 0.012 0.988 0.000 0.000 0.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.0000 0.849 0.000 0.000 0.000 0.000 1.000 0.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 5 0.6708 0.424 0.016 0.104 0.128 0.000 0.564 0.188
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 2 0.7003 0.232 0.000 0.468 0.132 0.000 0.152 0.248
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 6 0.0000 0.922 0.000 0.000 0.000 0.000 0.000 1.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.2969 0.756 0.776 0.000 0.000 0.000 0.224 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.2398 0.856 0.876 0.020 0.000 0.000 0.104 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.2412 0.852 0.880 0.000 0.028 0.000 0.092 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.3866 0.739 0.000 0.800 0.112 0.000 0.060 0.028
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 5 0.6537 0.485 0.060 0.124 0.236 0.000 0.560 0.020
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.7500 0.167 0.432 0.268 0.132 0.148 0.020 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.2135 0.773 0.128 0.000 0.872 0.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 6 0.5070 0.472 0.000 0.300 0.056 0.000 0.024 0.620
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0363 0.880 0.012 0.988 0.000 0.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.2398 0.856 0.876 0.020 0.000 0.000 0.104 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.1204 0.855 0.056 0.944 0.000 0.000 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0363 0.880 0.012 0.988 0.000 0.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> F325847E-F046-4B67-B01C-16919C401020 5 0.5697 0.355 0.004 0.012 0.128 0.000 0.564 0.292
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0363 0.880 0.012 0.988 0.000 0.000 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.5702 0.426 0.000 0.012 0.576 0.000 0.204 0.208
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 5 0.3269 0.742 0.184 0.024 0.000 0.000 0.792 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.3266 0.593 0.000 0.272 0.728 0.000 0.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.1863 0.856 0.896 0.000 0.000 0.000 0.104 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 6 0.0000 0.922 0.000 0.000 0.000 0.000 0.000 1.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 5 0.5741 0.361 0.016 0.028 0.064 0.000 0.568 0.324
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0260 0.873 0.000 0.992 0.000 0.000 0.008 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.2398 0.856 0.876 0.020 0.000 0.000 0.104 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.2629 0.801 0.040 0.000 0.000 0.000 0.868 0.092
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.837 1.000 0.000 0.000 0.000 0.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.5699 0.540 0.000 0.628 0.216 0.000 0.076 0.080
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.1267 0.861 0.060 0.940 0.000 0.000 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.3449 0.698 0.000 0.196 0.780 0.000 0.016 0.008
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.0547 0.851 0.020 0.000 0.000 0.000 0.980 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 6 0.0000 0.922 0.000 0.000 0.000 0.000 0.000 1.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.2135 0.771 0.128 0.000 0.000 0.000 0.872 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.0363 0.847 0.000 0.012 0.000 0.000 0.988 0.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.0508 0.851 0.012 0.004 0.000 0.000 0.984 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 5 0.3955 0.313 0.436 0.004 0.000 0.000 0.560 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 6 0.0000 0.922 0.000 0.000 0.000 0.000 0.000 1.000
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.1225 0.840 0.036 0.012 0.000 0.000 0.952 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.0820 0.874 0.012 0.972 0.000 0.000 0.016 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.837 1.000 0.000 0.000 0.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 6 0.2092 0.816 0.000 0.124 0.000 0.000 0.000 0.876
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.0146 0.850 0.004 0.000 0.000 0.000 0.996 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.0547 0.850 0.020 0.000 0.000 0.000 0.980 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.2398 0.856 0.876 0.020 0.000 0.000 0.104 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.3565 0.527 0.000 0.304 0.692 0.000 0.004 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0363 0.880 0.012 0.988 0.000 0.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.4253 0.687 0.732 0.160 0.000 0.000 0.108 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.0405 0.851 0.008 0.004 0.000 0.000 0.988 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0363 0.880 0.012 0.988 0.000 0.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.1616 0.842 0.028 0.012 0.000 0.000 0.940 0.020
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0937 0.821 0.960 0.000 0.040 0.000 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0363 0.880 0.012 0.988 0.000 0.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.1572 0.856 0.028 0.936 0.000 0.000 0.036 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.837 1.000 0.000 0.000 0.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.2380 0.831 0.892 0.068 0.004 0.000 0.036 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0146 0.880 0.000 0.000 0.996 0.000 0.004 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.0790 0.848 0.032 0.000 0.000 0.000 0.968 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0363 0.880 0.012 0.988 0.000 0.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.1908 0.829 0.096 0.000 0.900 0.000 0.004 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 6 0.4931 0.694 0.020 0.012 0.140 0.000 0.108 0.720
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.4224 0.166 0.432 0.000 0.552 0.000 0.016 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.837 1.000 0.000 0.000 0.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.2360 0.832 0.044 0.012 0.900 0.000 0.044 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0363 0.880 0.012 0.988 0.000 0.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.5592 0.097 0.000 0.476 0.412 0.000 0.012 0.100
#> 53A96249-66D5-4C26-893B-ADC71481D261 5 0.3938 0.496 0.000 0.324 0.000 0.000 0.660 0.016
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.2398 0.856 0.876 0.020 0.000 0.000 0.104 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.875 0.000 1.000 0.000 0.000 0.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0520 0.877 0.008 0.984 0.000 0.000 0.008 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.0458 0.850 0.016 0.000 0.000 0.000 0.984 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.0260 0.851 0.008 0.000 0.000 0.000 0.992 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.1714 0.858 0.908 0.000 0.000 0.000 0.092 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 5 0.3718 0.757 0.108 0.012 0.012 0.000 0.816 0.052
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.2135 0.845 0.872 0.000 0.000 0.000 0.128 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.0260 0.851 0.008 0.000 0.000 0.000 0.992 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.3817 0.338 0.568 0.000 0.000 0.000 0.432 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.1863 0.856 0.896 0.000 0.000 0.000 0.104 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.4135 0.265 0.000 0.584 0.004 0.000 0.008 0.404
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.2664 0.702 0.816 0.184 0.000 0.000 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 6 0.0000 0.922 0.000 0.000 0.000 0.000 0.000 1.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0363 0.880 0.012 0.988 0.000 0.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.4316 0.399 0.312 0.648 0.000 0.000 0.040 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.837 1.000 0.000 0.000 0.000 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.1245 0.855 0.000 0.952 0.032 0.000 0.016 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.1204 0.856 0.944 0.000 0.000 0.000 0.056 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.1663 0.821 0.000 0.912 0.000 0.000 0.088 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.0508 0.851 0.012 0.004 0.000 0.000 0.984 0.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.1151 0.868 0.012 0.956 0.032 0.000 0.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.0717 0.847 0.000 0.016 0.000 0.000 0.976 0.008
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.2136 0.835 0.000 0.012 0.908 0.000 0.016 0.064
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 6 0.0000 0.922 0.000 0.000 0.000 0.000 0.000 1.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.2048 0.796 0.000 0.120 0.880 0.000 0.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0260 0.879 0.000 0.000 0.992 0.000 0.008 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 5 0.4269 0.662 0.092 0.184 0.000 0.000 0.724 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 6 0.0291 0.917 0.000 0.004 0.000 0.000 0.004 0.992
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0363 0.880 0.012 0.988 0.000 0.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.0972 0.846 0.028 0.000 0.000 0.000 0.964 0.008
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 6 0.0000 0.922 0.000 0.000 0.000 0.000 0.000 1.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "mclust"]
# you can also extract it by
# res = res_list["CV:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.246 0.668 0.805 0.3388 0.801 0.801
#> 3 3 0.538 0.858 0.889 0.6233 0.635 0.555
#> 4 4 0.827 0.909 0.946 0.2919 0.793 0.569
#> 5 5 0.792 0.674 0.861 0.0633 0.966 0.881
#> 6 6 0.710 0.579 0.740 0.0476 0.921 0.712
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.8713 0.589 0.708 0.292
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 1 0.2948 0.753 0.948 0.052
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 1 0.0376 0.760 0.996 0.004
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.0000 0.761 1.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.2043 0.758 0.968 0.032
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.5059 0.737 0.888 0.112
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.5059 0.737 0.888 0.112
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 1 0.4022 0.743 0.920 0.080
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.5408 0.738 0.876 0.124
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.9833 0.397 0.576 0.424
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.7219 0.725 0.800 0.200
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.0376 0.760 0.996 0.004
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 1 0.5178 0.739 0.884 0.116
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 2 0.9970 -0.221 0.468 0.532
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.5294 0.733 0.880 0.120
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 1 0.5294 0.738 0.880 0.120
#> DC55EE78-203F-4092-9B83-14B1A529194B 1 0.4815 0.741 0.896 0.104
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.9795 0.412 0.584 0.416
#> F772EA39-E408-4908-BADD-C786D702BF9B 1 0.0376 0.760 0.996 0.004
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 2 0.4022 0.916 0.080 0.920
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 1 0.0000 0.761 1.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 1 0.5059 0.737 0.888 0.112
#> F325847E-F046-4B67-B01C-16919C401020 1 0.6623 0.734 0.828 0.172
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.5294 0.733 0.880 0.120
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 1 0.0000 0.761 1.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 2 0.4022 0.916 0.080 0.920
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 1 0.7376 0.721 0.792 0.208
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.9286 0.526 0.656 0.344
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.5408 0.731 0.876 0.124
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.9833 0.397 0.576 0.424
#> A8E48877-F8AB-44DD-A18B-194D87C44931 1 0.5178 0.725 0.884 0.116
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.5059 0.737 0.888 0.112
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 1 0.7883 0.671 0.764 0.236
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.9833 0.397 0.576 0.424
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.9775 0.418 0.588 0.412
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 2 0.9286 0.316 0.344 0.656
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 2 0.4022 0.916 0.080 0.920
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.5408 0.731 0.876 0.124
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 1 0.5059 0.737 0.888 0.112
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 1 0.0000 0.761 1.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.5408 0.731 0.876 0.124
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 2 0.4022 0.916 0.080 0.920
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 1 0.5408 0.736 0.876 0.124
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 2 0.4022 0.916 0.080 0.920
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.9833 0.397 0.576 0.424
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.5629 0.725 0.868 0.132
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 1 0.5294 0.723 0.880 0.120
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.5408 0.731 0.876 0.124
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.8608 0.593 0.716 0.284
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 1 0.1843 0.759 0.972 0.028
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.9087 0.549 0.676 0.324
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.9815 0.405 0.580 0.420
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.6343 0.698 0.840 0.160
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.9754 0.438 0.592 0.408
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.0000 0.761 1.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.9909 0.311 0.556 0.444
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 2 0.4022 0.916 0.080 0.920
#> A54731AE-FC40-407F-8D10-67DDC122237D 1 0.0376 0.760 0.996 0.004
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.7602 0.716 0.780 0.220
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.9795 0.411 0.584 0.416
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.9833 0.397 0.576 0.424
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.5408 0.731 0.876 0.124
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 1 0.0000 0.761 1.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.9833 0.397 0.576 0.424
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.3274 0.751 0.940 0.060
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 1 0.0000 0.761 1.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.8763 0.570 0.704 0.296
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.5946 0.740 0.856 0.144
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 2 0.4022 0.916 0.080 0.920
#> EE16D845-31F2-4178-800B-CA2C358841AD 1 0.0000 0.761 1.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 1 0.0376 0.760 0.996 0.004
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.9944 0.305 0.544 0.456
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.8499 0.595 0.724 0.276
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.6531 0.689 0.832 0.168
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.6887 0.672 0.816 0.184
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 2 0.4022 0.916 0.080 0.920
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.8661 0.586 0.712 0.288
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 1 0.0000 0.761 1.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.5178 0.735 0.884 0.116
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.5178 0.735 0.884 0.116
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.8661 0.656 0.712 0.288
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.9896 0.360 0.560 0.440
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.4022 0.916 0.080 0.920
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.5294 0.733 0.880 0.120
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 2 0.4022 0.916 0.080 0.920
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 1 0.0000 0.761 1.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 1 0.5059 0.737 0.888 0.112
#> 53A96249-66D5-4C26-893B-ADC71481D261 1 0.3879 0.752 0.924 0.076
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.9833 0.397 0.576 0.424
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 1 0.4815 0.741 0.896 0.104
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 1 0.0000 0.761 1.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.7299 0.721 0.796 0.204
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.9795 0.412 0.584 0.416
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.9522 0.483 0.628 0.372
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.9833 0.397 0.576 0.424
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.8861 0.629 0.696 0.304
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.8327 0.614 0.736 0.264
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.9000 0.560 0.684 0.316
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.9000 0.559 0.684 0.316
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.9833 0.397 0.576 0.424
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 1 0.3274 0.754 0.940 0.060
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.9815 0.405 0.580 0.420
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 2 0.4022 0.916 0.080 0.920
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 1 0.4562 0.736 0.904 0.096
#> 2D962371-EC83-490C-A663-478AF383BC1B 1 0.0000 0.761 1.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.0376 0.760 0.996 0.004
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 1 0.5408 0.731 0.876 0.124
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.9970 0.286 0.532 0.468
#> A60DC925-7343-496E-900D-0DD81D5C8123 1 0.8386 0.631 0.732 0.268
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.9815 0.405 0.580 0.420
#> 6969B6B2-7616-4664-9696-C4DACD10537B 1 0.0000 0.761 1.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.6247 0.701 0.844 0.156
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 2 0.4022 0.916 0.080 0.920
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.5408 0.731 0.876 0.124
#> D47D0433-2313-4A2F-B268-5AD293D7534E 1 0.4939 0.739 0.892 0.108
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.0938 0.760 0.988 0.012
#> 2629FEE3-A203-4411-8A70-02A796C9505C 1 0.5408 0.736 0.876 0.124
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 1 0.2423 0.757 0.960 0.040
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.5408 0.731 0.876 0.124
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.5842 0.718 0.860 0.140
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.5519 0.728 0.872 0.128
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 1 0.2043 0.759 0.968 0.032
#> A608BCEB-2C27-4927-A308-E6975F641722 1 0.0376 0.760 0.996 0.004
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 1 0.0376 0.760 0.996 0.004
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.9000 0.558 0.684 0.316
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 1 0.5408 0.721 0.876 0.124
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 2 0.1170 0.866 0.008 0.976 0.016
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.4790 0.860 0.056 0.848 0.096
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.5564 0.845 0.064 0.808 0.128
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.7337 0.794 0.152 0.708 0.140
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 2 0.3692 0.868 0.056 0.896 0.048
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.1289 0.928 0.000 0.032 0.968
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.1753 0.912 0.000 0.048 0.952
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.4628 0.862 0.056 0.856 0.088
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.1289 0.928 0.000 0.032 0.968
#> 5482053D-9F48-4773-B68A-302B3A612503 2 0.0983 0.862 0.016 0.980 0.004
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.6057 0.470 0.004 0.340 0.656
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.5375 0.845 0.056 0.816 0.128
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.2356 0.890 0.000 0.072 0.928
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 2 0.4136 0.844 0.020 0.864 0.116
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.1289 0.928 0.000 0.032 0.968
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.1289 0.928 0.000 0.032 0.968
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.8290 0.701 0.164 0.632 0.204
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 2 0.0983 0.862 0.016 0.980 0.004
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.5810 0.843 0.072 0.796 0.132
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.2384 1.000 0.936 0.008 0.056
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.8309 0.711 0.180 0.632 0.188
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.1031 0.928 0.000 0.024 0.976
#> F325847E-F046-4B67-B01C-16919C401020 3 0.1411 0.926 0.000 0.036 0.964
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.1289 0.928 0.000 0.032 0.968
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.8072 0.743 0.208 0.648 0.144
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.2384 1.000 0.936 0.008 0.056
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.1289 0.928 0.000 0.032 0.968
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 2 0.2774 0.861 0.008 0.920 0.072
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.0424 0.922 0.000 0.008 0.992
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 2 0.1129 0.861 0.020 0.976 0.004
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.3337 0.870 0.060 0.908 0.032
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.2959 0.853 0.000 0.100 0.900
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.8290 0.701 0.164 0.632 0.204
#> 91BA5F90-9174-4533-A050-39A28E34A94D 2 0.0983 0.862 0.016 0.980 0.004
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 2 0.0475 0.865 0.004 0.992 0.004
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 2 0.2486 0.846 0.060 0.932 0.008
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.2384 1.000 0.936 0.008 0.056
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0592 0.925 0.000 0.012 0.988
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.0592 0.925 0.000 0.012 0.988
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.6714 0.820 0.112 0.748 0.140
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.1031 0.928 0.000 0.024 0.976
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.2384 1.000 0.936 0.008 0.056
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.1031 0.928 0.000 0.024 0.976
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.2384 1.000 0.936 0.008 0.056
#> CB207A52-09AC-49D3-8240-5840CDFBB154 2 0.0829 0.863 0.012 0.984 0.004
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.0424 0.922 0.000 0.008 0.992
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.3237 0.870 0.056 0.912 0.032
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0892 0.928 0.000 0.020 0.980
#> 5E343116-414B-41F2-AAEE-A3225450135A 2 0.0475 0.866 0.004 0.992 0.004
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.4945 0.858 0.056 0.840 0.104
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 2 0.0237 0.866 0.000 0.996 0.004
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 2 0.1129 0.861 0.020 0.976 0.004
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 2 0.1919 0.871 0.024 0.956 0.020
#> AD294665-6F90-459C-90D5-3058F210225D 2 0.0661 0.864 0.008 0.988 0.004
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.6705 0.820 0.108 0.748 0.144
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 2 0.1989 0.848 0.048 0.948 0.004
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.2384 1.000 0.936 0.008 0.056
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.5334 0.849 0.060 0.820 0.120
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 2 0.4413 0.841 0.008 0.832 0.160
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 2 0.0475 0.865 0.004 0.992 0.004
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 2 0.1129 0.861 0.020 0.976 0.004
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.0424 0.922 0.000 0.008 0.992
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.8030 0.747 0.204 0.652 0.144
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 2 0.0829 0.863 0.012 0.984 0.004
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 2 0.3816 0.849 0.000 0.852 0.148
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.8030 0.747 0.204 0.652 0.144
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 2 0.1315 0.869 0.008 0.972 0.020
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.4062 0.769 0.000 0.164 0.836
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.2384 1.000 0.936 0.008 0.056
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.7888 0.758 0.196 0.664 0.140
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.6349 0.830 0.092 0.768 0.140
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 2 0.1647 0.854 0.036 0.960 0.004
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 2 0.1482 0.866 0.012 0.968 0.020
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0424 0.922 0.000 0.008 0.992
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0424 0.922 0.000 0.008 0.992
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.2384 1.000 0.936 0.008 0.056
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 2 0.2356 0.870 0.000 0.928 0.072
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.7944 0.755 0.196 0.660 0.144
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.1289 0.928 0.000 0.032 0.968
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.1289 0.928 0.000 0.032 0.968
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.6427 0.446 0.012 0.348 0.640
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 2 0.1267 0.860 0.024 0.972 0.004
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.2384 1.000 0.936 0.008 0.056
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.1289 0.928 0.000 0.032 0.968
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.2384 1.000 0.936 0.008 0.056
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.7853 0.762 0.188 0.668 0.144
#> 322AF320-1379-4F51-AFDC-5292A060CD52 3 0.8475 0.221 0.112 0.320 0.568
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.5852 0.833 0.060 0.788 0.152
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 2 0.1267 0.860 0.024 0.972 0.004
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.8521 0.671 0.164 0.608 0.228
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.8167 0.736 0.212 0.640 0.148
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.0747 0.926 0.000 0.016 0.984
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 2 0.0661 0.865 0.008 0.988 0.004
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 2 0.2063 0.863 0.008 0.948 0.044
#> C2662596-6E2F-4924-B051-CEA1AC87B197 2 0.0829 0.863 0.012 0.984 0.004
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 2 0.4261 0.848 0.012 0.848 0.140
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.0983 0.868 0.016 0.980 0.004
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 2 0.0237 0.866 0.000 0.996 0.004
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 2 0.0237 0.866 0.000 0.996 0.004
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 2 0.1129 0.861 0.020 0.976 0.004
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.6192 0.819 0.060 0.764 0.176
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 2 0.0983 0.863 0.016 0.980 0.004
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.2384 1.000 0.936 0.008 0.056
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.5117 0.860 0.060 0.832 0.108
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.8030 0.747 0.204 0.652 0.144
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.5137 0.857 0.064 0.832 0.104
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0424 0.922 0.000 0.008 0.992
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 2 0.1267 0.860 0.024 0.972 0.004
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.8290 0.701 0.164 0.632 0.204
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 2 0.1129 0.862 0.020 0.976 0.004
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.7888 0.758 0.196 0.664 0.140
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 2 0.1765 0.868 0.040 0.956 0.004
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.2384 1.000 0.936 0.008 0.056
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0424 0.922 0.000 0.008 0.992
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.8543 0.665 0.160 0.604 0.236
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 2 0.5020 0.857 0.056 0.836 0.108
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.1031 0.928 0.000 0.024 0.976
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.4790 0.859 0.056 0.848 0.096
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.0892 0.927 0.000 0.020 0.980
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0424 0.922 0.000 0.008 0.992
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.1289 0.928 0.000 0.032 0.968
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.2550 0.868 0.056 0.932 0.012
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.5334 0.849 0.060 0.820 0.120
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.6714 0.820 0.112 0.748 0.140
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 2 0.0237 0.866 0.000 0.996 0.004
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.4642 0.864 0.060 0.856 0.084
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.0188 0.985 0.996 0.000 0.004 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.3569 0.842 0.196 0.804 0.000 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.3266 0.852 0.168 0.832 0.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0000 0.849 0.000 1.000 0.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 2 0.4222 0.769 0.272 0.728 0.000 0.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.1867 0.901 0.072 0.000 0.928 0.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.3009 0.875 0.052 0.056 0.892 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.3569 0.842 0.196 0.804 0.000 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.1637 0.913 0.060 0.000 0.940 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.3726 0.734 0.212 0.000 0.788 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.3311 0.851 0.172 0.828 0.000 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.3833 0.830 0.080 0.072 0.848 0.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0707 0.969 0.980 0.000 0.020 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0336 0.846 0.000 0.992 0.008 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.1940 0.856 0.076 0.924 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 0.999 0.000 0.000 0.000 1.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.849 0.000 1.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> F325847E-F046-4B67-B01C-16919C401020 3 0.1792 0.905 0.068 0.000 0.932 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.849 0.000 1.000 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 0.999 0.000 0.000 0.000 1.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.1792 0.905 0.068 0.000 0.932 0.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0188 0.986 0.996 0.000 0.004 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.3764 0.829 0.216 0.784 0.000 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.2149 0.886 0.088 0.000 0.912 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.849 0.000 1.000 0.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 0.999 0.000 0.000 0.000 1.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.849 0.000 1.000 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 0.999 0.000 0.000 0.000 1.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0188 0.996 0.000 0.000 0.004 0.996
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.3688 0.835 0.208 0.792 0.000 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.0592 0.972 0.984 0.016 0.000 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.3569 0.842 0.196 0.804 0.000 0.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 2 0.4830 0.569 0.392 0.608 0.000 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.0000 0.849 0.000 1.000 0.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0000 0.999 0.000 0.000 0.000 1.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.3266 0.852 0.168 0.832 0.000 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.0469 0.978 0.988 0.000 0.012 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.849 0.000 1.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 2 0.4431 0.725 0.304 0.696 0.000 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.849 0.000 1.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.2149 0.879 0.088 0.000 0.912 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 0.999 0.000 0.000 0.000 1.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.849 0.000 1.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.1557 0.856 0.056 0.944 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0188 0.996 0.000 0.000 0.004 0.996
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.2760 0.835 0.872 0.000 0.128 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.849 0.000 1.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.4543 0.543 0.324 0.000 0.676 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0000 0.999 0.000 0.000 0.000 1.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.1302 0.923 0.044 0.000 0.956 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0000 0.999 0.000 0.000 0.000 1.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.849 0.000 1.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.3172 0.744 0.000 0.840 0.160 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.3725 0.847 0.180 0.812 0.008 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0336 0.846 0.000 0.992 0.008 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.849 0.000 1.000 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.0188 0.986 0.996 0.000 0.004 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.0188 0.986 0.996 0.000 0.004 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.2760 0.818 0.872 0.128 0.000 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.3355 0.853 0.160 0.836 0.004 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 0.999 0.000 0.000 0.000 1.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.3569 0.842 0.196 0.804 0.000 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.849 0.000 1.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.3688 0.832 0.208 0.792 0.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.2342 0.795 0.000 0.912 0.008 0.080
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.849 0.000 1.000 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 2 0.4981 0.391 0.464 0.536 0.000 0.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 0.999 0.000 0.000 0.000 1.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0592 0.842 0.000 0.984 0.016 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 2 0.3610 0.841 0.200 0.800 0.000 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.3569 0.842 0.196 0.804 0.000 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.4222 0.770 0.272 0.728 0.000 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.3172 0.853 0.160 0.840 0.000 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.849 0.000 1.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.3688 0.835 0.208 0.792 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.1282 0.9083 0.952 0.000 0.004 0.000 0.044
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.5680 0.0863 0.080 0.492 0.000 0.000 0.428
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.5476 0.1602 0.068 0.544 0.000 0.000 0.388
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0000 0.5278 0.000 1.000 0.000 0.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 2 0.5856 0.0167 0.096 0.464 0.000 0.000 0.440
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.2074 0.8760 0.044 0.000 0.920 0.000 0.036
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.2072 0.8861 0.020 0.036 0.928 0.000 0.016
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.5810 0.0532 0.092 0.480 0.000 0.000 0.428
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.2962 0.8508 0.084 0.000 0.868 0.000 0.048
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.9198 1.000 0.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.5535 0.2892 0.392 0.000 0.536 0.000 0.072
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.5877 0.1022 0.076 0.500 0.008 0.000 0.416
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.4729 0.6856 0.048 0.140 0.768 0.000 0.044
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.1168 0.8987 0.960 0.000 0.032 0.000 0.008
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0880 0.9115 0.000 0.000 0.968 0.000 0.032
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.0162 0.9150 0.000 0.000 0.996 0.000 0.004
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.4446 -0.0328 0.000 0.520 0.004 0.000 0.476
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0000 0.9198 1.000 0.000 0.000 0.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.2006 0.5165 0.012 0.916 0.000 0.000 0.072
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 0.9989 0.000 0.000 0.000 1.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.4249 0.0108 0.000 0.568 0.000 0.000 0.432
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.0162 0.9147 0.000 0.000 0.996 0.000 0.004
#> F325847E-F046-4B67-B01C-16919C401020 3 0.2795 0.8626 0.056 0.000 0.880 0.000 0.064
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0880 0.9115 0.000 0.000 0.968 0.000 0.032
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.5278 0.000 1.000 0.000 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 0.9989 0.000 0.000 0.000 1.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.2438 0.8758 0.040 0.000 0.900 0.000 0.060
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.1121 0.9088 0.956 0.000 0.000 0.000 0.044
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.1121 0.9117 0.000 0.000 0.956 0.000 0.044
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.9198 1.000 0.000 0.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.5933 -0.0335 0.104 0.448 0.000 0.000 0.448
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.2554 0.8555 0.072 0.000 0.892 0.000 0.036
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.2233 0.4616 0.000 0.892 0.004 0.000 0.104
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.9198 1.000 0.000 0.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.0880 0.9141 0.968 0.000 0.000 0.000 0.032
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.9198 1.000 0.000 0.000 0.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 0.9989 0.000 0.000 0.000 1.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.1341 0.9136 0.000 0.000 0.944 0.000 0.056
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.0703 0.9144 0.000 0.000 0.976 0.000 0.024
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.2020 0.5070 0.000 0.900 0.000 0.000 0.100
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0880 0.9123 0.000 0.000 0.968 0.000 0.032
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 0.9989 0.000 0.000 0.000 1.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.0404 0.9148 0.000 0.000 0.988 0.000 0.012
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0162 0.9947 0.000 0.000 0.004 0.996 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.0290 0.9190 0.992 0.000 0.000 0.000 0.008
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.1121 0.9117 0.000 0.000 0.956 0.000 0.044
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.5896 -0.0168 0.100 0.452 0.000 0.000 0.448
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0609 0.9153 0.000 0.000 0.980 0.000 0.020
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.3534 0.6637 0.744 0.000 0.000 0.000 0.256
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.5891 0.0188 0.100 0.468 0.000 0.000 0.432
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.1671 0.8865 0.924 0.000 0.000 0.000 0.076
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0000 0.9198 1.000 0.000 0.000 0.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.5812 -0.0784 0.476 0.092 0.000 0.000 0.432
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.0963 0.9126 0.964 0.000 0.000 0.000 0.036
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.2020 0.5067 0.000 0.900 0.000 0.000 0.100
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.9198 1.000 0.000 0.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0000 0.9989 0.000 0.000 0.000 1.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.3215 0.4667 0.056 0.852 0.000 0.000 0.092
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.2125 0.8872 0.920 0.004 0.024 0.000 0.052
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0703 0.9171 0.976 0.000 0.000 0.000 0.024
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.9198 1.000 0.000 0.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.1121 0.9117 0.000 0.000 0.956 0.000 0.044
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.5278 0.000 1.000 0.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0000 0.9198 1.000 0.000 0.000 0.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.6898 0.1448 0.300 0.296 0.004 0.000 0.400
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.5278 0.000 1.000 0.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.0963 0.9126 0.964 0.000 0.000 0.000 0.036
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.4451 0.6241 0.248 0.000 0.712 0.000 0.040
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 0.9989 0.000 0.000 0.000 1.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.5278 0.000 1.000 0.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.2036 0.5147 0.024 0.920 0.000 0.000 0.056
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.9198 1.000 0.000 0.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0404 0.9185 0.988 0.000 0.000 0.000 0.012
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.1121 0.9117 0.000 0.000 0.956 0.000 0.044
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.1121 0.9117 0.000 0.000 0.956 0.000 0.044
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0162 0.9947 0.000 0.000 0.004 0.996 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.4254 0.6124 0.740 0.000 0.220 0.000 0.040
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.5278 0.000 1.000 0.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0794 0.9117 0.000 0.000 0.972 0.000 0.028
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0880 0.9115 0.000 0.000 0.968 0.000 0.032
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.4718 0.4967 0.344 0.000 0.628 0.000 0.028
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.9198 1.000 0.000 0.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0000 0.9989 0.000 0.000 0.000 1.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.1981 0.8936 0.028 0.000 0.924 0.000 0.048
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0000 0.9989 0.000 0.000 0.000 1.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.5278 0.000 1.000 0.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 5 0.5178 -0.2757 0.000 0.476 0.040 0.000 0.484
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.6474 0.0712 0.084 0.500 0.036 0.000 0.380
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.9198 1.000 0.000 0.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.4446 -0.0328 0.000 0.520 0.004 0.000 0.476
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.1478 0.4978 0.000 0.936 0.000 0.000 0.064
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.0865 0.9149 0.004 0.000 0.972 0.000 0.024
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0880 0.9141 0.968 0.000 0.000 0.000 0.032
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.1270 0.9042 0.948 0.000 0.000 0.000 0.052
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.9198 1.000 0.000 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.1281 0.9109 0.956 0.000 0.012 0.000 0.032
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.3990 0.5512 0.688 0.004 0.000 0.000 0.308
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.1792 0.8804 0.916 0.000 0.000 0.000 0.084
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.2648 0.8097 0.848 0.000 0.000 0.000 0.152
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.9198 1.000 0.000 0.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.3584 0.4727 0.040 0.848 0.028 0.000 0.084
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.9198 1.000 0.000 0.000 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 0.9989 0.000 0.000 0.000 1.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.5847 0.0455 0.096 0.480 0.000 0.000 0.424
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.5278 0.000 1.000 0.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.4402 0.3838 0.056 0.740 0.000 0.000 0.204
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.1121 0.9117 0.000 0.000 0.956 0.000 0.044
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.9198 1.000 0.000 0.000 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.5113 0.2574 0.000 0.724 0.020 0.172 0.084
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.9198 1.000 0.000 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.5278 0.000 1.000 0.000 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.5382 0.2770 0.580 0.068 0.000 0.000 0.352
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 0.9989 0.000 0.000 0.000 1.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.1544 0.9127 0.000 0.000 0.932 0.000 0.068
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.4644 -0.0274 0.000 0.528 0.012 0.000 0.460
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 2 0.5836 0.0887 0.100 0.516 0.000 0.000 0.384
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.0609 0.9150 0.000 0.000 0.980 0.000 0.020
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.5680 0.0863 0.080 0.492 0.000 0.000 0.428
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.1197 0.9131 0.000 0.000 0.952 0.000 0.048
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.1121 0.9117 0.000 0.000 0.956 0.000 0.044
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0963 0.9110 0.000 0.000 0.964 0.000 0.036
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 5 0.6469 -0.0396 0.184 0.380 0.000 0.000 0.436
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.3215 0.4667 0.056 0.852 0.000 0.000 0.092
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0703 0.5270 0.000 0.976 0.000 0.000 0.024
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.1043 0.9114 0.960 0.000 0.000 0.000 0.040
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.5933 -0.0237 0.104 0.452 0.000 0.000 0.444
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.1930 0.83077 0.916 0.000 0.000 0.000 0.048 0.036
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.5469 0.43840 0.064 0.484 0.000 0.000 0.024 0.428
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.4825 0.50621 0.036 0.560 0.000 0.000 0.012 0.392
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.1863 0.56989 0.000 0.896 0.000 0.000 0.000 0.104
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 2 0.5831 0.40442 0.076 0.468 0.000 0.000 0.040 0.416
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.5572 -0.36174 0.004 0.028 0.520 0.000 0.388 0.060
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.6564 0.03787 0.000 0.216 0.528 0.000 0.176 0.080
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.5531 0.43655 0.064 0.484 0.000 0.000 0.028 0.424
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 5 0.3782 0.84118 0.000 0.000 0.412 0.000 0.588 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0865 0.82668 0.964 0.000 0.000 0.000 0.036 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.6260 0.07476 0.496 0.000 0.228 0.000 0.252 0.024
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.5390 0.46415 0.044 0.504 0.012 0.000 0.016 0.424
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.5506 -0.40215 0.004 0.028 0.504 0.000 0.412 0.052
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.2401 0.80544 0.892 0.000 0.060 0.000 0.044 0.004
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 5 0.3810 0.85108 0.000 0.000 0.428 0.000 0.572 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.4411 -0.24065 0.000 0.004 0.612 0.000 0.356 0.028
#> DC55EE78-203F-4092-9B83-14B1A529194B 6 0.4666 0.34656 0.000 0.388 0.000 0.000 0.048 0.564
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.2772 0.80866 0.816 0.000 0.000 0.000 0.180 0.004
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.3076 0.59430 0.000 0.760 0.000 0.000 0.000 0.240
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 0.99875 0.000 0.000 0.000 1.000 0.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 6 0.4401 0.30558 0.000 0.464 0.000 0.000 0.024 0.512
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.3101 0.37750 0.000 0.000 0.756 0.000 0.244 0.000
#> F325847E-F046-4B67-B01C-16919C401020 5 0.4256 0.80154 0.012 0.000 0.420 0.000 0.564 0.004
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 5 0.3810 0.85108 0.000 0.000 0.428 0.000 0.572 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0632 0.51072 0.000 0.976 0.000 0.000 0.000 0.024
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 0.99875 0.000 0.000 0.000 1.000 0.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 5 0.4032 0.82025 0.008 0.000 0.420 0.000 0.572 0.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.2402 0.81974 0.896 0.000 0.012 0.000 0.032 0.060
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.0000 0.65891 0.000 0.000 1.000 0.000 0.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0935 0.82853 0.964 0.000 0.000 0.000 0.032 0.004
#> A8E48877-F8AB-44DD-A18B-194D87C44931 6 0.6652 -0.31501 0.092 0.380 0.000 0.000 0.108 0.420
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.6358 -0.14871 0.184 0.000 0.516 0.000 0.256 0.044
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.4332 -0.23974 0.000 0.564 0.004 0.000 0.016 0.416
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.2738 0.81140 0.820 0.000 0.000 0.000 0.176 0.004
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.3213 0.81365 0.808 0.000 0.000 0.000 0.160 0.032
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.1010 0.82535 0.960 0.000 0.000 0.000 0.036 0.004
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0291 0.99305 0.004 0.000 0.000 0.992 0.000 0.004
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.2527 0.52064 0.000 0.000 0.832 0.000 0.168 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.1958 0.60933 0.000 0.000 0.896 0.000 0.100 0.004
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.2378 0.59205 0.000 0.848 0.000 0.000 0.000 0.152
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.3221 0.34628 0.000 0.000 0.736 0.000 0.264 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 0.99875 0.000 0.000 0.000 1.000 0.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.1387 0.64282 0.000 0.000 0.932 0.000 0.068 0.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 0.99875 0.000 0.000 0.000 1.000 0.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.2595 0.81810 0.836 0.000 0.000 0.000 0.160 0.004
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.0000 0.65891 0.000 0.000 1.000 0.000 0.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 6 0.6855 -0.26083 0.112 0.360 0.000 0.000 0.116 0.412
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.2378 0.57059 0.000 0.000 0.848 0.000 0.152 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.5219 0.62017 0.552 0.000 0.000 0.000 0.340 0.108
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.5780 0.39810 0.076 0.464 0.000 0.000 0.036 0.424
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.4368 0.76004 0.708 0.000 0.000 0.000 0.204 0.088
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.2703 0.81108 0.824 0.000 0.000 0.000 0.172 0.004
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.6298 0.40917 0.512 0.044 0.000 0.000 0.156 0.288
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.1492 0.82953 0.940 0.000 0.000 0.000 0.024 0.036
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.2491 0.59385 0.000 0.836 0.000 0.000 0.000 0.164
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.1010 0.82535 0.960 0.000 0.000 0.000 0.036 0.004
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0000 0.99875 0.000 0.000 0.000 1.000 0.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.3927 0.56493 0.040 0.752 0.000 0.000 0.008 0.200
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.3830 0.76489 0.812 0.000 0.056 0.000 0.052 0.080
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.1498 0.83570 0.940 0.000 0.000 0.000 0.028 0.032
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.2597 0.81072 0.824 0.000 0.000 0.000 0.176 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.0000 0.65891 0.000 0.000 1.000 0.000 0.000 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0632 0.51072 0.000 0.976 0.000 0.000 0.000 0.024
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0146 0.83183 0.996 0.000 0.000 0.000 0.004 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 6 0.7632 -0.16006 0.208 0.328 0.052 0.000 0.052 0.360
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0632 0.51072 0.000 0.976 0.000 0.000 0.000 0.024
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.3517 0.80590 0.820 0.000 0.012 0.000 0.072 0.096
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 5 0.6054 0.22108 0.304 0.000 0.284 0.000 0.412 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 0.99875 0.000 0.000 0.000 1.000 0.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0713 0.51924 0.000 0.972 0.000 0.000 0.000 0.028
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.3023 0.59642 0.004 0.784 0.000 0.000 0.000 0.212
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.1010 0.82535 0.960 0.000 0.000 0.000 0.036 0.004
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0891 0.82972 0.968 0.000 0.000 0.000 0.024 0.008
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0000 0.65891 0.000 0.000 1.000 0.000 0.000 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0000 0.65891 0.000 0.000 1.000 0.000 0.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 0.99875 0.000 0.000 0.000 1.000 0.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.3497 0.77895 0.832 0.000 0.084 0.000 0.048 0.036
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0632 0.51072 0.000 0.976 0.000 0.000 0.000 0.024
#> E5557F52-015D-49DC-9E23-989FC259976F 5 0.3810 0.85108 0.000 0.000 0.428 0.000 0.572 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 5 0.3810 0.85108 0.000 0.000 0.428 0.000 0.572 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.6397 -0.36020 0.372 0.000 0.300 0.000 0.316 0.012
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0865 0.82668 0.964 0.000 0.000 0.000 0.036 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0146 0.99688 0.000 0.000 0.000 0.996 0.000 0.004
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 5 0.3797 0.84753 0.000 0.000 0.420 0.000 0.580 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0146 0.99688 0.000 0.000 0.000 0.996 0.000 0.004
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0260 0.52920 0.000 0.992 0.000 0.000 0.000 0.008
#> 322AF320-1379-4F51-AFDC-5292A060CD52 6 0.6096 0.32779 0.000 0.288 0.152 0.000 0.032 0.528
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.5444 0.46007 0.048 0.500 0.012 0.000 0.016 0.424
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.2378 0.81781 0.848 0.000 0.000 0.000 0.152 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 6 0.4666 0.34656 0.000 0.388 0.000 0.000 0.048 0.564
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.3853 0.00888 0.000 0.680 0.000 0.000 0.016 0.304
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.1897 0.62847 0.004 0.000 0.908 0.000 0.084 0.004
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.3388 0.80720 0.792 0.000 0.000 0.000 0.172 0.036
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.1863 0.82613 0.920 0.000 0.000 0.000 0.036 0.044
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0865 0.82668 0.964 0.000 0.000 0.000 0.036 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.3445 0.79405 0.832 0.000 0.096 0.000 0.036 0.036
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.5185 0.62501 0.564 0.000 0.000 0.000 0.328 0.108
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.3746 0.79653 0.780 0.000 0.000 0.000 0.140 0.080
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.4795 0.67754 0.604 0.000 0.000 0.000 0.324 0.072
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.2178 0.82269 0.868 0.000 0.000 0.000 0.132 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.4060 0.57124 0.020 0.728 0.008 0.000 0.008 0.236
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0935 0.83502 0.964 0.000 0.000 0.000 0.032 0.004
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 0.99875 0.000 0.000 0.000 1.000 0.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.5576 0.42960 0.068 0.480 0.000 0.000 0.028 0.424
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0632 0.51072 0.000 0.976 0.000 0.000 0.000 0.024
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.4453 0.56142 0.012 0.660 0.000 0.000 0.032 0.296
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0000 0.65891 0.000 0.000 1.000 0.000 0.000 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0865 0.82668 0.964 0.000 0.000 0.000 0.036 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 6 0.6989 0.27591 0.000 0.376 0.128 0.072 0.016 0.408
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0865 0.82668 0.964 0.000 0.000 0.000 0.036 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.1075 0.53041 0.000 0.952 0.000 0.000 0.000 0.048
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.5884 0.51835 0.496 0.008 0.000 0.000 0.324 0.172
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 0.99875 0.000 0.000 0.000 1.000 0.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.2219 0.55166 0.000 0.000 0.864 0.000 0.136 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 6 0.4620 0.32934 0.000 0.420 0.004 0.000 0.032 0.544
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 2 0.5766 0.46210 0.076 0.528 0.004 0.000 0.032 0.360
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.1141 0.64893 0.000 0.000 0.948 0.000 0.052 0.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.5463 0.44525 0.064 0.492 0.000 0.000 0.024 0.420
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.3847 -0.57476 0.000 0.000 0.544 0.000 0.456 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0000 0.65891 0.000 0.000 1.000 0.000 0.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 5 0.3810 0.85108 0.000 0.000 0.428 0.000 0.572 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 6 0.7350 -0.02624 0.284 0.228 0.000 0.000 0.120 0.368
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.3604 0.56565 0.036 0.788 0.000 0.000 0.008 0.168
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.2631 0.59368 0.000 0.820 0.000 0.000 0.000 0.180
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.2706 0.82896 0.860 0.000 0.000 0.000 0.104 0.036
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 6 0.6944 -0.22309 0.136 0.344 0.000 0.000 0.108 0.412
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "NMF"]
# you can also extract it by
# res = res_list["CV:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.679 0.889 0.921 0.4783 0.517 0.517
#> 3 3 0.650 0.838 0.898 0.3807 0.706 0.490
#> 4 4 0.663 0.651 0.812 0.1082 0.883 0.685
#> 5 5 0.878 0.850 0.939 0.0803 0.830 0.489
#> 6 6 0.801 0.756 0.870 0.0427 0.908 0.618
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.4431 0.933 0.908 0.092
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.0000 0.900 0.000 1.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.4161 0.905 0.084 0.916
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.4562 0.899 0.096 0.904
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 2 0.5294 0.816 0.120 0.880
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.8608 0.727 0.716 0.284
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 2 0.0672 0.898 0.008 0.992
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0000 0.900 0.000 1.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.4690 0.932 0.900 0.100
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0672 0.921 0.992 0.008
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.4298 0.933 0.912 0.088
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.0376 0.899 0.004 0.996
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.0376 0.899 0.004 0.996
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.924 1.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.4562 0.933 0.904 0.096
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.0672 0.898 0.008 0.992
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.4161 0.905 0.084 0.916
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0672 0.921 0.992 0.008
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.4431 0.901 0.092 0.908
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.0000 0.924 1.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.4161 0.905 0.084 0.916
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 1 0.9044 0.665 0.680 0.320
#> F325847E-F046-4B67-B01C-16919C401020 1 0.5737 0.915 0.864 0.136
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.4562 0.933 0.904 0.096
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.4431 0.901 0.092 0.908
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.0000 0.924 1.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 1 0.7883 0.805 0.764 0.236
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.4431 0.933 0.908 0.092
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.4562 0.933 0.904 0.096
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0376 0.923 0.996 0.004
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.0000 0.900 0.000 1.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.5629 0.917 0.868 0.132
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.4161 0.905 0.084 0.916
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0672 0.921 0.992 0.008
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.5294 0.925 0.880 0.120
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0376 0.923 0.996 0.004
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.0000 0.924 1.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.4562 0.933 0.904 0.096
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 1 0.5178 0.927 0.884 0.116
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.5294 0.885 0.120 0.880
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.4562 0.933 0.904 0.096
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.0000 0.924 1.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.6148 0.778 0.152 0.848
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.0000 0.924 1.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.3431 0.933 0.936 0.064
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.4562 0.933 0.904 0.096
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.0000 0.900 0.000 1.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.4562 0.933 0.904 0.096
#> 5E343116-414B-41F2-AAEE-A3225450135A 2 0.9358 0.381 0.352 0.648
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.0376 0.899 0.004 0.996
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.7219 0.852 0.800 0.200
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0000 0.924 1.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 2 0.7453 0.690 0.212 0.788
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.5294 0.925 0.880 0.120
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.4161 0.905 0.084 0.916
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0672 0.921 0.992 0.008
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0000 0.924 1.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.900 0.000 1.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.5408 0.923 0.876 0.124
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.5294 0.925 0.880 0.120
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0672 0.921 0.992 0.008
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.4431 0.933 0.908 0.092
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.4161 0.905 0.084 0.916
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0000 0.924 1.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.7139 0.856 0.804 0.196
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.4161 0.905 0.084 0.916
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.5519 0.920 0.872 0.128
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.4298 0.933 0.912 0.088
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.0000 0.924 1.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.4431 0.901 0.092 0.908
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.4431 0.901 0.092 0.908
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.924 1.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0376 0.923 0.996 0.004
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.5178 0.927 0.884 0.116
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.4562 0.933 0.904 0.096
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.0000 0.924 1.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.4815 0.931 0.896 0.104
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.4161 0.905 0.084 0.916
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.4562 0.933 0.904 0.096
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.5178 0.927 0.884 0.116
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0376 0.923 0.996 0.004
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0376 0.923 0.996 0.004
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.0000 0.924 1.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.5294 0.925 0.880 0.120
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.0000 0.924 1.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.4161 0.905 0.084 0.916
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0938 0.896 0.012 0.988
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0000 0.900 0.000 1.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0672 0.921 0.992 0.008
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.3431 0.906 0.064 0.936
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.4161 0.905 0.084 0.916
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.4431 0.933 0.908 0.092
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.5178 0.927 0.884 0.116
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.4939 0.930 0.892 0.108
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.924 1.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.4562 0.933 0.904 0.096
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.8813 0.707 0.300 0.700
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.5629 0.917 0.868 0.132
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 2 0.9983 0.211 0.476 0.524
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0672 0.921 0.992 0.008
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.900 0.000 1.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0672 0.921 0.992 0.008
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.0000 0.924 1.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.0000 0.900 0.000 1.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.4161 0.905 0.084 0.916
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.4562 0.899 0.096 0.904
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 1 0.4690 0.932 0.900 0.100
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.924 1.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 1.0000 0.226 0.496 0.504
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0672 0.921 0.992 0.008
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.4161 0.905 0.084 0.916
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 2 0.5294 0.815 0.120 0.880
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.0000 0.924 1.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.4431 0.933 0.908 0.092
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.3431 0.906 0.064 0.936
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 2 0.2236 0.885 0.036 0.964
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.6973 0.724 0.188 0.812
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0000 0.900 0.000 1.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.4562 0.933 0.904 0.096
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.4562 0.933 0.904 0.096
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.5178 0.927 0.884 0.116
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.5059 0.890 0.112 0.888
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.900 0.000 1.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.4161 0.905 0.084 0.916
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.5294 0.925 0.880 0.120
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.0376 0.899 0.004 0.996
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.6192 0.177 0.420 0.000 0.580
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.1585 0.935 0.008 0.964 0.028
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.950 0.000 1.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0000 0.950 0.000 1.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.5307 0.816 0.056 0.124 0.820
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.2434 0.868 0.036 0.024 0.940
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.4062 0.795 0.000 0.164 0.836
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.1315 0.941 0.008 0.972 0.020
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.1643 0.864 0.044 0.000 0.956
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.3896 0.886 0.864 0.008 0.128
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.2625 0.844 0.084 0.000 0.916
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.0892 0.944 0.000 0.980 0.020
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.3375 0.842 0.008 0.100 0.892
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 3 0.5968 0.476 0.364 0.000 0.636
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.1643 0.864 0.044 0.000 0.956
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.3816 0.809 0.000 0.148 0.852
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.950 0.000 1.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.3896 0.886 0.864 0.008 0.128
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0237 0.948 0.004 0.996 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.2063 0.834 0.948 0.008 0.044
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.950 0.000 1.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.1647 0.869 0.004 0.036 0.960
#> F325847E-F046-4B67-B01C-16919C401020 3 0.1647 0.867 0.036 0.004 0.960
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.1529 0.866 0.040 0.000 0.960
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.950 0.000 1.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.2063 0.834 0.948 0.008 0.044
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.1647 0.867 0.036 0.004 0.960
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 3 0.1643 0.874 0.044 0.000 0.956
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.0747 0.872 0.016 0.000 0.984
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.3896 0.886 0.864 0.008 0.128
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.1711 0.932 0.008 0.960 0.032
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.1399 0.870 0.028 0.004 0.968
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.950 0.000 1.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.3896 0.886 0.864 0.008 0.128
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 3 0.5058 0.683 0.244 0.000 0.756
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.3715 0.886 0.868 0.004 0.128
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.2063 0.834 0.948 0.008 0.044
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.1289 0.868 0.032 0.000 0.968
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.3425 0.847 0.112 0.004 0.884
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.5247 0.687 0.224 0.768 0.008
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0747 0.871 0.016 0.000 0.984
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.2063 0.834 0.948 0.008 0.044
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.3851 0.814 0.004 0.136 0.860
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.4963 0.676 0.792 0.008 0.200
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.4235 0.819 0.824 0.000 0.176
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.3116 0.849 0.108 0.000 0.892
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.1711 0.932 0.008 0.960 0.032
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0747 0.872 0.016 0.000 0.984
#> 5E343116-414B-41F2-AAEE-A3225450135A 2 0.8346 0.258 0.360 0.548 0.092
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.6189 0.472 0.004 0.364 0.632
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 3 0.4974 0.803 0.236 0.000 0.764
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.3715 0.886 0.868 0.004 0.128
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 2 0.6940 0.646 0.068 0.708 0.224
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.3686 0.848 0.140 0.000 0.860
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.0000 0.950 0.000 1.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.3896 0.886 0.864 0.008 0.128
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.2866 0.815 0.916 0.008 0.076
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0592 0.947 0.000 0.988 0.012
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.3482 0.838 0.128 0.000 0.872
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.4796 0.745 0.220 0.000 0.780
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.3896 0.886 0.864 0.008 0.128
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.1643 0.869 0.044 0.000 0.956
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.950 0.000 1.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.3551 0.884 0.868 0.000 0.132
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.3482 0.838 0.128 0.000 0.872
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.950 0.000 1.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 3 0.3551 0.837 0.132 0.000 0.868
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.6111 0.267 0.396 0.000 0.604
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.2063 0.834 0.948 0.008 0.044
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.950 0.000 1.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.950 0.000 1.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.3715 0.886 0.868 0.004 0.128
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.3965 0.884 0.860 0.008 0.132
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.3412 0.840 0.124 0.000 0.876
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.3482 0.841 0.128 0.000 0.872
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.5958 0.486 0.692 0.008 0.300
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 3 0.1643 0.867 0.044 0.000 0.956
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.950 0.000 1.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.1529 0.866 0.040 0.000 0.960
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.1643 0.864 0.044 0.000 0.956
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.6683 0.209 0.500 0.008 0.492
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.3715 0.886 0.868 0.004 0.128
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.1950 0.835 0.952 0.008 0.040
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.1289 0.868 0.032 0.000 0.968
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.1950 0.835 0.952 0.008 0.040
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.950 0.000 1.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 3 0.5237 0.817 0.120 0.056 0.824
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0424 0.948 0.000 0.992 0.008
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.3896 0.886 0.864 0.008 0.128
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.950 0.000 1.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.950 0.000 1.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.2356 0.864 0.072 0.000 0.928
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 3 0.4555 0.739 0.200 0.000 0.800
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.5178 0.690 0.256 0.000 0.744
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.3896 0.886 0.864 0.008 0.128
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.3551 0.864 0.132 0.000 0.868
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.5588 0.849 0.808 0.068 0.124
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 3 0.3482 0.838 0.128 0.000 0.872
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.7898 0.539 0.616 0.300 0.084
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.3896 0.886 0.864 0.008 0.128
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0592 0.947 0.000 0.988 0.012
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.3896 0.886 0.864 0.008 0.128
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.1950 0.835 0.952 0.008 0.040
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.1129 0.942 0.004 0.976 0.020
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.950 0.000 1.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.1753 0.918 0.048 0.952 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.1529 0.869 0.040 0.000 0.960
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.3715 0.886 0.868 0.004 0.128
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.5944 0.762 0.088 0.792 0.120
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.3896 0.886 0.864 0.008 0.128
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.950 0.000 1.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 2 0.7453 0.651 0.152 0.700 0.148
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.1950 0.835 0.952 0.008 0.040
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0592 0.872 0.012 0.000 0.988
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.950 0.000 1.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 3 0.4139 0.839 0.124 0.016 0.860
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.3610 0.837 0.016 0.096 0.888
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0829 0.946 0.004 0.984 0.012
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.1529 0.866 0.040 0.000 0.960
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.3340 0.845 0.120 0.000 0.880
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.1529 0.866 0.040 0.000 0.960
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.1453 0.933 0.024 0.968 0.008
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0829 0.946 0.004 0.984 0.012
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.950 0.000 1.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.2356 0.868 0.072 0.000 0.928
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.2063 0.922 0.008 0.948 0.044
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.6897 0.5750 0.168 0.000 0.588 0.244
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.4933 0.5985 0.000 0.688 0.016 0.296
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0524 0.8713 0.008 0.988 0.000 0.004
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0469 0.8702 0.012 0.988 0.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.5769 0.6438 0.000 0.056 0.652 0.292
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.4049 0.7094 0.000 0.008 0.780 0.212
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.2546 0.7297 0.000 0.008 0.900 0.092
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.4770 0.6094 0.000 0.700 0.012 0.288
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.4360 0.6967 0.008 0.000 0.744 0.248
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0188 0.8988 0.996 0.004 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.5250 0.6305 0.176 0.000 0.744 0.080
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.0927 0.8593 0.000 0.976 0.016 0.008
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.4228 0.7004 0.000 0.008 0.760 0.232
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 3 0.7841 -0.1877 0.356 0.000 0.380 0.264
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.3249 0.7269 0.008 0.000 0.852 0.140
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.3051 0.7305 0.000 0.028 0.884 0.088
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0336 0.8726 0.008 0.992 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0188 0.8988 0.996 0.004 0.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.1302 0.8468 0.044 0.956 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.4456 0.6592 0.280 0.004 0.000 0.716
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0336 0.8726 0.008 0.992 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.0000 0.7195 0.000 0.000 1.000 0.000
#> F325847E-F046-4B67-B01C-16919C401020 3 0.4621 0.6748 0.000 0.008 0.708 0.284
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.1452 0.7265 0.008 0.000 0.956 0.036
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0336 0.8726 0.008 0.992 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.4456 0.6592 0.280 0.004 0.000 0.716
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.4621 0.6748 0.000 0.008 0.708 0.284
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 3 0.3625 0.5842 0.012 0.000 0.828 0.160
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.0524 0.7159 0.008 0.000 0.988 0.004
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0188 0.8988 0.996 0.004 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.7786 0.0412 0.000 0.416 0.256 0.328
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.3300 0.7276 0.000 0.008 0.848 0.144
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0336 0.8726 0.008 0.992 0.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0188 0.8988 0.996 0.004 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 3 0.5345 0.5703 0.004 0.008 0.584 0.404
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.8966 1.000 0.000 0.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.4456 0.6592 0.280 0.004 0.000 0.716
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0188 0.7186 0.004 0.000 0.996 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.0469 0.7156 0.000 0.000 0.988 0.012
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 1 0.4431 0.5469 0.696 0.304 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0336 0.7173 0.008 0.000 0.992 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.4456 0.6592 0.280 0.004 0.000 0.716
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.1452 0.7267 0.000 0.008 0.956 0.036
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.5334 0.6412 0.236 0.004 0.044 0.716
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.6398 0.1349 0.576 0.000 0.080 0.344
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.4122 0.4836 0.004 0.000 0.760 0.236
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.7763 0.0598 0.000 0.432 0.264 0.304
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0336 0.7173 0.008 0.000 0.992 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 4 0.9858 -0.0530 0.276 0.272 0.168 0.284
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.6592 0.5907 0.000 0.116 0.600 0.284
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 4 0.5229 -0.1971 0.008 0.008 0.336 0.648
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0188 0.8974 0.996 0.000 0.004 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 3 0.7459 0.4505 0.004 0.160 0.484 0.352
#> AD294665-6F90-459C-90D5-3058F210225D 4 0.4814 -0.0891 0.000 0.008 0.316 0.676
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.0336 0.8726 0.008 0.992 0.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0188 0.8988 0.996 0.004 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.4718 0.6571 0.272 0.004 0.008 0.716
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.2124 0.8262 0.000 0.924 0.008 0.068
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.4955 0.4266 0.000 0.008 0.648 0.344
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.5447 0.4924 0.004 0.008 0.528 0.460
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0188 0.8988 0.996 0.004 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.2048 0.6775 0.008 0.000 0.928 0.064
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0336 0.8726 0.008 0.992 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0469 0.8890 0.988 0.000 0.012 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.4539 0.6822 0.000 0.008 0.720 0.272
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0336 0.8726 0.008 0.992 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 4 0.4917 -0.1454 0.000 0.008 0.336 0.656
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.5408 0.0597 0.488 0.000 0.500 0.012
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.4456 0.6592 0.280 0.004 0.000 0.716
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0336 0.8726 0.008 0.992 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0336 0.8726 0.008 0.992 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0336 0.8886 0.992 0.000 0.000 0.008
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0336 0.8948 0.992 0.000 0.008 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.3610 0.5398 0.000 0.000 0.800 0.200
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.4304 0.4103 0.000 0.000 0.716 0.284
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.5843 0.5949 0.156 0.004 0.124 0.716
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 3 0.4799 0.6733 0.004 0.008 0.704 0.284
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0336 0.8726 0.008 0.992 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.2342 0.7300 0.008 0.000 0.912 0.080
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.3649 0.7115 0.000 0.000 0.796 0.204
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.3610 0.6408 0.800 0.000 0.200 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0188 0.8974 0.996 0.000 0.004 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.4456 0.6592 0.280 0.004 0.000 0.716
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0817 0.7260 0.000 0.000 0.976 0.024
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.4456 0.6592 0.280 0.004 0.000 0.716
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0336 0.8726 0.008 0.992 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 3 0.7753 -0.0700 0.000 0.256 0.432 0.312
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.1452 0.8473 0.000 0.956 0.008 0.036
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0188 0.8988 0.996 0.004 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0336 0.8726 0.008 0.992 0.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0336 0.8726 0.008 0.992 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.0592 0.7136 0.000 0.000 0.984 0.016
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 3 0.6945 0.5951 0.120 0.008 0.588 0.284
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.5170 0.6477 0.008 0.008 0.660 0.324
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0188 0.8974 0.996 0.000 0.004 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.4574 0.6752 0.016 0.008 0.768 0.208
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.1724 0.8586 0.948 0.032 0.000 0.020
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 3 0.4697 0.6680 0.000 0.008 0.696 0.296
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.4911 0.4917 0.704 0.008 0.008 0.280
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0188 0.8988 0.996 0.004 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0336 0.8659 0.000 0.992 0.000 0.008
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0188 0.8988 0.996 0.004 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.4456 0.6592 0.280 0.004 0.000 0.716
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.6340 0.4989 0.000 0.620 0.096 0.284
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0336 0.8726 0.008 0.992 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.4331 0.5666 0.712 0.288 0.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0524 0.7159 0.008 0.000 0.988 0.004
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0188 0.8974 0.996 0.000 0.004 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.7698 0.2524 0.036 0.536 0.116 0.312
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0188 0.8988 0.996 0.004 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0336 0.8726 0.008 0.992 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.8691 0.1943 0.032 0.328 0.328 0.312
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.4456 0.6592 0.280 0.004 0.000 0.716
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0336 0.7173 0.008 0.000 0.992 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0336 0.8681 0.000 0.992 0.008 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 4 0.6499 -0.3763 0.000 0.076 0.400 0.524
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.1042 0.7253 0.000 0.008 0.972 0.020
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.4158 0.6852 0.000 0.768 0.008 0.224
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.0336 0.7173 0.008 0.000 0.992 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.4193 0.4373 0.000 0.000 0.732 0.268
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.3074 0.7255 0.000 0.000 0.848 0.152
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.4543 0.4452 0.324 0.676 0.000 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.2480 0.8115 0.000 0.904 0.008 0.088
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0336 0.8726 0.008 0.992 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.5130 0.6317 0.004 0.008 0.644 0.344
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.7917 0.1934 0.000 0.340 0.348 0.312
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 5 0.5271 0.6333 0.168 0.000 0.152 0.000 0.680
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 5 0.0000 0.9075 0.000 0.000 0.000 0.000 1.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.0324 0.9053 0.000 0.000 0.004 0.004 0.992
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 5 0.4161 0.3469 0.000 0.000 0.392 0.000 0.608
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.2648 0.7990 0.000 0.000 0.848 0.000 0.152
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 5 0.0000 0.9075 0.000 0.000 0.000 0.000 1.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 5 0.3274 0.7017 0.000 0.000 0.220 0.000 0.780
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.4219 0.2830 0.584 0.000 0.416 0.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.0162 0.9454 0.000 0.996 0.000 0.000 0.004
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.4300 0.0513 0.000 0.000 0.524 0.000 0.476
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 3 0.2719 0.7963 0.144 0.000 0.852 0.004 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.2471 0.8159 0.000 0.000 0.864 0.000 0.136
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.1908 0.8596 0.000 0.000 0.908 0.000 0.092
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0162 0.9562 0.004 0.000 0.000 0.996 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.0000 0.9193 0.000 0.000 1.000 0.000 0.000
#> F325847E-F046-4B67-B01C-16919C401020 5 0.2690 0.7816 0.000 0.000 0.156 0.000 0.844
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.9193 0.000 0.000 1.000 0.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0162 0.9562 0.004 0.000 0.000 0.996 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 5 0.0609 0.8982 0.000 0.000 0.020 0.000 0.980
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 3 0.1197 0.8888 0.048 0.000 0.952 0.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.0000 0.9193 0.000 0.000 1.000 0.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 5 0.0000 0.9075 0.000 0.000 0.000 0.000 1.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.3796 0.5793 0.000 0.000 0.700 0.000 0.300
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.0000 0.9075 0.000 0.000 0.000 0.000 1.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 0.9532 0.000 0.000 0.000 1.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0000 0.9193 0.000 0.000 1.000 0.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.0162 0.9188 0.000 0.000 0.996 0.004 0.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 1 0.4114 0.3692 0.624 0.376 0.000 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0000 0.9193 0.000 0.000 1.000 0.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0162 0.9562 0.004 0.000 0.000 0.996 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.1043 0.8988 0.000 0.000 0.960 0.000 0.040
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 0.9532 0.000 0.000 0.000 1.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.5319 0.2680 0.012 0.000 0.032 0.400 0.556
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.0162 0.9188 0.000 0.000 0.996 0.004 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 5 0.0000 0.9075 0.000 0.000 0.000 0.000 1.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0000 0.9193 0.000 0.000 1.000 0.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.0000 0.9075 0.000 0.000 0.000 0.000 1.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.0162 0.9060 0.000 0.004 0.000 0.000 0.996
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.0324 0.9053 0.000 0.000 0.004 0.004 0.992
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 5 0.0000 0.9075 0.000 0.000 0.000 0.000 1.000
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.0290 0.9044 0.000 0.000 0.000 0.008 0.992
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0162 0.9562 0.004 0.000 0.000 0.996 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.3274 0.7112 0.000 0.780 0.000 0.000 0.220
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.3835 0.6494 0.000 0.000 0.732 0.008 0.260
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.0000 0.9075 0.000 0.000 0.000 0.000 1.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.0000 0.9193 0.000 0.000 1.000 0.000 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0162 0.9214 0.996 0.000 0.004 0.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.2011 0.8453 0.000 0.000 0.088 0.004 0.908
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.0000 0.9075 0.000 0.000 0.000 0.000 1.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.4192 0.3154 0.596 0.000 0.404 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0162 0.9562 0.004 0.000 0.000 0.996 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0162 0.9188 0.000 0.000 0.996 0.004 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0162 0.9188 0.000 0.000 0.996 0.004 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0162 0.9562 0.004 0.000 0.000 0.996 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.0404 0.9029 0.000 0.000 0.012 0.000 0.988
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0290 0.9168 0.000 0.000 0.992 0.000 0.008
#> F3135F5E-2E90-4923-B634-E994563D17B7 5 0.4088 0.4250 0.000 0.000 0.368 0.000 0.632
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0404 0.9153 0.988 0.000 0.012 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0162 0.9562 0.004 0.000 0.000 0.996 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.9193 0.000 0.000 1.000 0.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0162 0.9562 0.004 0.000 0.000 0.996 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 3 0.3491 0.6651 0.000 0.228 0.768 0.004 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.2020 0.8525 0.000 0.900 0.000 0.000 0.100
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.0162 0.9188 0.000 0.000 0.996 0.004 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.0324 0.9053 0.000 0.000 0.004 0.004 0.992
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.1430 0.8756 0.000 0.000 0.052 0.004 0.944
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 4 0.6309 0.1599 0.000 0.000 0.368 0.472 0.160
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.0162 0.9213 0.996 0.004 0.000 0.000 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.0162 0.9065 0.000 0.000 0.000 0.004 0.996
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 5 0.4219 0.2907 0.416 0.000 0.000 0.000 0.584
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0162 0.9562 0.004 0.000 0.000 0.996 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 5 0.0000 0.9075 0.000 0.000 0.000 0.000 1.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.3274 0.6971 0.780 0.220 0.000 0.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0000 0.9193 0.000 0.000 1.000 0.000 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.1205 0.9082 0.000 0.956 0.040 0.004 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.0000 0.9075 0.000 0.000 0.000 0.000 1.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0162 0.9562 0.004 0.000 0.000 0.996 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.9193 0.000 0.000 1.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.0613 0.9021 0.000 0.008 0.004 0.004 0.984
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.0510 0.9136 0.000 0.000 0.984 0.000 0.016
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 5 0.0000 0.9075 0.000 0.000 0.000 0.000 1.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.0000 0.9193 0.000 0.000 1.000 0.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0162 0.9188 0.000 0.000 0.996 0.004 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0703 0.9087 0.000 0.000 0.976 0.000 0.024
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.4291 0.1329 0.464 0.536 0.000 0.000 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.3932 0.5382 0.000 0.672 0.000 0.000 0.328
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.9487 0.000 1.000 0.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.0000 0.9075 0.000 0.000 0.000 0.000 1.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 5 0.0000 0.9075 0.000 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.5167 0.5747 0.140 0.000 0.668 0.000 0.172 0.020
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 5 0.2219 0.7151 0.000 0.000 0.000 0.000 0.864 0.136
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.1141 0.8967 0.000 0.948 0.000 0.000 0.000 0.052
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0508 0.9005 0.004 0.984 0.000 0.000 0.000 0.012
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.3965 0.3534 0.000 0.008 0.000 0.000 0.604 0.388
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 5 0.4023 0.6550 0.000 0.000 0.100 0.000 0.756 0.144
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 6 0.5633 0.4938 0.000 0.000 0.196 0.000 0.272 0.532
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 5 0.2048 0.7221 0.000 0.000 0.000 0.000 0.880 0.120
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.2679 0.7831 0.032 0.000 0.868 0.000 0.096 0.004
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.9372 1.000 0.000 0.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.2320 0.7411 0.132 0.000 0.864 0.000 0.000 0.004
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.5361 0.2065 0.000 0.512 0.000 0.000 0.372 0.116
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.3854 0.7168 0.000 0.016 0.788 0.000 0.056 0.140
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 6 0.5373 0.5749 0.216 0.000 0.196 0.000 0.000 0.588
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.1493 0.8174 0.004 0.000 0.936 0.000 0.056 0.004
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.1995 0.8115 0.000 0.000 0.912 0.000 0.036 0.052
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.1007 0.8982 0.000 0.956 0.000 0.000 0.000 0.044
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0000 0.9372 1.000 0.000 0.000 0.000 0.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.1007 0.8982 0.000 0.956 0.000 0.000 0.000 0.044
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0146 0.9020 0.000 0.996 0.000 0.000 0.000 0.004
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.0458 0.8277 0.000 0.000 0.984 0.000 0.000 0.016
#> F325847E-F046-4B67-B01C-16919C401020 3 0.4025 0.3603 0.000 0.000 0.576 0.000 0.416 0.008
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.8260 0.000 0.000 1.000 0.000 0.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0458 0.9005 0.000 0.984 0.000 0.000 0.000 0.016
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 5 0.5361 -0.0599 0.000 0.000 0.444 0.000 0.448 0.108
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 3 0.4453 0.1495 0.036 0.000 0.592 0.000 0.000 0.372
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.1663 0.7829 0.000 0.000 0.912 0.000 0.000 0.088
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.9372 1.000 0.000 0.000 0.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 5 0.1957 0.7248 0.000 0.000 0.000 0.000 0.888 0.112
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.2553 0.7638 0.000 0.000 0.848 0.000 0.144 0.008
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0547 0.8998 0.000 0.980 0.000 0.000 0.000 0.020
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.9372 1.000 0.000 0.000 0.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.0405 0.7332 0.000 0.000 0.004 0.000 0.988 0.008
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.1124 0.9293 0.956 0.000 0.008 0.000 0.000 0.036
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0000 0.8260 0.000 0.000 1.000 0.000 0.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 6 0.3244 0.7025 0.000 0.000 0.268 0.000 0.000 0.732
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.4117 0.6172 0.256 0.704 0.004 0.000 0.000 0.036
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0632 0.8227 0.000 0.000 0.976 0.000 0.000 0.024
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.4340 0.5915 0.000 0.000 0.720 0.000 0.176 0.104
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.6027 0.2754 0.004 0.000 0.036 0.360 0.504 0.096
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 6 0.3695 0.5538 0.000 0.000 0.376 0.000 0.000 0.624
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 5 0.1957 0.7248 0.000 0.000 0.000 0.000 0.888 0.112
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0458 0.8248 0.000 0.000 0.984 0.000 0.000 0.016
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.1075 0.7197 0.000 0.000 0.000 0.000 0.952 0.048
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.4375 0.6037 0.000 0.020 0.152 0.000 0.748 0.080
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.3672 0.3940 0.000 0.000 0.000 0.000 0.632 0.368
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.1124 0.9293 0.956 0.000 0.008 0.000 0.000 0.036
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 5 0.1910 0.7253 0.000 0.000 0.000 0.000 0.892 0.108
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.0520 0.7316 0.000 0.000 0.000 0.008 0.984 0.008
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.0717 0.8979 0.000 0.976 0.008 0.000 0.000 0.016
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0865 0.9306 0.964 0.000 0.000 0.000 0.000 0.036
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.5440 0.3909 0.000 0.556 0.000 0.000 0.288 0.156
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 6 0.3602 0.6360 0.000 0.000 0.056 0.000 0.160 0.784
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.0405 0.7319 0.000 0.000 0.004 0.000 0.988 0.008
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.9372 1.000 0.000 0.000 0.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.3151 0.5346 0.000 0.000 0.748 0.000 0.000 0.252
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0363 0.9012 0.000 0.988 0.000 0.000 0.000 0.012
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.1492 0.9177 0.940 0.000 0.024 0.000 0.000 0.036
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 6 0.4196 0.3785 0.000 0.000 0.028 0.000 0.332 0.640
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0146 0.9020 0.000 0.996 0.000 0.000 0.000 0.004
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.0260 0.7319 0.000 0.000 0.000 0.000 0.992 0.008
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.3094 0.7044 0.140 0.000 0.824 0.000 0.000 0.036
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0363 0.9014 0.000 0.988 0.000 0.000 0.000 0.012
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0713 0.9007 0.000 0.972 0.000 0.000 0.000 0.028
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.1225 0.9272 0.952 0.000 0.012 0.000 0.000 0.036
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.9372 1.000 0.000 0.000 0.000 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 6 0.3175 0.7091 0.000 0.000 0.256 0.000 0.000 0.744
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.2854 0.6169 0.000 0.000 0.792 0.000 0.000 0.208
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.4798 0.3744 0.000 0.000 0.312 0.000 0.612 0.076
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.1141 0.8967 0.000 0.948 0.000 0.000 0.000 0.052
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0632 0.8270 0.000 0.000 0.976 0.000 0.024 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.2480 0.7816 0.000 0.000 0.872 0.000 0.104 0.024
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.2941 0.6545 0.780 0.000 0.220 0.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.1124 0.9293 0.956 0.000 0.008 0.000 0.000 0.036
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0547 0.8239 0.000 0.000 0.980 0.000 0.000 0.020
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.1141 0.8967 0.000 0.948 0.000 0.000 0.000 0.052
#> 322AF320-1379-4F51-AFDC-5292A060CD52 6 0.4131 0.6971 0.000 0.100 0.156 0.000 0.000 0.744
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.3943 0.7403 0.000 0.760 0.000 0.000 0.084 0.156
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.9372 1.000 0.000 0.000 0.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.1141 0.8967 0.000 0.948 0.000 0.000 0.000 0.052
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0458 0.9005 0.000 0.984 0.000 0.000 0.000 0.016
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.2100 0.7587 0.000 0.000 0.884 0.000 0.004 0.112
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.3320 0.6013 0.016 0.000 0.000 0.000 0.772 0.212
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.3509 0.5723 0.000 0.000 0.016 0.000 0.744 0.240
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.9372 1.000 0.000 0.000 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 5 0.7271 0.0124 0.000 0.000 0.292 0.108 0.376 0.224
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.0000 0.9372 1.000 0.000 0.000 0.000 0.000 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.3747 0.3383 0.000 0.000 0.000 0.000 0.604 0.396
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.3955 0.3027 0.608 0.000 0.000 0.000 0.384 0.008
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.9372 1.000 0.000 0.000 0.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.1556 0.8828 0.000 0.920 0.000 0.000 0.000 0.080
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.1124 0.9293 0.956 0.000 0.008 0.000 0.000 0.036
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 5 0.2048 0.7221 0.000 0.000 0.000 0.000 0.880 0.120
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0363 0.9012 0.000 0.988 0.000 0.000 0.000 0.012
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.2416 0.7646 0.844 0.156 0.000 0.000 0.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0363 0.8253 0.000 0.000 0.988 0.000 0.000 0.012
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.1124 0.9293 0.956 0.000 0.008 0.000 0.000 0.036
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.3333 0.7122 0.000 0.784 0.024 0.000 0.000 0.192
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.9372 1.000 0.000 0.000 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0458 0.9005 0.000 0.984 0.000 0.000 0.000 0.016
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.1910 0.6918 0.000 0.000 0.000 0.000 0.892 0.108
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0458 0.8248 0.000 0.000 0.984 0.000 0.000 0.016
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0363 0.9005 0.000 0.988 0.012 0.000 0.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 6 0.3831 0.4689 0.000 0.012 0.008 0.000 0.268 0.712
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.3175 0.6997 0.000 0.000 0.808 0.000 0.164 0.028
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 5 0.2558 0.7006 0.000 0.004 0.000 0.000 0.840 0.156
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.0000 0.8260 0.000 0.000 1.000 0.000 0.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 6 0.3351 0.6853 0.000 0.000 0.288 0.000 0.000 0.712
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0777 0.8277 0.000 0.000 0.972 0.000 0.024 0.004
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.4209 0.7427 0.124 0.756 0.008 0.000 0.000 0.112
#> A608BCEB-2C27-4927-A308-E6975F641722 5 0.5482 0.3433 0.000 0.300 0.000 0.000 0.544 0.156
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.1141 0.8967 0.000 0.948 0.000 0.000 0.000 0.052
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.0291 0.7328 0.000 0.000 0.004 0.000 0.992 0.004
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 5 0.1957 0.7248 0.000 0.000 0.000 0.000 0.888 0.112
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "hclust"]
# you can also extract it by
# res = res_list["MAD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.308 0.772 0.877 0.4065 0.643 0.643
#> 3 3 0.292 0.469 0.684 0.4567 0.682 0.515
#> 4 4 0.350 0.352 0.643 0.1770 0.696 0.395
#> 5 5 0.429 0.427 0.628 0.0702 0.773 0.442
#> 6 6 0.451 0.351 0.591 0.0429 0.880 0.593
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.4298 0.8305 0.912 0.088
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 1 0.9129 0.5951 0.672 0.328
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.3733 0.8666 0.072 0.928
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.7883 0.6630 0.236 0.764
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.0938 0.8486 0.988 0.012
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.9552 0.5413 0.624 0.376
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.9552 0.5413 0.624 0.376
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 1 0.9129 0.5951 0.672 0.328
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.2603 0.8510 0.956 0.044
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.8465 1.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.6973 0.7708 0.812 0.188
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.9732 0.1775 0.404 0.596
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 1 0.9460 0.5267 0.636 0.364
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.6048 0.7850 0.852 0.148
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.2778 0.8506 0.952 0.048
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 1 0.9922 0.3489 0.552 0.448
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.8819 0.000 1.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.2948 0.8455 0.948 0.052
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.1184 0.8795 0.016 0.984
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.4815 0.8324 0.896 0.104
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.8819 0.000 1.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 1 0.9580 0.4915 0.620 0.380
#> F325847E-F046-4B67-B01C-16919C401020 1 0.7219 0.7664 0.800 0.200
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.2778 0.8506 0.952 0.048
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.8819 0.000 1.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.4815 0.8324 0.896 0.104
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 1 0.6973 0.7748 0.812 0.188
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.6531 0.7682 0.832 0.168
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.5946 0.7955 0.856 0.144
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.1843 0.8472 0.972 0.028
#> A8E48877-F8AB-44DD-A18B-194D87C44931 1 0.9129 0.5951 0.672 0.328
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.2603 0.8518 0.956 0.044
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.4562 0.8516 0.096 0.904
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.8465 1.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.1843 0.8515 0.972 0.028
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.8465 1.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.4815 0.8324 0.896 0.104
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.5946 0.8102 0.856 0.144
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 1 0.9491 0.4935 0.632 0.368
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.3733 0.8666 0.072 0.928
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.2778 0.8506 0.952 0.048
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.4815 0.8324 0.896 0.104
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 1 0.6531 0.7880 0.832 0.168
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.4815 0.8324 0.896 0.104
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.6531 0.7682 0.832 0.168
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.6048 0.7850 0.852 0.148
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 1 0.9129 0.5951 0.672 0.328
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.2778 0.8506 0.952 0.048
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.5294 0.8167 0.880 0.120
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 1 0.8955 0.5926 0.688 0.312
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.6531 0.7682 0.832 0.168
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.2603 0.8473 0.956 0.044
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.9909 0.3879 0.556 0.444
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.1843 0.8515 0.972 0.028
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.5178 0.8397 0.116 0.884
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.8465 1.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.7056 0.7794 0.808 0.192
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.8819 0.000 1.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.0938 0.8486 0.988 0.012
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.1843 0.8515 0.972 0.028
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.8465 1.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.6048 0.7850 0.852 0.148
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.8819 0.000 1.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.2423 0.8442 0.960 0.040
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.0938 0.8486 0.988 0.012
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.8819 0.000 1.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.1843 0.8515 0.972 0.028
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0000 0.8465 1.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.4815 0.8324 0.896 0.104
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.5842 0.8005 0.140 0.860
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.8819 0.000 1.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.8465 1.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.6048 0.7850 0.852 0.148
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.8016 0.7096 0.756 0.244
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.8081 0.7078 0.752 0.248
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.4815 0.8324 0.896 0.104
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.6343 0.7754 0.840 0.160
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.8819 0.000 1.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.0672 0.8494 0.992 0.008
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.0938 0.8506 0.988 0.012
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.2778 0.8506 0.952 0.048
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.1843 0.8474 0.972 0.028
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.4815 0.8324 0.896 0.104
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.3274 0.8504 0.940 0.060
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.4815 0.8324 0.896 0.104
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.8819 0.000 1.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.4562 0.8529 0.096 0.904
#> 53A96249-66D5-4C26-893B-ADC71481D261 1 0.9988 0.1741 0.520 0.480
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.1843 0.8472 0.972 0.028
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.8819 0.000 1.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.3733 0.8630 0.072 0.928
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.6343 0.7754 0.840 0.160
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.6343 0.7754 0.840 0.160
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.1414 0.8508 0.980 0.020
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.1633 0.8472 0.976 0.024
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.0938 0.8486 0.988 0.012
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.5294 0.8167 0.880 0.120
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.0938 0.8486 0.988 0.012
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.5294 0.8167 0.880 0.120
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.8465 1.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.8819 0.000 1.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.2236 0.8455 0.964 0.036
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.4815 0.8324 0.896 0.104
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 1 0.9129 0.5951 0.672 0.328
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.8819 0.000 1.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.7883 0.6630 0.236 0.764
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 1 0.9248 0.5664 0.660 0.340
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.8465 1.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.5294 0.8344 0.120 0.880
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.8465 1.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.5294 0.8344 0.120 0.880
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.0938 0.8486 0.988 0.012
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.4815 0.8324 0.896 0.104
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.2778 0.8506 0.952 0.048
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.4562 0.8529 0.096 0.904
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.6712 0.7709 0.824 0.176
#> 2629FEE3-A203-4411-8A70-02A796C9505C 1 0.6531 0.7880 0.832 0.168
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.9881 0.0304 0.436 0.564
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.6801 0.7720 0.820 0.180
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.7745 0.7326 0.772 0.228
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.2948 0.8503 0.948 0.052
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 1 0.9608 0.4827 0.616 0.384
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.9129 0.4433 0.328 0.672
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.8819 0.000 1.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.1843 0.8515 0.972 0.028
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 1 0.9129 0.5951 0.672 0.328
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.6235 0.0477 0.564 0.000 0.436
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 3 0.9026 0.4502 0.196 0.248 0.556
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.3042 0.8587 0.040 0.920 0.040
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.7328 0.5414 0.364 0.596 0.040
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.6026 0.1760 0.624 0.000 0.376
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.8985 0.0736 0.540 0.160 0.300
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.8985 0.0736 0.540 0.160 0.300
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 3 0.9026 0.4502 0.196 0.248 0.556
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.6079 0.4090 0.388 0.000 0.612
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.6302 0.1503 0.520 0.000 0.480
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.4842 0.4327 0.224 0.000 0.776
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.9515 -0.1874 0.424 0.388 0.188
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.7244 0.4820 0.092 0.208 0.700
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.3551 0.5056 0.868 0.000 0.132
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.6140 0.3837 0.404 0.000 0.596
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 1 0.9170 0.1091 0.540 0.248 0.212
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.8877 0.000 1.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.5397 0.4994 0.720 0.000 0.280
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.1585 0.8802 0.008 0.964 0.028
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 3 0.4931 0.4629 0.232 0.000 0.768
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.8877 0.000 1.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.4002 0.4817 0.000 0.160 0.840
#> F325847E-F046-4B67-B01C-16919C401020 1 0.5098 0.3539 0.752 0.000 0.248
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.6168 0.3728 0.412 0.000 0.588
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.8877 0.000 1.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 3 0.4931 0.4629 0.232 0.000 0.768
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.7677 0.5335 0.244 0.096 0.660
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.2384 0.5088 0.936 0.008 0.056
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.5692 0.3711 0.724 0.008 0.268
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.5465 0.4913 0.712 0.000 0.288
#> A8E48877-F8AB-44DD-A18B-194D87C44931 3 0.9026 0.4502 0.196 0.248 0.556
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.6267 0.2571 0.452 0.000 0.548
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.3752 0.8183 0.000 0.856 0.144
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.6302 0.1503 0.520 0.000 0.480
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.5733 0.3006 0.676 0.000 0.324
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.5465 0.4857 0.712 0.000 0.288
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 3 0.4931 0.4629 0.232 0.000 0.768
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.6168 0.5339 0.224 0.036 0.740
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.8291 0.4198 0.116 0.280 0.604
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.3042 0.8587 0.040 0.920 0.040
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.6180 0.3622 0.416 0.000 0.584
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 3 0.4931 0.4629 0.232 0.000 0.768
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.7778 0.5196 0.264 0.092 0.644
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 3 0.4931 0.4629 0.232 0.000 0.768
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.2384 0.5088 0.936 0.008 0.056
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.4062 0.4560 0.836 0.000 0.164
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 3 0.9026 0.4502 0.196 0.248 0.556
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.6180 0.3622 0.416 0.000 0.584
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.8105 0.4273 0.336 0.084 0.580
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.9147 0.4233 0.200 0.260 0.540
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0848 0.4992 0.984 0.008 0.008
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.5623 0.4866 0.716 0.004 0.280
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.9698 -0.0782 0.436 0.228 0.336
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.5733 0.3006 0.676 0.000 0.324
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.4575 0.7983 0.012 0.828 0.160
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.5465 0.4857 0.712 0.000 0.288
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.6126 0.2484 0.600 0.000 0.400
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.8877 0.000 1.000 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.6008 0.1855 0.628 0.000 0.372
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.5733 0.3006 0.676 0.000 0.324
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.6302 0.1503 0.520 0.000 0.480
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.4062 0.4560 0.836 0.000 0.164
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.8877 0.000 1.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.4605 0.4963 0.796 0.000 0.204
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.6026 0.1760 0.624 0.000 0.376
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.8877 0.000 1.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.5706 0.3092 0.680 0.000 0.320
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.6225 0.2598 0.432 0.000 0.568
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 3 0.4931 0.4629 0.232 0.000 0.768
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.6452 0.7027 0.252 0.712 0.036
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.8877 0.000 1.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.5465 0.4857 0.712 0.000 0.288
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.3551 0.5056 0.868 0.000 0.132
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.6721 0.5492 0.136 0.116 0.748
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.6644 0.5459 0.140 0.108 0.752
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 3 0.4931 0.4629 0.232 0.000 0.768
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0892 0.4997 0.980 0.000 0.020
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.8877 0.000 1.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.6095 0.3863 0.392 0.000 0.608
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.6095 0.3860 0.392 0.000 0.608
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.6204 0.3457 0.424 0.000 0.576
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.5178 0.5031 0.744 0.000 0.256
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 3 0.4931 0.4629 0.232 0.000 0.768
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.6225 0.3358 0.432 0.000 0.568
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 3 0.4931 0.4629 0.232 0.000 0.768
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.8877 0.000 1.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.3623 0.8393 0.032 0.896 0.072
#> 53A96249-66D5-4C26-893B-ADC71481D261 3 0.7467 0.3862 0.056 0.320 0.624
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.5363 0.4939 0.724 0.000 0.276
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.8877 0.000 1.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.2796 0.8507 0.000 0.908 0.092
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0892 0.4997 0.980 0.000 0.020
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0237 0.5003 0.996 0.000 0.004
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.6309 0.1262 0.496 0.000 0.504
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.5465 0.4843 0.712 0.000 0.288
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.6204 0.0587 0.576 0.000 0.424
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 3 0.8105 0.4273 0.336 0.084 0.580
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.6008 0.1855 0.628 0.000 0.372
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 3 0.8105 0.4273 0.336 0.084 0.580
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.6302 0.1503 0.520 0.000 0.480
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.8877 0.000 1.000 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.5098 0.5052 0.752 0.000 0.248
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 3 0.4931 0.4629 0.232 0.000 0.768
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 3 0.9026 0.4502 0.196 0.248 0.556
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.8877 0.000 1.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.7328 0.5414 0.364 0.596 0.040
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.6208 0.5047 0.068 0.164 0.768
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.5465 0.4857 0.712 0.000 0.288
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.4178 0.7923 0.000 0.828 0.172
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.6299 0.1622 0.524 0.000 0.476
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.4178 0.7923 0.000 0.828 0.172
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.6026 0.1760 0.624 0.000 0.376
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 3 0.4931 0.4629 0.232 0.000 0.768
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.6180 0.3638 0.416 0.000 0.584
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.3623 0.8393 0.032 0.896 0.072
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.1711 0.5026 0.960 0.008 0.032
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.7778 0.5196 0.264 0.092 0.644
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 1 0.9690 -0.1117 0.424 0.356 0.220
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.4978 0.3967 0.780 0.004 0.216
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.6587 0.5493 0.156 0.092 0.752
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.6026 0.4265 0.376 0.000 0.624
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 3 0.4235 0.4767 0.000 0.176 0.824
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.8445 0.3325 0.424 0.488 0.088
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.8877 0.000 1.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.5733 0.3006 0.676 0.000 0.324
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.9026 0.4502 0.196 0.248 0.556
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.7126 0.252271 0.552 0.000 0.272 0.176
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 1 0.9648 -0.057898 0.368 0.184 0.276 0.172
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.2733 0.818194 0.032 0.916 0.032 0.020
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.6387 0.433160 0.016 0.584 0.356 0.044
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.1305 0.386913 0.960 0.000 0.004 0.036
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.7571 0.420012 0.128 0.092 0.636 0.144
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.7571 0.420012 0.128 0.092 0.636 0.144
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 1 0.9648 -0.057898 0.368 0.184 0.276 0.172
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.5712 0.331167 0.644 0.000 0.308 0.048
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.5519 0.230169 0.684 0.000 0.052 0.264
#> A31D342D-C67C-428B-BAED-C6E844277A09 4 0.7900 0.100329 0.332 0.000 0.300 0.368
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 3 0.7347 0.167685 0.016 0.324 0.540 0.120
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.9372 0.057490 0.312 0.140 0.388 0.160
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.7806 -0.040219 0.408 0.000 0.332 0.260
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.5535 0.332613 0.656 0.000 0.304 0.040
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.8560 0.409490 0.128 0.196 0.540 0.136
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0376 0.852469 0.000 0.992 0.004 0.004
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.7006 0.017094 0.456 0.000 0.116 0.428
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.1388 0.843803 0.000 0.960 0.012 0.028
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.4511 0.758161 0.268 0.000 0.008 0.724
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0376 0.852469 0.000 0.992 0.004 0.004
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.9213 0.049335 0.220 0.088 0.384 0.308
#> F325847E-F046-4B67-B01C-16919C401020 3 0.6607 0.303210 0.296 0.000 0.592 0.112
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.5592 0.332322 0.656 0.000 0.300 0.044
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0188 0.852132 0.000 0.996 0.000 0.004
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.4511 0.758161 0.268 0.000 0.008 0.724
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 1 0.8077 0.106721 0.444 0.040 0.388 0.128
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.6750 -0.128110 0.472 0.000 0.436 0.092
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.7270 0.204271 0.332 0.000 0.504 0.164
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.6843 0.125920 0.532 0.000 0.112 0.356
#> A8E48877-F8AB-44DD-A18B-194D87C44931 1 0.9648 -0.057898 0.368 0.184 0.276 0.172
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.6500 0.301106 0.620 0.000 0.260 0.120
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.3726 0.791724 0.012 0.844 0.012 0.132
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.5519 0.230169 0.684 0.000 0.052 0.264
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.3107 0.367592 0.884 0.000 0.036 0.080
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.6885 0.016094 0.460 0.000 0.104 0.436
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.4511 0.758161 0.268 0.000 0.008 0.724
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.6575 0.218951 0.508 0.000 0.412 0.080
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 1 0.9206 -0.065067 0.368 0.224 0.324 0.084
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.2733 0.818194 0.032 0.916 0.032 0.020
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.5546 0.335708 0.664 0.000 0.292 0.044
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.4511 0.758161 0.268 0.000 0.008 0.724
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 1 0.7978 0.118585 0.468 0.036 0.368 0.128
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.4511 0.758161 0.268 0.000 0.008 0.724
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.6750 -0.128110 0.472 0.000 0.436 0.092
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.7554 0.216569 0.316 0.000 0.472 0.212
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 1 0.9648 -0.057898 0.368 0.184 0.276 0.172
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.5546 0.335708 0.664 0.000 0.292 0.044
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.6528 0.322199 0.688 0.040 0.192 0.080
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 1 0.9306 -0.000311 0.436 0.200 0.232 0.132
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.6642 -0.142872 0.492 0.000 0.424 0.084
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 4 0.6985 0.071743 0.312 0.000 0.140 0.548
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 3 0.8755 0.351820 0.172 0.160 0.524 0.144
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.3107 0.367592 0.884 0.000 0.036 0.080
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.4278 0.775955 0.016 0.816 0.020 0.148
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.6885 0.016094 0.460 0.000 0.104 0.436
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.6801 0.092652 0.124 0.000 0.308 0.568
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0376 0.852469 0.000 0.992 0.004 0.004
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.1209 0.387128 0.964 0.000 0.004 0.032
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.3107 0.367592 0.884 0.000 0.036 0.080
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.5519 0.230169 0.684 0.000 0.052 0.264
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.7554 0.216569 0.316 0.000 0.472 0.212
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0188 0.852132 0.000 0.996 0.000 0.004
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.6552 0.223757 0.628 0.000 0.144 0.228
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.1305 0.386913 0.960 0.000 0.004 0.036
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.852387 0.000 1.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.3198 0.366433 0.880 0.000 0.040 0.080
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.6296 0.325875 0.652 0.000 0.224 0.124
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.4511 0.758161 0.268 0.000 0.008 0.724
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.5535 0.613925 0.008 0.704 0.244 0.044
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.852387 0.000 1.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.6885 0.016094 0.460 0.000 0.104 0.436
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.7806 -0.040219 0.408 0.000 0.332 0.260
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.7704 0.146300 0.460 0.044 0.412 0.084
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.7825 0.121138 0.436 0.040 0.424 0.100
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.4511 0.758161 0.268 0.000 0.008 0.724
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 3 0.6960 0.119298 0.420 0.000 0.468 0.112
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0376 0.852469 0.000 0.992 0.004 0.004
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.5883 0.345090 0.640 0.000 0.300 0.060
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.5814 0.345240 0.644 0.000 0.300 0.056
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.5472 0.341570 0.676 0.000 0.280 0.044
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.7221 0.000812 0.432 0.000 0.140 0.428
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.4511 0.758161 0.268 0.000 0.008 0.724
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.6587 0.247859 0.596 0.000 0.292 0.112
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.4511 0.758161 0.268 0.000 0.008 0.724
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0376 0.852469 0.000 0.992 0.004 0.004
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.3272 0.795443 0.052 0.892 0.020 0.036
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.9852 -0.277632 0.280 0.292 0.260 0.168
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.6792 0.149361 0.548 0.000 0.112 0.340
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0376 0.852469 0.000 0.992 0.004 0.004
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.2862 0.820709 0.012 0.900 0.012 0.076
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.6452 0.116666 0.460 0.000 0.472 0.068
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.6875 -0.119114 0.504 0.000 0.388 0.108
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.3691 0.377271 0.856 0.000 0.076 0.068
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.6619 0.160107 0.568 0.000 0.100 0.332
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.2908 0.391176 0.896 0.000 0.064 0.040
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.6528 0.322199 0.688 0.040 0.192 0.080
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.1209 0.387128 0.964 0.000 0.004 0.032
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.6528 0.322199 0.688 0.040 0.192 0.080
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.5519 0.230169 0.684 0.000 0.052 0.264
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0376 0.852469 0.000 0.992 0.004 0.004
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 4 0.7284 -0.077025 0.424 0.000 0.148 0.428
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.4511 0.758161 0.268 0.000 0.008 0.724
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 1 0.9648 -0.057898 0.368 0.184 0.276 0.172
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.852387 0.000 1.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.6387 0.433160 0.016 0.584 0.356 0.044
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.8857 -0.020383 0.356 0.092 0.412 0.140
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.6885 0.016094 0.460 0.000 0.104 0.436
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.4078 0.774985 0.012 0.816 0.012 0.160
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.5646 0.220717 0.672 0.000 0.056 0.272
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.4078 0.774985 0.012 0.816 0.012 0.160
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.1305 0.386913 0.960 0.000 0.004 0.036
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.4511 0.758161 0.268 0.000 0.008 0.724
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.5522 0.337520 0.668 0.000 0.288 0.044
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.3272 0.795443 0.052 0.892 0.020 0.036
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.6354 -0.136840 0.520 0.000 0.416 0.064
#> 2629FEE3-A203-4411-8A70-02A796C9505C 1 0.7978 0.118585 0.468 0.036 0.368 0.128
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 3 0.7421 0.252323 0.020 0.288 0.560 0.132
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.6761 0.338205 0.224 0.000 0.608 0.168
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.7719 0.139582 0.448 0.032 0.416 0.104
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.5677 0.312420 0.628 0.000 0.332 0.040
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 3 0.9418 0.034158 0.220 0.108 0.352 0.320
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.7441 0.192449 0.016 0.476 0.396 0.112
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.852387 0.000 1.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.3107 0.367592 0.884 0.000 0.036 0.080
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 1 0.9648 -0.057898 0.368 0.184 0.276 0.172
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.6915 0.24216 0.200 0.000 0.584 0.128 0.088
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 5 0.7471 0.48020 0.000 0.104 0.368 0.104 0.424
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.3257 0.80118 0.000 0.856 0.024 0.016 0.104
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.6568 0.50285 0.248 0.568 0.000 0.028 0.156
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.5687 0.13408 0.432 0.000 0.496 0.068 0.004
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 5 0.7808 0.36573 0.328 0.020 0.184 0.044 0.424
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 5 0.7808 0.36573 0.328 0.020 0.184 0.044 0.424
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 5 0.7471 0.48020 0.000 0.104 0.368 0.104 0.424
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.1883 0.47335 0.012 0.000 0.932 0.008 0.048
#> 5482053D-9F48-4773-B68A-302B3A612503 3 0.6715 0.14209 0.252 0.000 0.532 0.196 0.020
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.7596 0.02767 0.072 0.000 0.460 0.264 0.204
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 5 0.7825 0.23044 0.264 0.248 0.012 0.048 0.428
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 5 0.6622 0.25034 0.000 0.060 0.408 0.064 0.468
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.5777 0.45818 0.652 0.000 0.184 0.152 0.012
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.1195 0.47309 0.012 0.000 0.960 0.000 0.028
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 1 0.8990 -0.31799 0.336 0.140 0.176 0.044 0.304
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.2304 0.81837 0.008 0.892 0.000 0.000 0.100
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.7378 0.41335 0.404 0.000 0.224 0.336 0.036
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.1310 0.82169 0.000 0.956 0.000 0.024 0.020
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.2813 0.94461 0.000 0.000 0.168 0.832 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.2017 0.82104 0.008 0.912 0.000 0.000 0.080
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 5 0.7358 0.22948 0.004 0.032 0.308 0.212 0.444
#> F325847E-F046-4B67-B01C-16919C401020 3 0.7411 -0.12617 0.360 0.000 0.388 0.040 0.212
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.1799 0.47359 0.020 0.000 0.940 0.012 0.028
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0162 0.82699 0.000 0.996 0.000 0.000 0.004
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.2813 0.94461 0.000 0.000 0.168 0.832 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.5330 -0.00316 0.000 0.012 0.596 0.040 0.352
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.5327 0.43613 0.728 0.000 0.100 0.040 0.132
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.7761 -0.11019 0.268 0.000 0.456 0.104 0.172
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.7193 0.37231 0.412 0.000 0.300 0.268 0.020
#> A8E48877-F8AB-44DD-A18B-194D87C44931 5 0.7471 0.48020 0.000 0.104 0.368 0.104 0.424
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.5627 0.37101 0.072 0.000 0.704 0.064 0.160
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.4630 0.72709 0.000 0.744 0.000 0.116 0.140
#> 91BA5F90-9174-4533-A050-39A28E34A94D 3 0.6715 0.14209 0.252 0.000 0.532 0.196 0.020
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.6510 -0.00365 0.452 0.000 0.424 0.096 0.028
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.7003 0.41471 0.488 0.000 0.160 0.316 0.036
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.2813 0.94461 0.000 0.000 0.168 0.832 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.4000 0.33236 0.000 0.000 0.748 0.024 0.228
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.7230 -0.14267 0.004 0.176 0.476 0.036 0.308
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.3257 0.80118 0.000 0.856 0.024 0.016 0.104
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.2152 0.47794 0.032 0.000 0.924 0.012 0.032
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.2813 0.94461 0.000 0.000 0.168 0.832 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.5379 -0.00392 0.000 0.008 0.600 0.052 0.340
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.2813 0.94461 0.000 0.000 0.168 0.832 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.5327 0.43613 0.728 0.000 0.100 0.040 0.132
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.7191 0.26257 0.448 0.000 0.372 0.108 0.072
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 5 0.7471 0.48020 0.000 0.104 0.368 0.104 0.424
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.2152 0.47794 0.032 0.000 0.924 0.012 0.032
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.7647 0.34012 0.196 0.004 0.500 0.088 0.212
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.8360 -0.26800 0.044 0.132 0.404 0.084 0.336
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.3527 0.42410 0.828 0.000 0.056 0.000 0.116
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.5911 0.32337 0.528 0.000 0.020 0.392 0.060
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 5 0.8720 0.46517 0.220 0.080 0.176 0.080 0.444
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.6510 -0.00365 0.452 0.000 0.424 0.096 0.028
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.5121 0.71600 0.008 0.716 0.000 0.132 0.144
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.7003 0.41471 0.488 0.000 0.160 0.316 0.036
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.5050 0.21047 0.356 0.000 0.024 0.608 0.012
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.3013 0.80472 0.008 0.832 0.000 0.000 0.160
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.5546 0.12713 0.436 0.000 0.496 0.068 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.6510 -0.00365 0.452 0.000 0.424 0.096 0.028
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 3 0.6715 0.14209 0.252 0.000 0.532 0.196 0.020
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.7191 0.26257 0.448 0.000 0.372 0.108 0.072
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0162 0.82699 0.000 0.996 0.000 0.000 0.004
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.7034 0.37294 0.488 0.000 0.296 0.184 0.032
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.5687 0.13408 0.432 0.000 0.496 0.068 0.004
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.82702 0.000 1.000 0.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.6509 0.00636 0.456 0.000 0.420 0.096 0.028
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.3784 0.47452 0.088 0.000 0.832 0.064 0.016
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.2813 0.94461 0.000 0.000 0.168 0.832 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.5488 0.64631 0.184 0.696 0.000 0.028 0.092
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.82702 0.000 1.000 0.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.7003 0.41471 0.488 0.000 0.160 0.316 0.036
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.5777 0.45818 0.652 0.000 0.184 0.152 0.012
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.4865 0.19376 0.004 0.000 0.640 0.032 0.324
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.5016 0.14700 0.004 0.000 0.616 0.036 0.344
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.2813 0.94461 0.000 0.000 0.168 0.832 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.4629 0.40567 0.724 0.000 0.224 0.008 0.044
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.3013 0.80472 0.008 0.832 0.000 0.000 0.160
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.3297 0.47756 0.032 0.000 0.868 0.040 0.060
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.3076 0.47949 0.028 0.000 0.880 0.040 0.052
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.2171 0.48339 0.032 0.000 0.924 0.016 0.028
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.6762 0.44238 0.524 0.000 0.132 0.308 0.036
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.2813 0.94461 0.000 0.000 0.168 0.832 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.5635 0.28933 0.056 0.000 0.684 0.056 0.204
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.2813 0.94461 0.000 0.000 0.168 0.832 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.3013 0.80472 0.008 0.832 0.000 0.000 0.160
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.3661 0.78297 0.008 0.852 0.040 0.024 0.076
#> 53A96249-66D5-4C26-893B-ADC71481D261 3 0.8351 -0.22814 0.012 0.244 0.360 0.092 0.292
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.7188 0.36264 0.408 0.000 0.316 0.256 0.020
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.3013 0.80472 0.008 0.832 0.000 0.000 0.160
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.3844 0.76322 0.000 0.804 0.000 0.064 0.132
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.5640 0.34827 0.588 0.000 0.336 0.012 0.064
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.2494 0.44706 0.904 0.000 0.056 0.008 0.032
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.6617 0.30359 0.320 0.000 0.540 0.088 0.052
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.7202 0.33796 0.392 0.000 0.336 0.252 0.020
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.5743 0.28343 0.308 0.000 0.600 0.080 0.012
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 3 0.7647 0.34012 0.196 0.004 0.500 0.088 0.212
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 3 0.5546 0.12713 0.436 0.000 0.496 0.068 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 3 0.7647 0.34012 0.196 0.004 0.500 0.088 0.212
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 3 0.6715 0.14209 0.252 0.000 0.532 0.196 0.020
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.3013 0.80472 0.008 0.832 0.000 0.000 0.160
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.6700 0.44949 0.536 0.000 0.128 0.300 0.036
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.2813 0.94461 0.000 0.000 0.168 0.832 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 5 0.7471 0.48020 0.000 0.104 0.368 0.104 0.424
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.82702 0.000 1.000 0.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.6568 0.50285 0.248 0.568 0.000 0.028 0.156
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.6327 -0.13232 0.004 0.032 0.468 0.060 0.436
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.7003 0.41471 0.488 0.000 0.160 0.316 0.036
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.4930 0.71455 0.000 0.716 0.000 0.140 0.144
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 3 0.6826 0.09740 0.256 0.000 0.512 0.212 0.020
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.4930 0.71455 0.000 0.716 0.000 0.140 0.144
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.5687 0.13408 0.432 0.000 0.496 0.068 0.004
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.2813 0.94461 0.000 0.000 0.168 0.832 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.1997 0.48019 0.024 0.000 0.932 0.016 0.028
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.3661 0.78297 0.008 0.852 0.040 0.024 0.076
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.4285 0.41386 0.792 0.000 0.080 0.012 0.116
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.5379 -0.00392 0.000 0.008 0.600 0.052 0.340
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 5 0.7844 0.31112 0.264 0.212 0.020 0.048 0.456
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.7694 0.08162 0.396 0.000 0.360 0.088 0.156
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.5024 0.17613 0.004 0.000 0.628 0.040 0.328
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.1357 0.45790 0.004 0.000 0.948 0.000 0.048
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 5 0.7511 0.23561 0.004 0.044 0.276 0.224 0.452
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.7662 0.25933 0.272 0.424 0.004 0.048 0.252
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0162 0.82703 0.000 0.996 0.000 0.000 0.004
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.6510 -0.00365 0.452 0.000 0.424 0.096 0.028
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 5 0.7471 0.48020 0.000 0.104 0.368 0.104 0.424
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 6 0.7107 -0.2042 0.120 0.000 0.372 0.112 0.008 0.388
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 3 0.6532 0.2670 0.000 0.032 0.604 0.080 0.132 0.152
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.4419 0.2094 0.000 0.764 0.028 0.024 0.152 0.032
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.6018 0.3468 0.016 0.544 0.012 0.020 0.068 0.340
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.6803 -0.0333 0.400 0.000 0.428 0.064 0.044 0.064
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 6 0.6039 0.4128 0.040 0.000 0.356 0.020 0.060 0.524
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 6 0.6039 0.4128 0.040 0.000 0.356 0.020 0.060 0.524
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 3 0.6532 0.2670 0.000 0.032 0.604 0.080 0.132 0.152
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.4746 0.4293 0.016 0.000 0.636 0.016 0.016 0.316
#> 5482053D-9F48-4773-B68A-302B3A612503 3 0.7518 -0.0730 0.280 0.000 0.392 0.176 0.008 0.144
#> A31D342D-C67C-428B-BAED-C6E844277A09 4 0.8382 0.0894 0.092 0.000 0.248 0.332 0.104 0.224
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 6 0.7970 0.3068 0.016 0.180 0.220 0.024 0.132 0.428
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.5814 0.2814 0.000 0.028 0.672 0.064 0.144 0.092
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.6297 0.1865 0.460 0.000 0.048 0.092 0.008 0.392
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.4670 0.4216 0.020 0.000 0.636 0.012 0.012 0.320
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 6 0.7305 0.4662 0.040 0.060 0.224 0.016 0.136 0.524
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.3756 -0.6821 0.000 0.600 0.000 0.000 0.400 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.6778 0.4278 0.544 0.000 0.112 0.168 0.008 0.168
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.1478 0.5322 0.000 0.944 0.000 0.004 0.032 0.020
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.1714 0.8990 0.000 0.000 0.092 0.908 0.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.3634 -0.5753 0.000 0.644 0.000 0.000 0.356 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.6838 0.0066 0.016 0.004 0.492 0.292 0.148 0.048
#> F325847E-F046-4B67-B01C-16919C401020 6 0.4808 0.4493 0.060 0.000 0.164 0.020 0.024 0.732
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.4930 0.4177 0.024 0.000 0.620 0.016 0.016 0.324
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0146 0.5220 0.000 0.996 0.000 0.000 0.004 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.1714 0.8990 0.000 0.000 0.092 0.908 0.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.5449 0.3574 0.000 0.000 0.624 0.044 0.076 0.256
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.6234 0.1034 0.456 0.000 0.036 0.044 0.044 0.420
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 6 0.6027 0.3767 0.060 0.000 0.184 0.088 0.028 0.640
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.7275 0.4643 0.480 0.000 0.200 0.152 0.012 0.156
#> A8E48877-F8AB-44DD-A18B-194D87C44931 3 0.6532 0.2670 0.000 0.032 0.604 0.080 0.132 0.152
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.6405 0.3543 0.060 0.000 0.472 0.044 0.036 0.388
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.4657 0.5062 0.004 0.684 0.000 0.052 0.248 0.012
#> 91BA5F90-9174-4533-A050-39A28E34A94D 3 0.7518 -0.0730 0.280 0.000 0.392 0.176 0.008 0.144
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.7372 0.1562 0.408 0.000 0.356 0.096 0.044 0.096
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.5785 0.5145 0.632 0.000 0.120 0.204 0.020 0.024
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.1714 0.8990 0.000 0.000 0.092 0.908 0.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.4044 0.4432 0.008 0.000 0.788 0.024 0.044 0.136
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.5611 0.2921 0.000 0.152 0.688 0.032 0.068 0.060
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.4419 0.2094 0.000 0.764 0.028 0.024 0.152 0.032
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.5024 0.4189 0.032 0.000 0.624 0.016 0.016 0.312
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.1714 0.8990 0.000 0.000 0.092 0.908 0.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.5487 0.3537 0.000 0.000 0.612 0.048 0.068 0.272
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.1714 0.8990 0.000 0.000 0.092 0.908 0.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.6234 0.1034 0.456 0.000 0.036 0.044 0.044 0.420
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 6 0.6011 0.3100 0.236 0.000 0.096 0.052 0.012 0.604
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 3 0.6532 0.2670 0.000 0.032 0.604 0.080 0.132 0.152
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.5024 0.4189 0.032 0.000 0.624 0.016 0.016 0.312
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.5315 0.3332 0.204 0.000 0.660 0.096 0.000 0.040
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.7334 0.3113 0.048 0.084 0.592 0.064 0.092 0.120
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.5394 0.1180 0.544 0.000 0.012 0.012 0.056 0.376
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.3873 0.4012 0.772 0.000 0.000 0.176 0.020 0.032
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 3 0.6929 -0.2693 0.016 0.004 0.420 0.052 0.132 0.376
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.7372 0.1562 0.408 0.000 0.356 0.096 0.044 0.096
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.5170 0.4997 0.008 0.656 0.000 0.064 0.248 0.024
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.5785 0.5145 0.632 0.000 0.120 0.204 0.020 0.024
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.5708 0.2411 0.164 0.000 0.012 0.564 0.000 0.260
#> A54731AE-FC40-407F-8D10-67DDC122237D 5 0.3857 1.0000 0.000 0.468 0.000 0.000 0.532 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.6805 -0.0420 0.404 0.000 0.424 0.064 0.044 0.064
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.7372 0.1562 0.408 0.000 0.356 0.096 0.044 0.096
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 3 0.7518 -0.0730 0.280 0.000 0.392 0.176 0.008 0.144
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 6 0.6011 0.3100 0.236 0.000 0.096 0.052 0.012 0.604
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0146 0.5220 0.000 0.996 0.000 0.000 0.004 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.7214 0.4553 0.512 0.000 0.180 0.092 0.032 0.184
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.6803 -0.0333 0.400 0.000 0.428 0.064 0.044 0.064
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.5197 0.000 1.000 0.000 0.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.7400 0.1628 0.408 0.000 0.352 0.096 0.044 0.100
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.6102 0.3685 0.084 0.000 0.592 0.056 0.016 0.252
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.1714 0.8990 0.000 0.000 0.092 0.908 0.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.4914 0.4176 0.004 0.680 0.012 0.012 0.048 0.244
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.5197 0.000 1.000 0.000 0.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.5785 0.5145 0.632 0.000 0.120 0.204 0.020 0.024
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.6297 0.1865 0.460 0.000 0.048 0.092 0.008 0.392
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.2615 0.4379 0.004 0.000 0.892 0.020 0.044 0.040
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.2815 0.4231 0.000 0.000 0.876 0.024 0.044 0.056
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.1714 0.8990 0.000 0.000 0.092 0.908 0.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 6 0.4683 0.0439 0.436 0.000 0.016 0.004 0.012 0.532
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 5 0.3857 1.0000 0.000 0.468 0.000 0.000 0.532 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.5455 0.4294 0.036 0.000 0.624 0.040 0.020 0.280
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.5328 0.4310 0.032 0.000 0.628 0.040 0.016 0.284
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.5274 0.4254 0.036 0.000 0.620 0.024 0.020 0.300
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.5767 0.5179 0.656 0.000 0.104 0.176 0.020 0.044
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.1714 0.8990 0.000 0.000 0.092 0.908 0.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.6155 0.3329 0.040 0.000 0.472 0.052 0.028 0.408
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.1714 0.8990 0.000 0.000 0.092 0.908 0.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 5 0.3857 1.0000 0.000 0.468 0.000 0.000 0.532 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.5346 -0.1565 0.000 0.672 0.032 0.044 0.220 0.032
#> 53A96249-66D5-4C26-893B-ADC71481D261 3 0.7697 0.1266 0.000 0.108 0.448 0.100 0.268 0.076
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.7231 0.4550 0.476 0.000 0.200 0.144 0.008 0.172
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 5 0.3857 1.0000 0.000 0.468 0.000 0.000 0.532 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.3739 0.5151 0.004 0.752 0.000 0.020 0.220 0.004
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 6 0.4886 0.2200 0.264 0.000 0.048 0.012 0.012 0.664
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.4686 0.1420 0.588 0.000 0.000 0.004 0.044 0.364
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.6969 0.1600 0.296 0.000 0.496 0.096 0.036 0.076
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.7146 0.4373 0.472 0.000 0.224 0.144 0.004 0.156
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.7427 0.1364 0.280 0.000 0.444 0.088 0.032 0.156
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 3 0.5315 0.3332 0.204 0.000 0.660 0.096 0.000 0.040
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 3 0.6805 -0.0420 0.404 0.000 0.424 0.064 0.044 0.064
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 3 0.5315 0.3332 0.204 0.000 0.660 0.096 0.000 0.040
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 3 0.7518 -0.0730 0.280 0.000 0.392 0.176 0.008 0.144
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 5 0.3857 1.0000 0.000 0.468 0.000 0.000 0.532 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.5783 0.5147 0.660 0.000 0.100 0.168 0.020 0.052
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.1714 0.8990 0.000 0.000 0.092 0.908 0.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 3 0.6532 0.2670 0.000 0.032 0.604 0.080 0.132 0.152
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.5197 0.000 1.000 0.000 0.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.6018 0.3468 0.016 0.544 0.012 0.020 0.068 0.340
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.4710 0.3485 0.000 0.008 0.752 0.052 0.120 0.068
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.5785 0.5145 0.632 0.000 0.120 0.204 0.020 0.024
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.5159 0.4974 0.016 0.656 0.000 0.068 0.248 0.012
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 3 0.7560 -0.1174 0.296 0.000 0.372 0.180 0.008 0.144
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.5159 0.4974 0.016 0.656 0.000 0.068 0.248 0.012
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.6803 -0.0333 0.400 0.000 0.428 0.064 0.044 0.064
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.1714 0.8990 0.000 0.000 0.092 0.908 0.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.5099 0.4246 0.028 0.000 0.624 0.020 0.020 0.308
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.5346 -0.1565 0.000 0.672 0.032 0.044 0.220 0.032
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.6099 0.1043 0.504 0.000 0.040 0.024 0.056 0.376
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.5487 0.3537 0.000 0.000 0.612 0.048 0.068 0.272
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 6 0.7855 0.3517 0.016 0.140 0.252 0.024 0.132 0.436
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 6 0.5448 0.4639 0.096 0.000 0.112 0.056 0.028 0.708
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.3299 0.4258 0.000 0.000 0.844 0.028 0.048 0.080
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.4466 0.4234 0.008 0.000 0.652 0.012 0.016 0.312
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 3 0.7398 -0.0444 0.020 0.016 0.444 0.304 0.156 0.060
#> A608BCEB-2C27-4927-A308-E6975F641722 6 0.7331 0.0283 0.016 0.248 0.028 0.024 0.256 0.428
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0405 0.5143 0.000 0.988 0.000 0.000 0.008 0.004
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.7372 0.1562 0.408 0.000 0.356 0.096 0.044 0.096
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.6532 0.2670 0.000 0.032 0.604 0.080 0.132 0.152
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "kmeans"]
# you can also extract it by
# res = res_list["MAD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.983 0.948 0.977 0.4738 0.529 0.529
#> 3 3 0.424 0.587 0.799 0.3531 0.711 0.496
#> 4 4 0.495 0.479 0.680 0.1357 0.885 0.685
#> 5 5 0.554 0.467 0.671 0.0759 0.813 0.454
#> 6 6 0.662 0.587 0.734 0.0479 0.891 0.556
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.0000 0.9760 1.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.0672 0.9780 0.008 0.992
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0376 0.9785 0.004 0.996
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0376 0.9785 0.004 0.996
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.6887 0.7708 0.816 0.184
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.1633 0.9601 0.976 0.024
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 2 0.2236 0.9547 0.036 0.964
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0672 0.9780 0.008 0.992
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.0376 0.9751 0.996 0.004
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0376 0.9759 0.996 0.004
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.0376 0.9751 0.996 0.004
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.0672 0.9780 0.008 0.992
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.0938 0.9752 0.012 0.988
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0376 0.9759 0.996 0.004
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.0376 0.9751 0.996 0.004
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.0376 0.9767 0.004 0.996
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0376 0.9785 0.004 0.996
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0376 0.9759 0.996 0.004
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0376 0.9785 0.004 0.996
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.0376 0.9759 0.996 0.004
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0376 0.9785 0.004 0.996
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.0938 0.9752 0.012 0.988
#> F325847E-F046-4B67-B01C-16919C401020 1 0.0376 0.9751 0.996 0.004
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.0376 0.9751 0.996 0.004
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0376 0.9785 0.004 0.996
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.0376 0.9759 0.996 0.004
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 2 0.0938 0.9752 0.012 0.988
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0000 0.9760 1.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.0376 0.9751 0.996 0.004
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0376 0.9759 0.996 0.004
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.1184 0.9755 0.016 0.984
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.0376 0.9751 0.996 0.004
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0376 0.9785 0.004 0.996
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0376 0.9759 0.996 0.004
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.0000 0.9760 1.000 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0376 0.9759 0.996 0.004
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.0376 0.9759 0.996 0.004
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.0376 0.9751 0.996 0.004
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.1633 0.9661 0.024 0.976
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0376 0.9785 0.004 0.996
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.0376 0.9751 0.996 0.004
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.0376 0.9759 0.996 0.004
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.0938 0.9752 0.012 0.988
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.0376 0.9759 0.996 0.004
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.0376 0.9759 0.996 0.004
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.0376 0.9751 0.996 0.004
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.1184 0.9755 0.016 0.984
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.0376 0.9751 0.996 0.004
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.0000 0.9760 1.000 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.0938 0.9752 0.012 0.988
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0000 0.9760 1.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0376 0.9759 0.996 0.004
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.6531 0.7931 0.832 0.168
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.0000 0.9760 1.000 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.8499 0.6098 0.276 0.724
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0376 0.9759 0.996 0.004
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0376 0.9759 0.996 0.004
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0376 0.9767 0.004 0.996
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.0000 0.9760 1.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0000 0.9760 1.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0376 0.9759 0.996 0.004
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.0000 0.9760 1.000 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0376 0.9785 0.004 0.996
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0376 0.9759 0.996 0.004
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.0376 0.9743 0.996 0.004
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0376 0.9785 0.004 0.996
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.0000 0.9760 1.000 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0376 0.9751 0.996 0.004
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.0376 0.9759 0.996 0.004
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0376 0.9785 0.004 0.996
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0376 0.9785 0.004 0.996
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0376 0.9759 0.996 0.004
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0376 0.9759 0.996 0.004
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.0376 0.9751 0.996 0.004
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.0376 0.9751 0.996 0.004
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.0376 0.9759 0.996 0.004
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0376 0.9751 0.996 0.004
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0376 0.9785 0.004 0.996
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.0376 0.9751 0.996 0.004
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.0376 0.9751 0.996 0.004
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0376 0.9751 0.996 0.004
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0376 0.9759 0.996 0.004
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.0938 0.9756 0.012 0.988
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.0376 0.9751 0.996 0.004
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.0376 0.9759 0.996 0.004
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0376 0.9785 0.004 0.996
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0376 0.9785 0.004 0.996
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0376 0.9767 0.004 0.996
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0376 0.9759 0.996 0.004
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0376 0.9767 0.004 0.996
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0376 0.9785 0.004 0.996
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0376 0.9751 0.996 0.004
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0000 0.9760 1.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.0000 0.9760 1.000 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0376 0.9759 0.996 0.004
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.0000 0.9760 1.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.9815 0.2811 0.580 0.420
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.0000 0.9760 1.000 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.0376 0.9759 0.996 0.004
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0376 0.9759 0.996 0.004
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0376 0.9767 0.004 0.996
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0376 0.9759 0.996 0.004
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.0376 0.9759 0.996 0.004
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.0672 0.9765 0.008 0.992
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0376 0.9785 0.004 0.996
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.9881 0.2025 0.436 0.564
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.0938 0.9752 0.012 0.988
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0376 0.9759 0.996 0.004
#> A60DC925-7343-496E-900D-0DD81D5C8123 1 0.9993 0.0676 0.516 0.484
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0376 0.9759 0.996 0.004
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0376 0.9785 0.004 0.996
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.6801 0.7764 0.820 0.180
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.0376 0.9759 0.996 0.004
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.0376 0.9751 0.996 0.004
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0376 0.9785 0.004 0.996
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.5408 0.8494 0.876 0.124
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.0938 0.9752 0.012 0.988
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0672 0.9780 0.008 0.992
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.0376 0.9751 0.996 0.004
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.0376 0.9751 0.996 0.004
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.0376 0.9751 0.996 0.004
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0376 0.9785 0.004 0.996
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0672 0.9780 0.008 0.992
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0376 0.9785 0.004 0.996
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.0000 0.9760 1.000 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.0938 0.9752 0.012 0.988
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.6026 0.0693 0.376 0.000 0.624
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.5760 0.5558 0.000 0.672 0.328
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.8600 0.000 1.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.3989 0.7728 0.124 0.864 0.012
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.5200 0.5700 0.184 0.020 0.796
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.3038 0.6256 0.104 0.000 0.896
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.7104 0.5386 0.140 0.136 0.724
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.5178 0.6531 0.000 0.744 0.256
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0747 0.6790 0.016 0.000 0.984
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.6062 0.5737 0.616 0.000 0.384
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.3752 0.6000 0.144 0.000 0.856
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.4874 0.7474 0.144 0.828 0.028
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.5678 0.3658 0.000 0.316 0.684
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.3879 0.7025 0.848 0.000 0.152
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0747 0.6790 0.016 0.000 0.984
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.4937 0.7533 0.028 0.824 0.148
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.8600 0.000 1.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.3752 0.7056 0.856 0.000 0.144
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0424 0.8596 0.000 0.992 0.008
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.4121 0.6951 0.832 0.000 0.168
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.8600 0.000 1.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.5706 0.3617 0.000 0.320 0.680
#> F325847E-F046-4B67-B01C-16919C401020 3 0.4654 0.5356 0.208 0.000 0.792
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.4702 0.4706 0.212 0.000 0.788
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0424 0.8596 0.000 0.992 0.008
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.4121 0.6951 0.832 0.000 0.168
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.4291 0.5948 0.000 0.180 0.820
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.5291 0.6337 0.732 0.000 0.268
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.6308 0.1984 0.508 0.000 0.492
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.3752 0.7056 0.856 0.000 0.144
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.6286 0.2667 0.000 0.536 0.464
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.0592 0.6783 0.012 0.000 0.988
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0424 0.8596 0.000 0.992 0.008
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.6062 0.5737 0.616 0.000 0.384
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 3 0.6225 -0.0837 0.432 0.000 0.568
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.5016 0.7074 0.760 0.000 0.240
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.4002 0.6955 0.840 0.000 0.160
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0747 0.6790 0.016 0.000 0.984
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.5363 0.4407 0.000 0.276 0.724
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.8600 0.000 1.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0747 0.6790 0.016 0.000 0.984
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.4121 0.6951 0.832 0.000 0.168
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.5650 0.3729 0.000 0.312 0.688
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.4121 0.6951 0.832 0.000 0.168
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.6079 0.5952 0.612 0.000 0.388
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.6204 0.3729 0.576 0.000 0.424
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.6286 0.2667 0.000 0.536 0.464
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0747 0.6790 0.016 0.000 0.984
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.4750 0.5168 0.216 0.000 0.784
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.5835 0.3329 0.000 0.340 0.660
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.5254 0.6344 0.736 0.000 0.264
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.2796 0.7017 0.908 0.000 0.092
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 3 0.6859 0.1446 0.420 0.016 0.564
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.5497 0.5845 0.708 0.000 0.292
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.7181 0.5135 0.304 0.648 0.048
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.5098 0.7035 0.752 0.000 0.248
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.1031 0.6611 0.976 0.000 0.024
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0237 0.8594 0.000 0.996 0.004
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.6062 0.1252 0.384 0.000 0.616
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.6204 -0.0541 0.424 0.000 0.576
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.6062 0.5737 0.616 0.000 0.384
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.6299 0.2395 0.524 0.000 0.476
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0424 0.8596 0.000 0.992 0.008
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.4887 0.7162 0.772 0.000 0.228
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.4575 0.5742 0.184 0.004 0.812
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.8600 0.000 1.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.5560 0.5771 0.700 0.000 0.300
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.4399 0.5290 0.188 0.000 0.812
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.4062 0.6938 0.836 0.000 0.164
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0424 0.8596 0.000 0.992 0.008
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0424 0.8596 0.000 0.992 0.008
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.5098 0.7035 0.752 0.000 0.248
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.3941 0.7014 0.844 0.000 0.156
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0592 0.6785 0.012 0.000 0.988
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0892 0.6780 0.020 0.000 0.980
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.4121 0.6951 0.832 0.000 0.168
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.5882 0.5140 0.652 0.000 0.348
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.8600 0.000 1.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.1529 0.6690 0.040 0.000 0.960
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0747 0.6790 0.016 0.000 0.984
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.5138 0.4457 0.252 0.000 0.748
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.2878 0.7027 0.904 0.000 0.096
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 3 0.9736 0.1503 0.356 0.228 0.416
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0747 0.6790 0.016 0.000 0.984
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.4121 0.6951 0.832 0.000 0.168
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.8600 0.000 1.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0237 0.8594 0.000 0.996 0.004
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.5529 0.5949 0.000 0.704 0.296
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.3752 0.7056 0.856 0.000 0.144
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.8600 0.000 1.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0424 0.8596 0.000 0.992 0.008
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.6299 0.2445 0.524 0.000 0.476
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.5216 0.6355 0.740 0.000 0.260
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.4235 0.5784 0.176 0.000 0.824
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.5560 0.6828 0.700 0.000 0.300
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.6045 0.0912 0.380 0.000 0.620
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 3 0.7633 0.5131 0.120 0.200 0.680
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 3 0.4555 0.5517 0.200 0.000 0.800
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 3 0.6126 0.0460 0.400 0.000 0.600
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.6062 0.5737 0.616 0.000 0.384
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0237 0.8594 0.000 0.996 0.004
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.2878 0.7040 0.904 0.000 0.096
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.4121 0.6951 0.832 0.000 0.168
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.6286 0.2667 0.000 0.536 0.464
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0424 0.8596 0.000 0.992 0.008
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.8179 0.1555 0.424 0.504 0.072
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.5650 0.3729 0.000 0.312 0.688
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.5098 0.7035 0.752 0.000 0.248
#> A60DC925-7343-496E-900D-0DD81D5C8123 1 0.7083 0.1776 0.592 0.380 0.028
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.5968 0.6074 0.636 0.000 0.364
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0424 0.8596 0.000 0.992 0.008
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.5092 0.5670 0.176 0.020 0.804
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.4121 0.6951 0.832 0.000 0.168
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.1753 0.6646 0.048 0.000 0.952
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.8600 0.000 1.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.7069 0.1663 0.508 0.020 0.472
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.5216 0.4703 0.000 0.260 0.740
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.2681 0.8346 0.028 0.932 0.040
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.6026 0.2024 0.376 0.000 0.624
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0592 0.6785 0.012 0.000 0.988
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0592 0.6785 0.012 0.000 0.988
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.5650 0.5719 0.000 0.688 0.312
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0424 0.8594 0.000 0.992 0.008
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.8600 0.000 1.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.6154 0.0161 0.408 0.000 0.592
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.6308 0.1785 0.000 0.508 0.492
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.7205 0.0482 0.344 0.000 0.504 0.152
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.7748 0.1628 0.000 0.436 0.304 0.260
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.1576 0.8394 0.004 0.948 0.000 0.048
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.3925 0.7020 0.176 0.808 0.000 0.016
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.7375 0.3738 0.176 0.000 0.488 0.336
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.5200 0.5865 0.072 0.000 0.744 0.184
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.7937 0.3982 0.224 0.024 0.524 0.228
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.7372 0.3753 0.000 0.524 0.236 0.240
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0927 0.6223 0.016 0.000 0.976 0.008
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.6996 0.2052 0.580 0.000 0.228 0.192
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.4094 0.5530 0.116 0.000 0.828 0.056
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.7482 0.4235 0.316 0.504 0.004 0.176
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.5279 0.5785 0.000 0.072 0.736 0.192
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.1406 0.3993 0.960 0.000 0.024 0.016
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.1356 0.6151 0.032 0.000 0.960 0.008
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.8258 0.4961 0.080 0.556 0.156 0.208
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0657 0.8457 0.004 0.984 0.000 0.012
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.2089 0.3851 0.932 0.000 0.020 0.048
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0707 0.8464 0.000 0.980 0.000 0.020
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.5790 0.9505 0.340 0.000 0.044 0.616
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0336 0.8463 0.000 0.992 0.000 0.008
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.5240 0.5809 0.000 0.072 0.740 0.188
#> F325847E-F046-4B67-B01C-16919C401020 3 0.5700 0.1120 0.412 0.000 0.560 0.028
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.3498 0.5089 0.160 0.000 0.832 0.008
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.1022 0.8445 0.000 0.968 0.000 0.032
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.5790 0.9505 0.340 0.000 0.044 0.616
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.3626 0.6017 0.000 0.004 0.812 0.184
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.3570 0.4425 0.860 0.000 0.092 0.048
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.5766 0.2200 0.564 0.000 0.404 0.032
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.2060 0.3787 0.932 0.000 0.016 0.052
#> A8E48877-F8AB-44DD-A18B-194D87C44931 3 0.7828 0.1044 0.000 0.340 0.396 0.264
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.1022 0.6278 0.000 0.000 0.968 0.032
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.1118 0.8439 0.000 0.964 0.000 0.036
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.6969 0.2058 0.584 0.000 0.224 0.192
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 3 0.7911 -0.1177 0.348 0.000 0.348 0.304
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.6275 0.0269 0.640 0.000 0.104 0.256
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.5790 0.9505 0.340 0.000 0.044 0.616
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0657 0.6236 0.012 0.000 0.984 0.004
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.6281 0.5654 0.020 0.068 0.672 0.240
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.1109 0.8445 0.004 0.968 0.000 0.028
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0469 0.6244 0.012 0.000 0.988 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.5790 0.9505 0.340 0.000 0.044 0.616
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.5318 0.5766 0.000 0.072 0.732 0.196
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.5790 0.9505 0.340 0.000 0.044 0.616
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.7122 0.1814 0.516 0.000 0.144 0.340
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.5666 0.3137 0.616 0.000 0.348 0.036
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 3 0.7828 0.1044 0.000 0.340 0.396 0.264
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0937 0.6258 0.012 0.000 0.976 0.012
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.7393 0.3155 0.180 0.000 0.488 0.332
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.8066 0.4364 0.028 0.172 0.484 0.316
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.5248 0.4152 0.748 0.000 0.088 0.164
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.3208 0.2717 0.848 0.000 0.004 0.148
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.7527 0.1501 0.484 0.000 0.216 0.300
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.5624 0.4088 0.720 0.000 0.108 0.172
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.6421 0.2132 0.432 0.508 0.004 0.056
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.6648 0.0690 0.612 0.000 0.140 0.248
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.5057 -0.1975 0.648 0.000 0.012 0.340
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.2466 0.8204 0.004 0.900 0.000 0.096
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.7770 0.0280 0.336 0.000 0.416 0.248
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.7893 -0.0692 0.324 0.000 0.376 0.300
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.6969 0.2058 0.584 0.000 0.224 0.192
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.5860 0.2605 0.580 0.000 0.380 0.040
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0592 0.8448 0.000 0.984 0.000 0.016
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.4149 0.2484 0.804 0.000 0.028 0.168
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.7327 0.3755 0.176 0.000 0.504 0.320
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0817 0.8465 0.000 0.976 0.000 0.024
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.5731 0.4076 0.712 0.000 0.116 0.172
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.5030 0.4535 0.188 0.000 0.752 0.060
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.5790 0.9505 0.340 0.000 0.044 0.616
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0592 0.8448 0.000 0.984 0.000 0.016
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0707 0.8464 0.000 0.980 0.000 0.020
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.6648 0.0690 0.612 0.000 0.140 0.248
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.1584 0.4081 0.952 0.000 0.036 0.012
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.1118 0.6278 0.000 0.000 0.964 0.036
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.1042 0.6287 0.008 0.000 0.972 0.020
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.5790 0.9505 0.340 0.000 0.044 0.616
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.6267 0.4195 0.664 0.000 0.188 0.148
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0779 0.8457 0.004 0.980 0.000 0.016
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.1722 0.6061 0.048 0.000 0.944 0.008
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0657 0.6236 0.012 0.000 0.984 0.004
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.4919 0.4613 0.200 0.000 0.752 0.048
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.3142 0.2980 0.860 0.000 0.008 0.132
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.5415 0.5794 0.144 0.028 0.060 0.768
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0469 0.6244 0.012 0.000 0.988 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.5717 0.9220 0.324 0.000 0.044 0.632
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0779 0.8457 0.004 0.980 0.000 0.016
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.3743 0.7711 0.000 0.824 0.016 0.160
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.7792 0.2360 0.004 0.460 0.304 0.232
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.2174 0.3821 0.928 0.000 0.020 0.052
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0779 0.8457 0.004 0.980 0.000 0.016
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.1022 0.8445 0.000 0.968 0.000 0.032
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.5775 0.2131 0.560 0.000 0.408 0.032
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.5096 0.4215 0.760 0.000 0.084 0.156
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.6815 0.3798 0.136 0.000 0.580 0.284
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.6653 0.1919 0.624 0.000 0.196 0.180
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.7802 0.0174 0.304 0.000 0.420 0.276
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 3 0.8743 0.4016 0.108 0.124 0.476 0.292
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 3 0.7285 0.3193 0.176 0.000 0.516 0.308
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.7911 0.0071 0.348 0.000 0.348 0.304
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.6969 0.2058 0.584 0.000 0.224 0.192
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.2466 0.8204 0.004 0.900 0.000 0.096
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.3870 0.1783 0.788 0.000 0.004 0.208
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.5790 0.9505 0.340 0.000 0.044 0.616
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 3 0.7795 0.1000 0.000 0.344 0.404 0.252
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0817 0.8455 0.000 0.976 0.000 0.024
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.6224 -0.0221 0.516 0.436 0.004 0.044
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.5240 0.5788 0.000 0.072 0.740 0.188
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.6648 0.0690 0.612 0.000 0.140 0.248
#> A60DC925-7343-496E-900D-0DD81D5C8123 1 0.6345 -0.0171 0.520 0.424 0.004 0.052
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.6875 0.2039 0.596 0.000 0.220 0.184
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.1398 0.8415 0.004 0.956 0.000 0.040
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.7421 0.3394 0.172 0.000 0.456 0.372
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.5790 0.9505 0.340 0.000 0.044 0.616
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.1722 0.6048 0.048 0.000 0.944 0.008
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0592 0.8471 0.000 0.984 0.000 0.016
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.7135 0.3089 0.560 0.000 0.200 0.240
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.4959 0.5852 0.000 0.052 0.752 0.196
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.6552 0.6630 0.080 0.680 0.036 0.204
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.5856 -0.0352 0.464 0.000 0.504 0.032
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0817 0.6282 0.000 0.000 0.976 0.024
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0376 0.6254 0.004 0.000 0.992 0.004
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.7366 0.3615 0.000 0.524 0.252 0.224
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.2281 0.8181 0.000 0.904 0.000 0.096
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0469 0.8471 0.000 0.988 0.000 0.012
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.7888 -0.0580 0.320 0.000 0.380 0.300
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.7798 0.1196 0.000 0.336 0.408 0.256
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.6893 0.1278 0.488 0.000 0.360 0.072 0.080
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 3 0.8916 0.1607 0.020 0.224 0.296 0.288 0.172
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.2569 0.8309 0.000 0.896 0.012 0.076 0.016
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.3970 0.6068 0.000 0.744 0.000 0.020 0.236
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.7099 0.2119 0.100 0.000 0.220 0.120 0.560
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.7083 0.1631 0.040 0.000 0.444 0.148 0.368
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 5 0.6827 -0.0719 0.012 0.000 0.356 0.192 0.440
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 4 0.8781 -0.3591 0.012 0.256 0.280 0.288 0.164
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.2136 0.6324 0.088 0.000 0.904 0.000 0.008
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.1671 0.6089 0.924 0.000 0.076 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.4015 0.5384 0.204 0.000 0.768 0.012 0.016
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 5 0.6135 0.1519 0.000 0.272 0.004 0.156 0.568
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.4665 0.5597 0.000 0.012 0.760 0.088 0.140
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 5 0.4830 -0.1115 0.488 0.000 0.020 0.000 0.492
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.2248 0.6303 0.088 0.000 0.900 0.000 0.012
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 5 0.8473 -0.1327 0.000 0.284 0.176 0.228 0.312
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.1444 0.8427 0.000 0.948 0.000 0.040 0.012
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.4499 0.4349 0.684 0.000 0.016 0.008 0.292
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0566 0.8389 0.000 0.984 0.000 0.004 0.012
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.5241 0.8589 0.356 0.000 0.008 0.596 0.040
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.1041 0.8438 0.000 0.964 0.000 0.032 0.004
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.4914 0.5557 0.012 0.004 0.748 0.092 0.144
#> F325847E-F046-4B67-B01C-16919C401020 5 0.5078 0.2446 0.020 0.000 0.464 0.008 0.508
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.4035 0.5116 0.156 0.000 0.784 0.000 0.060
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.1978 0.8224 0.004 0.928 0.000 0.044 0.024
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.5241 0.8589 0.356 0.000 0.008 0.596 0.040
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.4436 0.5609 0.000 0.004 0.768 0.088 0.140
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 5 0.6132 0.1006 0.364 0.000 0.108 0.008 0.520
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 5 0.5808 0.3395 0.096 0.000 0.392 0.000 0.512
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.4173 0.4360 0.688 0.000 0.012 0.000 0.300
#> A8E48877-F8AB-44DD-A18B-194D87C44931 3 0.8872 0.1964 0.020 0.204 0.316 0.288 0.172
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.1200 0.6271 0.012 0.000 0.964 0.008 0.016
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.2693 0.8068 0.016 0.896 0.000 0.060 0.028
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.2388 0.6095 0.900 0.000 0.072 0.028 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.8088 -0.0687 0.356 0.000 0.168 0.132 0.344
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.1314 0.5740 0.960 0.000 0.012 0.012 0.016
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.5139 0.8539 0.360 0.000 0.004 0.596 0.040
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.2136 0.6322 0.088 0.000 0.904 0.000 0.008
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.7127 0.3291 0.020 0.012 0.512 0.212 0.244
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.2102 0.8379 0.000 0.916 0.004 0.068 0.012
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.2248 0.6303 0.088 0.000 0.900 0.000 0.012
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.5241 0.8589 0.356 0.000 0.008 0.596 0.040
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.5055 0.5317 0.000 0.008 0.720 0.112 0.160
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.5241 0.8589 0.356 0.000 0.008 0.596 0.040
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.7091 0.0212 0.360 0.000 0.068 0.104 0.468
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 5 0.5868 0.3422 0.104 0.000 0.380 0.000 0.516
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 3 0.8886 0.1912 0.020 0.204 0.312 0.288 0.176
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.2786 0.6243 0.084 0.000 0.884 0.012 0.020
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.7809 0.0235 0.332 0.000 0.168 0.096 0.404
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.7488 -0.1300 0.020 0.016 0.356 0.208 0.400
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.4529 0.3286 0.156 0.000 0.036 0.036 0.772
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.3750 0.4905 0.756 0.000 0.000 0.012 0.232
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 5 0.5235 0.3600 0.028 0.000 0.060 0.208 0.704
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.5737 0.3288 0.148 0.000 0.084 0.068 0.700
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 5 0.6270 0.0340 0.032 0.428 0.004 0.056 0.480
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.1012 0.5827 0.968 0.000 0.020 0.012 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 5 0.6785 -0.2966 0.224 0.000 0.004 0.380 0.392
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.3984 0.7751 0.000 0.804 0.012 0.140 0.044
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.7567 0.1929 0.220 0.000 0.208 0.084 0.488
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.8022 -0.0179 0.376 0.000 0.172 0.120 0.332
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.2388 0.6095 0.900 0.000 0.072 0.028 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 5 0.5846 0.3571 0.088 0.000 0.380 0.004 0.528
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.1012 0.8349 0.000 0.968 0.000 0.012 0.020
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.3359 0.5506 0.816 0.000 0.020 0.000 0.164
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.7231 0.2275 0.104 0.000 0.244 0.116 0.536
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0404 0.8394 0.000 0.988 0.000 0.000 0.012
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.5049 0.3513 0.124 0.000 0.080 0.044 0.752
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.4474 0.3536 0.332 0.000 0.652 0.004 0.012
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.5139 0.8539 0.360 0.000 0.004 0.596 0.040
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.1300 0.8369 0.000 0.956 0.000 0.016 0.028
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0566 0.8389 0.000 0.984 0.000 0.004 0.012
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.1012 0.5827 0.968 0.000 0.020 0.012 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 5 0.4980 -0.1112 0.484 0.000 0.028 0.000 0.488
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.1989 0.6254 0.020 0.000 0.932 0.016 0.032
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.2615 0.6368 0.080 0.000 0.892 0.008 0.020
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.5241 0.8589 0.356 0.000 0.008 0.596 0.040
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.5033 0.3725 0.092 0.000 0.128 0.032 0.748
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.1877 0.8397 0.000 0.924 0.000 0.064 0.012
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.2880 0.6113 0.108 0.000 0.868 0.004 0.020
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.2011 0.6336 0.088 0.000 0.908 0.000 0.004
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.4356 0.3541 0.340 0.000 0.648 0.000 0.012
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.3395 0.4997 0.764 0.000 0.000 0.000 0.236
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.5919 0.7621 0.296 0.016 0.016 0.616 0.056
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.2189 0.6320 0.084 0.000 0.904 0.000 0.012
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.5241 0.8589 0.356 0.000 0.008 0.596 0.040
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.1877 0.8397 0.000 0.924 0.000 0.064 0.012
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.5755 0.6759 0.000 0.700 0.072 0.144 0.084
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.8243 -0.0398 0.000 0.340 0.328 0.184 0.148
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.4249 0.4388 0.688 0.000 0.016 0.000 0.296
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.1877 0.8397 0.000 0.924 0.000 0.064 0.012
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.1818 0.8246 0.000 0.932 0.000 0.044 0.024
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 5 0.5846 0.3506 0.088 0.000 0.380 0.004 0.528
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.4530 0.3234 0.164 0.000 0.036 0.032 0.768
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.8102 0.0876 0.256 0.000 0.320 0.096 0.328
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.1597 0.6118 0.940 0.000 0.048 0.000 0.012
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 5 0.8088 0.0247 0.332 0.000 0.220 0.104 0.344
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.9171 -0.0423 0.356 0.052 0.188 0.168 0.236
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.8160 0.1445 0.228 0.000 0.288 0.116 0.368
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.6699 0.2766 0.576 0.000 0.080 0.084 0.260
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.1608 0.6099 0.928 0.000 0.072 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.3850 0.7818 0.000 0.816 0.012 0.128 0.044
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.2773 0.5270 0.836 0.000 0.000 0.000 0.164
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.5241 0.8589 0.356 0.000 0.008 0.596 0.040
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 3 0.8872 0.1964 0.020 0.204 0.316 0.288 0.172
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0798 0.8367 0.000 0.976 0.000 0.008 0.016
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 5 0.6275 0.0982 0.068 0.420 0.004 0.024 0.484
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.4503 0.5622 0.000 0.008 0.768 0.084 0.140
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.1012 0.5827 0.968 0.000 0.020 0.012 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 5 0.6664 0.0764 0.060 0.400 0.000 0.068 0.472
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.1608 0.6099 0.928 0.000 0.072 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.3143 0.7936 0.016 0.872 0.000 0.068 0.044
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.7393 0.1966 0.128 0.000 0.224 0.120 0.528
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.5241 0.8589 0.356 0.000 0.008 0.596 0.040
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.2677 0.6113 0.112 0.000 0.872 0.000 0.016
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0963 0.8438 0.000 0.964 0.000 0.036 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.2878 0.3937 0.048 0.000 0.048 0.016 0.888
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.4847 0.5544 0.004 0.008 0.748 0.092 0.148
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.7899 0.1954 0.000 0.352 0.072 0.260 0.316
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 5 0.5522 0.3085 0.056 0.000 0.424 0.004 0.516
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.1597 0.6299 0.024 0.000 0.948 0.008 0.020
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.2011 0.6336 0.088 0.000 0.908 0.000 0.004
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.8475 0.1933 0.020 0.424 0.248 0.156 0.152
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.4841 0.7136 0.000 0.716 0.012 0.220 0.052
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0963 0.8438 0.000 0.964 0.000 0.036 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.8022 -0.0179 0.376 0.000 0.172 0.120 0.332
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.8872 0.1964 0.020 0.204 0.316 0.288 0.172
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.7218 -0.16117 0.376 0.000 0.388 0.028 0.068 0.140
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 6 0.3607 0.69920 0.004 0.084 0.076 0.008 0.004 0.824
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.3181 0.79600 0.020 0.840 0.000 0.028 0.000 0.112
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.4632 0.50637 0.004 0.652 0.000 0.020 0.300 0.024
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.7067 0.33312 0.104 0.000 0.060 0.056 0.504 0.276
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 6 0.6474 0.41460 0.012 0.000 0.156 0.024 0.360 0.448
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 6 0.5918 0.53877 0.004 0.000 0.124 0.032 0.264 0.576
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 6 0.3462 0.69783 0.000 0.088 0.072 0.008 0.004 0.828
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0146 0.77143 0.000 0.000 0.996 0.000 0.004 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.1912 0.79064 0.924 0.000 0.052 0.008 0.008 0.008
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.2265 0.74335 0.044 0.000 0.912 0.012 0.020 0.012
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 6 0.6197 0.30097 0.000 0.184 0.000 0.016 0.388 0.412
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.5008 0.34141 0.000 0.004 0.596 0.032 0.024 0.344
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.4566 0.23152 0.492 0.000 0.000 0.020 0.480 0.008
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0260 0.77062 0.000 0.000 0.992 0.000 0.008 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 6 0.6110 0.61443 0.008 0.120 0.028 0.028 0.184 0.632
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.2537 0.81858 0.020 0.896 0.000 0.032 0.004 0.048
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.3568 0.71588 0.788 0.000 0.000 0.008 0.172 0.032
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.1346 0.82304 0.000 0.952 0.000 0.008 0.024 0.016
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.2562 0.95422 0.172 0.000 0.000 0.828 0.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.1657 0.82529 0.012 0.936 0.000 0.012 0.000 0.040
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.5264 0.37604 0.004 0.004 0.604 0.036 0.032 0.320
#> F325847E-F046-4B67-B01C-16919C401020 3 0.4829 -0.06542 0.004 0.000 0.488 0.008 0.472 0.028
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.1401 0.74441 0.020 0.000 0.948 0.000 0.028 0.004
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.2869 0.79647 0.008 0.880 0.000 0.036 0.052 0.024
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.2562 0.95422 0.172 0.000 0.000 0.828 0.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.4874 0.34993 0.000 0.000 0.600 0.032 0.024 0.344
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 5 0.5993 0.04013 0.332 0.000 0.116 0.012 0.524 0.016
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 5 0.5712 0.15666 0.080 0.000 0.412 0.016 0.484 0.008
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.2772 0.72344 0.816 0.000 0.000 0.000 0.180 0.004
#> A8E48877-F8AB-44DD-A18B-194D87C44931 6 0.3663 0.70107 0.004 0.072 0.084 0.008 0.008 0.824
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.2307 0.73794 0.000 0.000 0.900 0.024 0.012 0.064
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.3824 0.76452 0.012 0.824 0.000 0.048 0.072 0.044
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.2050 0.78825 0.920 0.000 0.048 0.008 0.012 0.012
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.8085 0.40004 0.224 0.000 0.072 0.092 0.384 0.228
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.2058 0.79356 0.924 0.000 0.028 0.024 0.012 0.012
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.2703 0.95311 0.172 0.000 0.000 0.824 0.000 0.004
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0000 0.77149 0.000 0.000 1.000 0.000 0.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 6 0.5305 0.53931 0.004 0.008 0.232 0.032 0.060 0.664
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.3042 0.80356 0.020 0.852 0.000 0.028 0.000 0.100
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0146 0.77143 0.000 0.000 0.996 0.000 0.004 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.2562 0.95422 0.172 0.000 0.000 0.828 0.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.5234 0.11489 0.000 0.004 0.512 0.036 0.024 0.424
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.2562 0.95422 0.172 0.000 0.000 0.828 0.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.6912 0.37840 0.280 0.000 0.012 0.064 0.476 0.168
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 5 0.5811 0.17774 0.092 0.000 0.396 0.016 0.488 0.008
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 6 0.3663 0.70107 0.004 0.072 0.084 0.008 0.008 0.824
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.1223 0.76515 0.004 0.000 0.960 0.008 0.012 0.016
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.7510 0.38321 0.256 0.000 0.064 0.040 0.428 0.212
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 6 0.5670 0.50575 0.008 0.008 0.108 0.028 0.196 0.652
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.3695 0.45328 0.096 0.000 0.008 0.032 0.824 0.040
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.3983 0.72318 0.776 0.000 0.000 0.024 0.156 0.044
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 6 0.4644 0.41596 0.008 0.000 0.024 0.004 0.380 0.584
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.6112 0.46028 0.120 0.000 0.020 0.064 0.632 0.164
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 5 0.6432 -0.11591 0.040 0.404 0.000 0.048 0.456 0.052
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.2179 0.79308 0.916 0.000 0.040 0.024 0.008 0.012
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.5347 0.47318 0.136 0.000 0.000 0.560 0.304 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.4700 0.58421 0.020 0.660 0.000 0.032 0.004 0.284
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.7221 0.45570 0.144 0.000 0.096 0.060 0.544 0.156
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.8185 0.39093 0.252 0.000 0.080 0.096 0.368 0.204
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.2050 0.78825 0.920 0.000 0.048 0.008 0.012 0.012
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 5 0.6013 0.18268 0.088 0.000 0.396 0.016 0.480 0.020
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.1722 0.81906 0.004 0.936 0.000 0.016 0.036 0.008
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.1700 0.78575 0.916 0.000 0.000 0.000 0.080 0.004
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.7057 0.35122 0.104 0.000 0.064 0.056 0.516 0.260
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.1003 0.82498 0.000 0.964 0.000 0.000 0.020 0.016
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.4608 0.44323 0.084 0.000 0.016 0.060 0.772 0.068
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.2773 0.63934 0.152 0.000 0.836 0.000 0.008 0.004
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.2703 0.95311 0.172 0.000 0.000 0.824 0.000 0.004
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.2037 0.82214 0.012 0.924 0.000 0.012 0.036 0.016
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.1346 0.82304 0.000 0.952 0.000 0.008 0.024 0.016
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.2179 0.79308 0.916 0.000 0.040 0.024 0.008 0.012
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.4536 0.25529 0.512 0.000 0.004 0.012 0.464 0.008
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.2342 0.73918 0.000 0.000 0.904 0.032 0.024 0.040
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.2271 0.74177 0.000 0.000 0.908 0.032 0.024 0.036
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.2562 0.95422 0.172 0.000 0.000 0.828 0.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.3857 0.45864 0.072 0.000 0.052 0.020 0.824 0.032
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.3060 0.80901 0.020 0.860 0.000 0.032 0.004 0.084
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0665 0.76743 0.004 0.000 0.980 0.000 0.008 0.008
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0146 0.77143 0.000 0.000 0.996 0.000 0.004 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.2653 0.66446 0.144 0.000 0.844 0.000 0.012 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.3056 0.75165 0.832 0.000 0.000 0.012 0.140 0.016
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.2726 0.90810 0.136 0.000 0.000 0.848 0.008 0.008
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0405 0.77103 0.000 0.000 0.988 0.000 0.008 0.004
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.2703 0.95311 0.172 0.000 0.000 0.824 0.000 0.004
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.3060 0.80901 0.020 0.860 0.000 0.032 0.004 0.084
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.5492 0.32678 0.012 0.564 0.004 0.040 0.024 0.356
#> 53A96249-66D5-4C26-893B-ADC71481D261 6 0.6670 0.43411 0.000 0.304 0.124 0.040 0.028 0.504
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.2738 0.72720 0.820 0.000 0.000 0.000 0.176 0.004
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.3060 0.80901 0.020 0.860 0.000 0.032 0.004 0.084
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.2919 0.79519 0.008 0.876 0.000 0.040 0.056 0.020
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 5 0.5611 0.15284 0.056 0.000 0.416 0.012 0.496 0.020
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.3674 0.45021 0.100 0.000 0.008 0.032 0.824 0.036
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.8197 0.33710 0.156 0.000 0.204 0.064 0.392 0.184
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.1590 0.79657 0.936 0.000 0.048 0.008 0.008 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 5 0.8094 0.40524 0.232 0.000 0.128 0.064 0.400 0.176
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 6 0.7619 0.13293 0.312 0.020 0.056 0.040 0.136 0.436
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.7884 0.37371 0.136 0.000 0.140 0.068 0.452 0.204
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.6664 -0.00898 0.504 0.000 0.032 0.020 0.264 0.180
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.1912 0.79064 0.924 0.000 0.052 0.008 0.008 0.008
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.4557 0.62191 0.020 0.688 0.000 0.032 0.004 0.256
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.2415 0.78090 0.888 0.000 0.000 0.012 0.084 0.016
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.2562 0.95422 0.172 0.000 0.000 0.828 0.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 6 0.3603 0.70048 0.004 0.072 0.088 0.008 0.004 0.824
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.1659 0.81975 0.004 0.940 0.000 0.008 0.028 0.020
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 5 0.6059 -0.02850 0.084 0.408 0.000 0.020 0.468 0.020
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.4982 0.35676 0.000 0.004 0.604 0.032 0.024 0.336
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.2179 0.79308 0.916 0.000 0.040 0.024 0.008 0.012
#> A60DC925-7343-496E-900D-0DD81D5C8123 5 0.6889 -0.14614 0.052 0.404 0.000 0.052 0.416 0.076
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.1655 0.79416 0.932 0.000 0.052 0.008 0.008 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.4716 0.70399 0.012 0.756 0.000 0.048 0.112 0.072
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.7116 0.33349 0.120 0.000 0.052 0.056 0.492 0.280
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.2562 0.95422 0.172 0.000 0.000 0.828 0.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0508 0.76862 0.004 0.000 0.984 0.000 0.012 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.1624 0.82735 0.008 0.936 0.000 0.012 0.000 0.044
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.3212 0.41302 0.044 0.000 0.004 0.028 0.856 0.068
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.4983 0.30877 0.000 0.000 0.580 0.036 0.024 0.360
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 6 0.4838 0.61783 0.008 0.128 0.004 0.004 0.144 0.712
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 5 0.5603 0.12969 0.048 0.000 0.432 0.020 0.484 0.016
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.2477 0.73425 0.000 0.000 0.896 0.032 0.024 0.048
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.77149 0.000 0.000 1.000 0.000 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 6 0.7353 0.25202 0.016 0.380 0.104 0.052 0.040 0.408
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.4727 0.24006 0.020 0.504 0.000 0.016 0.000 0.460
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.1624 0.82737 0.008 0.936 0.000 0.012 0.000 0.044
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.8185 0.39093 0.252 0.000 0.080 0.096 0.368 0.204
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 6 0.3603 0.70048 0.004 0.072 0.088 0.008 0.004 0.824
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "skmeans"]
# you can also extract it by
# res = res_list["MAD:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.971 0.987 0.4917 0.506 0.506
#> 3 3 0.630 0.717 0.848 0.3504 0.783 0.590
#> 4 4 0.651 0.529 0.769 0.1223 0.858 0.612
#> 5 5 0.708 0.545 0.758 0.0585 0.950 0.813
#> 6 6 0.792 0.684 0.827 0.0495 0.915 0.653
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.000 0.994 1.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.000 0.977 0.000 1.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.000 0.977 0.000 1.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.000 0.977 0.000 1.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 2 0.722 0.761 0.200 0.800
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 2 0.949 0.442 0.368 0.632
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 2 0.000 0.977 0.000 1.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.000 0.977 0.000 1.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.000 0.994 1.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.000 0.994 1.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.000 0.994 1.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.000 0.977 0.000 1.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.000 0.977 0.000 1.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.000 0.994 1.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.000 0.994 1.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.000 0.977 0.000 1.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.000 0.977 0.000 1.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.000 0.994 1.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.000 0.977 0.000 1.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.000 0.994 1.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.000 0.977 0.000 1.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.000 0.977 0.000 1.000
#> F325847E-F046-4B67-B01C-16919C401020 1 0.000 0.994 1.000 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.000 0.994 1.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.000 0.977 0.000 1.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.000 0.994 1.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 2 0.000 0.977 0.000 1.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.000 0.994 1.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.000 0.994 1.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.000 0.994 1.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.000 0.977 0.000 1.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.000 0.994 1.000 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.000 0.977 0.000 1.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.000 0.994 1.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.000 0.994 1.000 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.000 0.994 1.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.000 0.994 1.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.000 0.994 1.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.000 0.977 0.000 1.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.000 0.977 0.000 1.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.000 0.994 1.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.000 0.994 1.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.000 0.977 0.000 1.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.000 0.994 1.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.000 0.994 1.000 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.000 0.994 1.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.000 0.977 0.000 1.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.000 0.994 1.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.141 0.974 0.980 0.020
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.000 0.977 0.000 1.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.000 0.994 1.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.000 0.994 1.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 2 0.730 0.755 0.204 0.796
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.000 0.994 1.000 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.000 0.977 0.000 1.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.000 0.994 1.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.000 0.994 1.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.000 0.977 0.000 1.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.000 0.994 1.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.000 0.994 1.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.000 0.994 1.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.000 0.994 1.000 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.000 0.977 0.000 1.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.000 0.994 1.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.917 0.481 0.668 0.332
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.000 0.977 0.000 1.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.000 0.994 1.000 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.000 0.994 1.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.000 0.994 1.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.000 0.977 0.000 1.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.000 0.977 0.000 1.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.000 0.994 1.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.000 0.994 1.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.295 0.940 0.948 0.052
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.000 0.994 1.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.000 0.994 1.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.000 0.994 1.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.000 0.977 0.000 1.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.000 0.994 1.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.000 0.994 1.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.000 0.994 1.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.000 0.994 1.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.000 0.977 0.000 1.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.000 0.994 1.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.000 0.994 1.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.000 0.977 0.000 1.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.000 0.977 0.000 1.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.000 0.977 0.000 1.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.000 0.994 1.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.000 0.977 0.000 1.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.000 0.977 0.000 1.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.000 0.994 1.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.000 0.994 1.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.000 0.994 1.000 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.000 0.994 1.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.000 0.994 1.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.000 0.977 0.000 1.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.000 0.994 1.000 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.141 0.974 0.980 0.020
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.000 0.994 1.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.000 0.977 0.000 1.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.000 0.994 1.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.000 0.994 1.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.000 0.977 0.000 1.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.000 0.977 0.000 1.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.000 0.977 0.000 1.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.000 0.977 0.000 1.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.000 0.994 1.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.000 0.977 0.000 1.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.000 0.994 1.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.000 0.977 0.000 1.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 2 0.722 0.761 0.200 0.800
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.000 0.994 1.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.000 0.994 1.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.000 0.977 0.000 1.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 2 0.745 0.743 0.212 0.788
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.000 0.977 0.000 1.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.000 0.977 0.000 1.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.000 0.994 1.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.000 0.994 1.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.000 0.994 1.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.000 0.977 0.000 1.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.000 0.977 0.000 1.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.000 0.977 0.000 1.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.000 0.994 1.000 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.000 0.977 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.2066 0.703 0.940 0.000 0.060
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.0000 0.894 0.000 1.000 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.894 0.000 1.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.4555 0.723 0.000 0.800 0.200
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 2 0.9429 0.324 0.264 0.504 0.232
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.3644 0.665 0.124 0.004 0.872
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.5327 0.449 0.000 0.272 0.728
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0000 0.894 0.000 1.000 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.5733 0.749 0.324 0.000 0.676
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0747 0.734 0.984 0.000 0.016
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.6305 0.521 0.484 0.000 0.516
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.4555 0.723 0.000 0.800 0.200
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.6008 0.457 0.000 0.372 0.628
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.5733 0.729 0.676 0.000 0.324
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.5733 0.749 0.324 0.000 0.676
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.0000 0.894 0.000 1.000 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.894 0.000 1.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.5733 0.729 0.676 0.000 0.324
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.894 0.000 1.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.3412 0.786 0.876 0.000 0.124
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.894 0.000 1.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.6566 0.496 0.016 0.348 0.636
#> F325847E-F046-4B67-B01C-16919C401020 3 0.1753 0.624 0.048 0.000 0.952
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.5733 0.749 0.324 0.000 0.676
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.894 0.000 1.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.3412 0.786 0.876 0.000 0.124
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.7381 0.628 0.080 0.244 0.676
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.5785 0.727 0.668 0.000 0.332
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.1860 0.538 0.052 0.000 0.948
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.5733 0.729 0.676 0.000 0.324
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.0000 0.894 0.000 1.000 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.5733 0.749 0.324 0.000 0.676
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.894 0.000 1.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0747 0.734 0.984 0.000 0.016
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.4235 0.769 0.824 0.000 0.176
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.3412 0.786 0.876 0.000 0.124
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.3412 0.786 0.876 0.000 0.124
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.5733 0.749 0.324 0.000 0.676
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.5621 0.434 0.000 0.692 0.308
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.894 0.000 1.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.5733 0.749 0.324 0.000 0.676
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.3412 0.786 0.876 0.000 0.124
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.6045 0.442 0.000 0.380 0.620
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.3412 0.786 0.876 0.000 0.124
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.5733 0.729 0.676 0.000 0.324
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.3038 0.471 0.104 0.000 0.896
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.0000 0.894 0.000 1.000 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.5733 0.749 0.324 0.000 0.676
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.2261 0.687 0.932 0.000 0.068
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.0000 0.894 0.000 1.000 0.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.5760 0.728 0.672 0.000 0.328
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.5733 0.729 0.676 0.000 0.324
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 2 0.6906 0.554 0.032 0.644 0.324
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.5882 0.721 0.652 0.000 0.348
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.5733 0.591 0.000 0.676 0.324
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.744 1.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.5733 0.729 0.676 0.000 0.324
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.894 0.000 1.000 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.3686 0.600 0.860 0.000 0.140
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.2165 0.696 0.936 0.000 0.064
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0747 0.734 0.984 0.000 0.016
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.3116 0.464 0.108 0.000 0.892
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.894 0.000 1.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.5733 0.729 0.676 0.000 0.324
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.6633 0.536 0.444 0.008 0.548
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.894 0.000 1.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.5882 0.721 0.652 0.000 0.348
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.6267 -0.385 0.548 0.000 0.452
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.3412 0.786 0.876 0.000 0.124
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.894 0.000 1.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.894 0.000 1.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.744 1.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.5733 0.729 0.676 0.000 0.324
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.5733 0.749 0.324 0.000 0.676
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.5733 0.749 0.324 0.000 0.676
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.3412 0.786 0.876 0.000 0.124
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.6062 0.703 0.616 0.000 0.384
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.894 0.000 1.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.5733 0.749 0.324 0.000 0.676
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.5733 0.749 0.324 0.000 0.676
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.6308 0.508 0.492 0.000 0.508
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.5733 0.729 0.676 0.000 0.324
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.0592 0.884 0.012 0.988 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.5733 0.749 0.324 0.000 0.676
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.3412 0.786 0.876 0.000 0.124
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.894 0.000 1.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0000 0.894 0.000 1.000 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0000 0.894 0.000 1.000 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.5733 0.729 0.676 0.000 0.324
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.894 0.000 1.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.894 0.000 1.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.1163 0.563 0.028 0.000 0.972
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.5760 0.728 0.672 0.000 0.328
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.3941 0.561 0.844 0.000 0.156
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.744 1.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.2796 0.662 0.908 0.000 0.092
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.6935 0.339 0.372 0.604 0.024
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.3879 0.569 0.848 0.000 0.152
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.1031 0.726 0.976 0.000 0.024
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0747 0.734 0.984 0.000 0.016
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.894 0.000 1.000 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.5733 0.729 0.676 0.000 0.324
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.3412 0.786 0.876 0.000 0.124
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.0000 0.894 0.000 1.000 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.894 0.000 1.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.6906 0.554 0.032 0.644 0.324
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.6062 0.434 0.000 0.384 0.616
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.744 1.000 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.8554 0.444 0.116 0.560 0.324
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0747 0.734 0.984 0.000 0.016
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.894 0.000 1.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 2 0.7491 0.137 0.472 0.492 0.036
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.3412 0.786 0.876 0.000 0.124
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.5733 0.749 0.324 0.000 0.676
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.894 0.000 1.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 2 0.9544 0.212 0.208 0.464 0.328
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.5948 0.477 0.000 0.360 0.640
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0000 0.894 0.000 1.000 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.1031 0.565 0.024 0.000 0.976
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.5733 0.749 0.324 0.000 0.676
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.5733 0.749 0.324 0.000 0.676
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0000 0.894 0.000 1.000 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.894 0.000 1.000 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.894 0.000 1.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.2165 0.696 0.936 0.000 0.064
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.0000 0.894 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.6535 0.3208 0.588 0.000 0.100 0.312
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.1297 0.8987 0.020 0.964 0.000 0.016
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.4250 0.6312 0.000 0.724 0.276 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 4 0.6945 0.4000 0.076 0.028 0.296 0.600
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.4004 0.4555 0.064 0.004 0.844 0.088
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.4572 0.3287 0.024 0.164 0.796 0.016
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.1297 0.8987 0.020 0.964 0.000 0.016
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.4888 0.7592 0.412 0.000 0.588 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.4624 0.4502 0.660 0.000 0.000 0.340
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.5080 -0.5811 0.576 0.000 0.420 0.004
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.5928 0.4260 0.020 0.560 0.408 0.012
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.7053 0.7077 0.336 0.100 0.552 0.012
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.6285 0.2789 0.528 0.000 0.412 0.060
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.4888 0.7592 0.412 0.000 0.588 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.1174 0.8998 0.020 0.968 0.000 0.012
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.6453 0.3629 0.560 0.000 0.360 0.080
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.4697 0.0735 0.356 0.000 0.000 0.644
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.6926 0.7170 0.360 0.084 0.544 0.012
#> F325847E-F046-4B67-B01C-16919C401020 3 0.1305 0.4756 0.004 0.000 0.960 0.036
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.4888 0.7592 0.412 0.000 0.588 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.4697 0.0735 0.356 0.000 0.000 0.644
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.6531 0.7306 0.368 0.056 0.564 0.012
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.6527 0.2552 0.508 0.000 0.416 0.076
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.2408 0.4231 0.044 0.000 0.920 0.036
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.6453 0.3629 0.560 0.000 0.360 0.080
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.1297 0.8987 0.020 0.964 0.000 0.016
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.4888 0.7592 0.412 0.000 0.588 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.4624 0.4502 0.660 0.000 0.000 0.340
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 4 0.2796 0.4259 0.092 0.000 0.016 0.892
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.4941 0.3910 0.564 0.000 0.000 0.436
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.4697 0.0735 0.356 0.000 0.000 0.644
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.4888 0.7592 0.412 0.000 0.588 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.5585 0.6177 0.064 0.732 0.192 0.012
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.4888 0.7592 0.412 0.000 0.588 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.4697 0.0735 0.356 0.000 0.000 0.644
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.7446 0.6785 0.336 0.136 0.516 0.012
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.4697 0.0735 0.356 0.000 0.000 0.644
#> CB207A52-09AC-49D3-8240-5840CDFBB154 4 0.1724 0.4117 0.032 0.000 0.020 0.948
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.3793 0.3096 0.112 0.000 0.844 0.044
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.1297 0.8987 0.020 0.964 0.000 0.016
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.4888 0.7592 0.412 0.000 0.588 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 4 0.3545 0.4054 0.164 0.000 0.008 0.828
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.4436 0.6916 0.020 0.764 0.000 0.216
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 4 0.6292 0.3524 0.060 0.000 0.416 0.524
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.7175 0.4002 0.556 0.000 0.220 0.224
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 4 0.8119 0.2689 0.036 0.140 0.412 0.412
#> AD294665-6F90-459C-90D5-3058F210225D 4 0.6114 0.3555 0.048 0.000 0.428 0.524
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.5039 0.4394 0.004 0.592 0.404 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.4804 0.4362 0.616 0.000 0.000 0.384
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 3 0.7803 -0.4731 0.352 0.000 0.396 0.252
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0937 0.9022 0.012 0.976 0.000 0.012
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 4 0.6571 0.4070 0.124 0.000 0.264 0.612
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 4 0.3743 0.4156 0.160 0.000 0.016 0.824
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.4624 0.4502 0.660 0.000 0.000 0.340
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.3850 0.3059 0.116 0.000 0.840 0.044
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.6409 0.4258 0.560 0.000 0.076 0.364
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 4 0.6570 0.4003 0.116 0.000 0.280 0.604
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 4 0.6114 0.3555 0.048 0.000 0.428 0.524
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.5150 -0.5419 0.596 0.000 0.396 0.008
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.4697 0.0735 0.356 0.000 0.000 0.644
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.4804 0.4362 0.616 0.000 0.000 0.384
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.6222 0.2835 0.532 0.000 0.412 0.056
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.4888 0.7592 0.412 0.000 0.588 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.5060 0.7569 0.412 0.000 0.584 0.004
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.4697 0.0735 0.356 0.000 0.000 0.644
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 4 0.6257 0.3481 0.056 0.000 0.436 0.508
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.4888 0.7592 0.412 0.000 0.588 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.4888 0.7592 0.412 0.000 0.588 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.4790 -0.5302 0.620 0.000 0.380 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.7050 0.4035 0.564 0.000 0.264 0.172
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.6752 0.1475 0.132 0.280 0.000 0.588
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.4888 0.7592 0.412 0.000 0.588 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.4697 0.0735 0.356 0.000 0.000 0.644
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0937 0.9022 0.012 0.976 0.000 0.012
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.6453 0.3629 0.560 0.000 0.360 0.080
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.2002 0.4416 0.020 0.000 0.936 0.044
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 4 0.6354 0.3502 0.064 0.000 0.416 0.520
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 4 0.5448 0.3625 0.196 0.000 0.080 0.724
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.4624 0.4502 0.660 0.000 0.000 0.340
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 4 0.4010 0.4142 0.156 0.000 0.028 0.816
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.7201 0.1810 0.140 0.532 0.004 0.324
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 4 0.4418 0.4093 0.184 0.000 0.032 0.784
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 4 0.4713 0.1227 0.360 0.000 0.000 0.640
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.4624 0.4502 0.660 0.000 0.000 0.340
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0937 0.9022 0.012 0.976 0.000 0.012
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.6465 0.4207 0.556 0.000 0.080 0.364
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.4697 0.0735 0.356 0.000 0.000 0.644
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.1297 0.8987 0.020 0.964 0.000 0.016
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.6407 0.3197 0.068 0.520 0.412 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.7354 0.6875 0.332 0.128 0.528 0.012
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.4817 0.4337 0.612 0.000 0.000 0.388
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.8664 0.2086 0.100 0.464 0.320 0.116
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.4624 0.4502 0.660 0.000 0.000 0.340
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 4 0.5306 0.4356 0.112 0.032 0.072 0.784
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.4697 0.0735 0.356 0.000 0.000 0.644
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.4888 0.7592 0.412 0.000 0.588 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 4 0.6659 0.3470 0.040 0.024 0.416 0.520
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.7088 0.7051 0.332 0.104 0.552 0.012
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.1297 0.8987 0.020 0.964 0.000 0.016
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.1706 0.4554 0.016 0.000 0.948 0.036
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.4888 0.7592 0.412 0.000 0.588 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.4888 0.7592 0.412 0.000 0.588 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.1174 0.8998 0.020 0.968 0.000 0.012
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.9077 0.000 1.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 4 0.3743 0.4156 0.160 0.000 0.016 0.824
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.1297 0.8987 0.020 0.964 0.000 0.016
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.3197 0.67693 0.836 0.000 0.140 0.024 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.3944 0.76250 0.000 0.788 0.000 0.052 0.160
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.84294 0.000 1.000 0.000 0.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.3508 0.53390 0.000 0.748 0.000 0.000 0.252
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 4 0.5849 0.22031 0.128 0.000 0.004 0.604 0.264
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 5 0.5821 0.24868 0.000 0.000 0.400 0.096 0.504
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 5 0.6507 0.38236 0.000 0.124 0.296 0.028 0.552
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.3944 0.76250 0.000 0.788 0.000 0.052 0.160
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.79449 0.000 0.000 1.000 0.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0693 0.79339 0.980 0.000 0.008 0.012 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.3305 0.55564 0.224 0.000 0.776 0.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 5 0.4622 -0.11052 0.000 0.440 0.000 0.012 0.548
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.2681 0.71299 0.000 0.012 0.876 0.004 0.108
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.5148 0.30171 0.528 0.000 0.000 0.040 0.432
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.79449 0.000 0.000 1.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.3656 0.75676 0.000 0.784 0.000 0.020 0.196
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.84294 0.000 1.000 0.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.3366 0.69350 0.768 0.000 0.000 0.000 0.232
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.84294 0.000 1.000 0.000 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.6347 0.36834 0.160 0.000 0.000 0.432 0.408
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.84294 0.000 1.000 0.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.2575 0.72066 0.000 0.012 0.884 0.004 0.100
#> F325847E-F046-4B67-B01C-16919C401020 3 0.5250 -0.19945 0.000 0.000 0.536 0.048 0.416
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.79449 0.000 0.000 1.000 0.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.84294 0.000 1.000 0.000 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.6347 0.36834 0.160 0.000 0.000 0.432 0.408
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.2286 0.72344 0.000 0.000 0.888 0.004 0.108
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.5816 0.25170 0.500 0.000 0.024 0.044 0.432
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.6586 -0.37799 0.084 0.000 0.444 0.040 0.432
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.3210 0.71552 0.788 0.000 0.000 0.000 0.212
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.3944 0.76250 0.000 0.788 0.000 0.052 0.160
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.0000 0.79449 0.000 0.000 1.000 0.000 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.84294 0.000 1.000 0.000 0.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0693 0.79339 0.980 0.000 0.008 0.012 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 4 0.4267 0.36601 0.180 0.000 0.020 0.772 0.028
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.1270 0.77330 0.948 0.000 0.000 0.052 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.6347 0.36834 0.160 0.000 0.000 0.432 0.408
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0000 0.79449 0.000 0.000 1.000 0.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.6451 0.40022 0.000 0.568 0.256 0.020 0.156
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.84294 0.000 1.000 0.000 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0000 0.79449 0.000 0.000 1.000 0.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.6347 0.36834 0.160 0.000 0.000 0.432 0.408
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.3310 0.66709 0.000 0.024 0.836 0.004 0.136
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.6347 0.36834 0.160 0.000 0.000 0.432 0.408
#> CB207A52-09AC-49D3-8240-5840CDFBB154 4 0.5296 0.36211 0.180 0.000 0.000 0.676 0.144
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 5 0.6875 0.31408 0.116 0.000 0.412 0.040 0.432
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.3944 0.76250 0.000 0.788 0.000 0.052 0.160
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0000 0.79449 0.000 0.000 1.000 0.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 4 0.4567 0.13178 0.448 0.000 0.004 0.544 0.004
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.5887 0.49823 0.000 0.592 0.000 0.252 0.156
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 4 0.5153 0.04023 0.040 0.000 0.000 0.524 0.436
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.4069 0.71797 0.792 0.000 0.000 0.112 0.096
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 5 0.4582 0.05775 0.000 0.012 0.000 0.416 0.572
#> AD294665-6F90-459C-90D5-3058F210225D 4 0.5166 0.05555 0.032 0.000 0.004 0.528 0.436
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.4219 0.14089 0.000 0.584 0.000 0.000 0.416
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0162 0.79159 0.996 0.000 0.000 0.004 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 5 0.4893 -0.20727 0.088 0.000 0.000 0.208 0.704
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.2488 0.80045 0.000 0.872 0.000 0.004 0.124
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 4 0.6276 0.24832 0.164 0.000 0.016 0.592 0.228
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 4 0.5079 0.33089 0.316 0.000 0.020 0.640 0.024
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0693 0.79339 0.980 0.000 0.008 0.012 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 5 0.6932 0.31927 0.116 0.000 0.408 0.044 0.432
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.84294 0.000 1.000 0.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.3019 0.76697 0.864 0.000 0.000 0.048 0.088
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 4 0.6024 0.23137 0.128 0.000 0.012 0.604 0.256
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.84294 0.000 1.000 0.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 4 0.5174 0.04571 0.032 0.000 0.004 0.520 0.444
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.4161 0.32531 0.392 0.000 0.608 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.6347 0.36834 0.160 0.000 0.000 0.432 0.408
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.84294 0.000 1.000 0.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.84294 0.000 1.000 0.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0162 0.79159 0.996 0.000 0.000 0.004 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.5083 0.30923 0.532 0.000 0.000 0.036 0.432
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0000 0.79449 0.000 0.000 1.000 0.000 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0000 0.79449 0.000 0.000 1.000 0.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.6347 0.36834 0.160 0.000 0.000 0.432 0.408
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 4 0.5504 0.02491 0.040 0.000 0.012 0.516 0.432
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.84294 0.000 1.000 0.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.79449 0.000 0.000 1.000 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0000 0.79449 0.000 0.000 1.000 0.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.3949 0.42723 0.332 0.000 0.668 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.2690 0.75729 0.844 0.000 0.000 0.000 0.156
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.6922 0.33622 0.108 0.048 0.000 0.432 0.412
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.79449 0.000 0.000 1.000 0.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.6347 0.36834 0.160 0.000 0.000 0.432 0.408
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.84294 0.000 1.000 0.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0451 0.84015 0.000 0.988 0.000 0.004 0.008
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.2488 0.80046 0.000 0.872 0.000 0.004 0.124
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.3003 0.73674 0.812 0.000 0.000 0.000 0.188
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.84294 0.000 1.000 0.000 0.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.84294 0.000 1.000 0.000 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.5987 -0.29690 0.024 0.000 0.488 0.056 0.432
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 4 0.5211 0.03955 0.044 0.000 0.000 0.524 0.432
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 4 0.6292 0.22855 0.156 0.000 0.260 0.572 0.012
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0693 0.79339 0.980 0.000 0.008 0.012 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 4 0.5264 0.35664 0.264 0.000 0.068 0.660 0.008
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.5792 0.03487 0.452 0.468 0.004 0.076 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 4 0.5500 0.30963 0.172 0.000 0.144 0.676 0.008
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.3086 0.62666 0.816 0.000 0.004 0.180 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0693 0.79339 0.980 0.000 0.008 0.012 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.2389 0.80354 0.000 0.880 0.000 0.004 0.116
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.3043 0.76697 0.864 0.000 0.000 0.056 0.080
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.6347 0.36834 0.160 0.000 0.000 0.432 0.408
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.3944 0.76250 0.000 0.788 0.000 0.052 0.160
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.84294 0.000 1.000 0.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.6033 -0.15062 0.100 0.468 0.000 0.004 0.428
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.2880 0.70406 0.000 0.020 0.868 0.004 0.108
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0162 0.79159 0.996 0.000 0.000 0.004 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.6727 -0.01465 0.008 0.448 0.000 0.192 0.352
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0693 0.79339 0.980 0.000 0.008 0.012 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.84294 0.000 1.000 0.000 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 4 0.5556 0.30880 0.204 0.000 0.004 0.656 0.136
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.6347 0.36834 0.160 0.000 0.000 0.432 0.408
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.79449 0.000 0.000 1.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.84294 0.000 1.000 0.000 0.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 4 0.4440 0.00505 0.004 0.000 0.000 0.528 0.468
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.2338 0.72041 0.000 0.000 0.884 0.004 0.112
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.3577 0.77104 0.000 0.808 0.000 0.032 0.160
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.5828 -0.28162 0.016 0.000 0.496 0.056 0.432
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0000 0.79449 0.000 0.000 1.000 0.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.79449 0.000 0.000 1.000 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0290 0.83874 0.000 0.992 0.000 0.008 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.3236 0.78036 0.000 0.828 0.000 0.020 0.152
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.84294 0.000 1.000 0.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 4 0.5079 0.33089 0.316 0.000 0.020 0.640 0.024
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.3944 0.76250 0.000 0.788 0.000 0.052 0.160
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.3549 0.6520 0.784 0.000 0.184 0.004 0.004 0.024
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 6 0.2805 0.7851 0.000 0.184 0.000 0.000 0.004 0.812
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0458 0.8362 0.000 0.984 0.000 0.000 0.000 0.016
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.3641 0.5579 0.000 0.732 0.000 0.000 0.248 0.020
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.4537 0.6498 0.060 0.000 0.000 0.008 0.684 0.248
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 6 0.5125 0.4338 0.004 0.000 0.076 0.000 0.380 0.540
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 6 0.4193 0.5926 0.000 0.008 0.028 0.000 0.276 0.688
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 6 0.2805 0.7851 0.000 0.184 0.000 0.000 0.004 0.812
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.8140 0.000 0.000 1.000 0.000 0.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0520 0.8579 0.984 0.000 0.008 0.008 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.0713 0.7970 0.028 0.000 0.972 0.000 0.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 6 0.5386 0.4989 0.000 0.136 0.000 0.000 0.316 0.548
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.3668 0.4904 0.000 0.000 0.668 0.000 0.004 0.328
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.4702 0.2955 0.496 0.000 0.000 0.000 0.460 0.044
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.8140 0.000 0.000 1.000 0.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 6 0.4671 0.7244 0.000 0.152 0.000 0.000 0.160 0.688
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0146 0.8399 0.000 0.996 0.000 0.000 0.000 0.004
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.1910 0.8248 0.892 0.000 0.000 0.000 0.108 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0146 0.8401 0.000 0.996 0.000 0.000 0.000 0.004
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0146 0.9713 0.004 0.000 0.000 0.996 0.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0146 0.8399 0.000 0.996 0.000 0.000 0.000 0.004
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.3383 0.5779 0.000 0.000 0.728 0.000 0.004 0.268
#> F325847E-F046-4B67-B01C-16919C401020 3 0.4689 0.1446 0.000 0.000 0.516 0.000 0.440 0.044
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.8140 0.000 0.000 1.000 0.000 0.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0260 0.8388 0.000 0.992 0.000 0.000 0.000 0.008
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0146 0.9713 0.004 0.000 0.000 0.996 0.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.3547 0.5358 0.000 0.000 0.696 0.000 0.004 0.300
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.5061 0.2688 0.480 0.000 0.008 0.004 0.464 0.044
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 5 0.5631 -0.0745 0.052 0.000 0.444 0.000 0.460 0.044
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.1814 0.8307 0.900 0.000 0.000 0.000 0.100 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 6 0.2805 0.7851 0.000 0.184 0.000 0.000 0.004 0.812
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.0291 0.8108 0.004 0.000 0.992 0.000 0.000 0.004
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0260 0.8388 0.000 0.992 0.000 0.000 0.000 0.008
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0520 0.8579 0.984 0.000 0.008 0.008 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.6387 0.6325 0.072 0.000 0.004 0.120 0.548 0.256
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0713 0.8560 0.972 0.000 0.000 0.028 0.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0146 0.9713 0.004 0.000 0.000 0.996 0.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0000 0.8140 0.000 0.000 1.000 0.000 0.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 6 0.5038 0.7036 0.000 0.208 0.108 0.000 0.016 0.668
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0458 0.8362 0.000 0.984 0.000 0.000 0.000 0.016
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0000 0.8140 0.000 0.000 1.000 0.000 0.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0146 0.9713 0.004 0.000 0.000 0.996 0.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.3890 0.3431 0.000 0.000 0.596 0.000 0.004 0.400
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0146 0.9713 0.004 0.000 0.000 0.996 0.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.6010 0.4520 0.020 0.000 0.000 0.308 0.512 0.160
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 5 0.6058 0.0175 0.096 0.000 0.396 0.000 0.464 0.044
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 6 0.2805 0.7851 0.000 0.184 0.000 0.000 0.004 0.812
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0000 0.8140 0.000 0.000 1.000 0.000 0.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.5825 0.5208 0.280 0.000 0.000 0.008 0.528 0.184
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 6 0.4500 0.4836 0.016 0.104 0.000 0.000 0.144 0.736
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.0291 0.5945 0.004 0.000 0.000 0.004 0.992 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.2331 0.8329 0.888 0.000 0.000 0.032 0.080 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 6 0.3221 0.6051 0.000 0.000 0.000 0.000 0.264 0.736
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.3717 0.6267 0.004 0.000 0.004 0.060 0.796 0.136
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.4506 0.4004 0.000 0.608 0.000 0.000 0.348 0.044
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0547 0.8574 0.980 0.000 0.000 0.020 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.3380 0.6279 0.004 0.000 0.000 0.748 0.244 0.004
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.3756 0.0620 0.000 0.600 0.000 0.000 0.000 0.400
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.4916 0.6620 0.068 0.000 0.016 0.012 0.696 0.208
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.6530 0.6373 0.116 0.000 0.004 0.092 0.540 0.248
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0520 0.8579 0.984 0.000 0.008 0.008 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 5 0.6106 0.0217 0.096 0.000 0.392 0.000 0.464 0.048
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.8400 0.000 1.000 0.000 0.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.1780 0.8503 0.924 0.000 0.000 0.028 0.048 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.4666 0.6581 0.064 0.000 0.008 0.008 0.700 0.220
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0146 0.8401 0.000 0.996 0.000 0.000 0.000 0.004
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.2610 0.5594 0.004 0.000 0.004 0.060 0.884 0.048
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.2219 0.7098 0.136 0.000 0.864 0.000 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0146 0.9713 0.004 0.000 0.000 0.996 0.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.8400 0.000 1.000 0.000 0.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0146 0.8401 0.000 0.996 0.000 0.000 0.000 0.004
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0547 0.8574 0.980 0.000 0.000 0.020 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.4698 0.3099 0.504 0.000 0.000 0.000 0.452 0.044
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0146 0.8125 0.000 0.000 0.996 0.000 0.000 0.004
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0000 0.8140 0.000 0.000 1.000 0.000 0.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0146 0.9713 0.004 0.000 0.000 0.996 0.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.0508 0.5929 0.004 0.000 0.012 0.000 0.984 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0458 0.8362 0.000 0.984 0.000 0.000 0.000 0.016
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.8140 0.000 0.000 1.000 0.000 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0000 0.8140 0.000 0.000 1.000 0.000 0.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.3175 0.5936 0.256 0.000 0.744 0.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.1524 0.8499 0.932 0.000 0.000 0.008 0.060 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0146 0.9659 0.000 0.004 0.000 0.996 0.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.8140 0.000 0.000 1.000 0.000 0.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0146 0.9713 0.004 0.000 0.000 0.996 0.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0458 0.8362 0.000 0.984 0.000 0.000 0.000 0.016
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.1285 0.8041 0.000 0.944 0.000 0.000 0.004 0.052
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.3852 0.1173 0.000 0.612 0.000 0.000 0.004 0.384
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.1714 0.8357 0.908 0.000 0.000 0.000 0.092 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0458 0.8362 0.000 0.984 0.000 0.000 0.000 0.016
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0146 0.8401 0.000 0.996 0.000 0.000 0.000 0.004
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.5097 0.0603 0.016 0.000 0.476 0.000 0.464 0.044
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.0291 0.5945 0.004 0.000 0.000 0.004 0.992 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.7384 0.5744 0.072 0.000 0.148 0.080 0.520 0.180
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0520 0.8579 0.984 0.000 0.008 0.008 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 5 0.7383 0.6035 0.128 0.000 0.056 0.128 0.524 0.164
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.5642 0.0490 0.444 0.452 0.000 0.000 0.024 0.080
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.6328 0.6404 0.084 0.000 0.056 0.040 0.604 0.216
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.3020 0.7038 0.844 0.000 0.000 0.000 0.076 0.080
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0520 0.8579 0.984 0.000 0.008 0.008 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.3841 0.1202 0.000 0.616 0.000 0.000 0.004 0.380
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.1644 0.8520 0.932 0.000 0.000 0.028 0.040 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0146 0.9713 0.004 0.000 0.000 0.996 0.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 6 0.2805 0.7851 0.000 0.184 0.000 0.000 0.004 0.812
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0146 0.8401 0.000 0.996 0.000 0.000 0.000 0.004
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.4716 0.3551 0.004 0.576 0.000 0.000 0.376 0.044
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.3584 0.5215 0.000 0.000 0.688 0.000 0.004 0.308
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0547 0.8574 0.980 0.000 0.000 0.020 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.5297 0.4936 0.000 0.656 0.000 0.080 0.220 0.044
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0520 0.8579 0.984 0.000 0.008 0.008 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0260 0.8388 0.000 0.992 0.000 0.000 0.000 0.008
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.5117 0.6263 0.076 0.000 0.000 0.016 0.620 0.288
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0146 0.9713 0.004 0.000 0.000 0.996 0.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.8140 0.000 0.000 1.000 0.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0146 0.8399 0.000 0.996 0.000 0.000 0.000 0.004
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.0865 0.5688 0.000 0.000 0.000 0.000 0.964 0.036
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.3652 0.4994 0.000 0.000 0.672 0.000 0.004 0.324
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 6 0.4011 0.7636 0.000 0.212 0.000 0.000 0.056 0.732
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.5070 0.0635 0.012 0.000 0.476 0.000 0.464 0.048
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0000 0.8140 0.000 0.000 1.000 0.000 0.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.8140 0.000 0.000 1.000 0.000 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0547 0.8349 0.000 0.980 0.000 0.000 0.000 0.020
#> A608BCEB-2C27-4927-A308-E6975F641722 6 0.3668 0.6422 0.000 0.328 0.000 0.000 0.004 0.668
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0260 0.8399 0.000 0.992 0.000 0.000 0.000 0.008
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.6530 0.6373 0.116 0.000 0.004 0.092 0.540 0.248
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 6 0.2805 0.7851 0.000 0.184 0.000 0.000 0.004 0.812
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "pam"]
# you can also extract it by
# res = res_list["MAD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.830 0.865 0.939 0.4951 0.497 0.497
#> 3 3 0.504 0.712 0.836 0.2548 0.835 0.683
#> 4 4 0.716 0.768 0.881 0.1362 0.846 0.628
#> 5 5 0.678 0.643 0.814 0.1152 0.819 0.472
#> 6 6 0.778 0.742 0.870 0.0497 0.897 0.579
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.0000 0.9455 1.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.1843 0.9203 0.028 0.972
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.9192 0.000 1.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.2236 0.9170 0.036 0.964
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 2 0.3879 0.9069 0.076 0.924
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 2 0.4022 0.9045 0.080 0.920
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 2 0.3879 0.9069 0.076 0.924
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0000 0.9192 0.000 1.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.0000 0.9455 1.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.9455 1.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.0000 0.9455 1.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.2423 0.9191 0.040 0.960
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.3879 0.9069 0.076 0.924
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.9455 1.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.0000 0.9455 1.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.2778 0.9175 0.048 0.952
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.9192 0.000 1.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0000 0.9455 1.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.9192 0.000 1.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.9686 0.2863 0.604 0.396
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.9192 0.000 1.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.8144 0.7023 0.252 0.748
#> F325847E-F046-4B67-B01C-16919C401020 1 0.9815 0.2233 0.580 0.420
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.0000 0.9455 1.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.9192 0.000 1.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.9286 0.4339 0.656 0.344
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 2 0.3879 0.9069 0.076 0.924
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0000 0.9455 1.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.0000 0.9455 1.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.9455 1.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.2423 0.9191 0.040 0.960
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 2 0.9710 0.4030 0.400 0.600
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.9192 0.000 1.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.9455 1.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 2 0.9922 0.2487 0.448 0.552
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.9455 1.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.1184 0.9337 0.984 0.016
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.0000 0.9455 1.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.3879 0.9069 0.076 0.924
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.9192 0.000 1.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.0000 0.9455 1.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.0000 0.9455 1.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.3584 0.9105 0.068 0.932
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.2603 0.9099 0.956 0.044
#> CB207A52-09AC-49D3-8240-5840CDFBB154 2 0.4022 0.9045 0.080 0.920
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.0000 0.9455 1.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.2423 0.9191 0.040 0.960
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.0000 0.9455 1.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.0376 0.9427 0.996 0.004
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.2423 0.9191 0.040 0.960
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 2 0.4431 0.8954 0.092 0.908
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0000 0.9455 1.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 2 0.2603 0.9182 0.044 0.956
#> AD294665-6F90-459C-90D5-3058F210225D 2 0.9963 0.1963 0.464 0.536
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.2236 0.9170 0.036 0.964
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.9455 1.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0000 0.9455 1.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.9192 0.000 1.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.0000 0.9455 1.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0000 0.9455 1.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.9455 1.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.8763 0.5533 0.704 0.296
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.9192 0.000 1.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0000 0.9455 1.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.8386 0.6014 0.732 0.268
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.9192 0.000 1.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 2 0.4022 0.9045 0.080 0.920
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0000 0.9455 1.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.5408 0.8241 0.876 0.124
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.1843 0.9187 0.028 0.972
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.9192 0.000 1.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.9455 1.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.9455 1.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.0376 0.9427 0.996 0.004
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.0000 0.9455 1.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.0000 0.9455 1.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0000 0.9455 1.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.9192 0.000 1.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.0000 0.9455 1.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.9000 0.5020 0.684 0.316
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.9455 1.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.9455 1.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.0376 0.9190 0.004 0.996
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.0000 0.9455 1.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 2 0.8955 0.5766 0.312 0.688
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.9192 0.000 1.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0000 0.9192 0.000 1.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0000 0.9192 0.000 1.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.9455 1.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.9192 0.000 1.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.9192 0.000 1.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0000 0.9455 1.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.3274 0.8935 0.940 0.060
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.4298 0.8670 0.912 0.088
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.9455 1.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.0000 0.9455 1.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.2603 0.9086 0.956 0.044
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.0000 0.9455 1.000 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.2043 0.9184 0.968 0.032
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.9455 1.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.9192 0.000 1.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.9455 1.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.0000 0.9455 1.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.1184 0.9205 0.016 0.984
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.9192 0.000 1.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.9977 0.0915 0.472 0.528
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.5178 0.8746 0.116 0.884
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.9455 1.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.3879 0.9069 0.076 0.924
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.9455 1.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.2236 0.9170 0.036 0.964
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 2 0.2423 0.9191 0.040 0.960
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.0000 0.9455 1.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.0000 0.9455 1.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0376 0.9196 0.004 0.996
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 2 0.4022 0.9045 0.080 0.920
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.3879 0.9069 0.076 0.924
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.2423 0.9191 0.040 0.960
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.9286 0.4378 0.656 0.344
#> B12A4446-2310-4139-897F-CA030478CBD5 2 0.9954 0.2267 0.460 0.540
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.0000 0.9455 1.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.4298 0.8859 0.088 0.912
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.9192 0.000 1.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.9192 0.000 1.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.8499 0.5961 0.724 0.276
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.2423 0.9191 0.040 0.960
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.0000 0.85264 1.000 0.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 3 0.6292 0.78764 0.044 0.216 0.740
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.82271 0.000 1.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.2959 0.75864 0.100 0.900 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.7975 0.75540 0.160 0.180 0.660
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.8091 0.65954 0.320 0.088 0.592
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.7978 0.75309 0.164 0.176 0.660
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 3 0.5216 0.73861 0.000 0.260 0.740
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.4121 0.70627 0.832 0.000 0.168
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.85264 1.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.0000 0.85264 1.000 0.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.7053 0.40314 0.064 0.692 0.244
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.6754 0.78636 0.168 0.092 0.740
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.85264 1.000 0.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.0000 0.85264 1.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.8271 -0.29781 0.076 0.480 0.444
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.82271 0.000 1.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0000 0.85264 1.000 0.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.82271 0.000 1.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.6299 0.45005 0.524 0.000 0.476
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.82271 0.000 1.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.7021 0.76231 0.216 0.076 0.708
#> F325847E-F046-4B67-B01C-16919C401020 1 0.7582 0.17375 0.572 0.048 0.380
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.0000 0.85264 1.000 0.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.82271 0.000 1.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.6267 0.49098 0.548 0.000 0.452
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.6731 0.78479 0.172 0.088 0.740
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0000 0.85264 1.000 0.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.0000 0.85264 1.000 0.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.85264 1.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 3 0.6537 0.80156 0.064 0.196 0.740
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.6007 0.76662 0.192 0.044 0.764
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.82271 0.000 1.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.85264 1.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.8043 0.12601 0.556 0.072 0.372
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.4062 0.77240 0.836 0.000 0.164
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.9813 0.23409 0.424 0.316 0.260
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.0000 0.85264 1.000 0.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.8889 0.62205 0.164 0.276 0.560
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.82271 0.000 1.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.0000 0.85264 1.000 0.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.5327 0.71135 0.728 0.000 0.272
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.6854 0.79782 0.136 0.124 0.740
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.5497 0.69577 0.708 0.000 0.292
#> CB207A52-09AC-49D3-8240-5840CDFBB154 3 0.1529 0.68512 0.000 0.040 0.960
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.4346 0.70373 0.816 0.184 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 3 0.6537 0.80156 0.064 0.196 0.740
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.0747 0.84896 0.984 0.000 0.016
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.0000 0.85264 1.000 0.000 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.6537 0.80156 0.064 0.196 0.740
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 2 0.9702 0.00937 0.364 0.416 0.220
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.7878 0.52226 0.668 0.160 0.172
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 3 0.6576 0.80189 0.068 0.192 0.740
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.7570 0.17252 0.552 0.044 0.404
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.2959 0.75864 0.100 0.900 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.3686 0.78831 0.860 0.000 0.140
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.5216 0.71634 0.740 0.000 0.260
#> A54731AE-FC40-407F-8D10-67DDC122237D 3 0.5216 0.73861 0.000 0.260 0.740
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.0000 0.85264 1.000 0.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.3816 0.79736 0.852 0.000 0.148
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.85264 1.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 2 0.6941 0.16866 0.464 0.520 0.016
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.82271 0.000 1.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0592 0.84816 0.988 0.000 0.012
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.2165 0.81570 0.936 0.064 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.82271 0.000 1.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 3 0.6754 0.78562 0.168 0.092 0.740
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0000 0.85264 1.000 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.5948 0.62866 0.640 0.000 0.360
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.2356 0.78200 0.072 0.928 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.82271 0.000 1.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.4062 0.77240 0.836 0.000 0.164
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.85264 1.000 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.1163 0.83996 0.972 0.000 0.028
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.1031 0.84704 0.976 0.000 0.024
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.5216 0.71634 0.740 0.000 0.260
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.1860 0.82309 0.948 0.000 0.052
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.82271 0.000 1.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.0000 0.85264 1.000 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.7295 0.20514 0.584 0.036 0.380
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.85264 1.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.85264 1.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 3 0.0000 0.65240 0.000 0.000 1.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.1031 0.84214 0.976 0.000 0.024
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 3 0.4555 0.48250 0.200 0.000 0.800
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.82271 0.000 1.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.1411 0.79951 0.000 0.964 0.036
#> 53A96249-66D5-4C26-893B-ADC71481D261 3 0.5873 0.68772 0.004 0.312 0.684
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.85264 1.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.82271 0.000 1.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.2448 0.75088 0.000 0.924 0.076
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0000 0.85264 1.000 0.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.8504 0.40478 0.612 0.216 0.172
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.4796 0.75423 0.780 0.000 0.220
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.85264 1.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.4235 0.76762 0.824 0.000 0.176
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.5650 0.48410 0.688 0.312 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.2261 0.83584 0.932 0.000 0.068
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.2066 0.82495 0.940 0.060 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.85264 1.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0237 0.82074 0.000 0.996 0.004
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.85264 1.000 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.5216 0.71634 0.740 0.000 0.260
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 3 0.5858 0.76374 0.020 0.240 0.740
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.82271 0.000 1.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.3412 0.73990 0.124 0.876 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.8044 0.66558 0.312 0.088 0.600
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.3686 0.78826 0.860 0.000 0.140
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.5413 0.67685 0.164 0.800 0.036
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.85264 1.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.2878 0.76249 0.096 0.904 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.6537 0.80156 0.064 0.196 0.740
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.5216 0.71634 0.740 0.000 0.260
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.0000 0.85264 1.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0747 0.81659 0.016 0.984 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 2 0.8703 0.30406 0.168 0.588 0.244
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.6775 0.78696 0.164 0.096 0.740
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 3 0.6449 0.79688 0.056 0.204 0.740
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 2 0.6286 0.18744 0.464 0.536 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.7526 0.46656 0.424 0.040 0.536
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.0592 0.84800 0.988 0.000 0.012
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 3 0.8684 0.40465 0.108 0.392 0.500
#> A608BCEB-2C27-4927-A308-E6975F641722 3 0.5465 0.71417 0.000 0.288 0.712
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.4121 0.60840 0.000 0.832 0.168
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.7170 0.27823 0.612 0.036 0.352
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.6537 0.80156 0.064 0.196 0.740
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.0469 0.8884 0.988 0.000 0.012 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 3 0.1389 0.7580 0.000 0.048 0.952 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.9170 0.000 1.000 0.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0000 0.9170 0.000 1.000 0.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.4568 0.7419 0.076 0.124 0.800 0.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.4866 0.3063 0.404 0.000 0.596 0.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.5598 0.6380 0.076 0.220 0.704 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 3 0.1389 0.7580 0.000 0.048 0.952 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.4989 0.0932 0.528 0.000 0.472 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.8890 1.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.1389 0.8818 0.952 0.000 0.048 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.5630 0.3583 0.032 0.608 0.360 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.1940 0.7425 0.076 0.000 0.924 0.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.8890 1.000 0.000 0.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.1389 0.8818 0.952 0.000 0.048 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.5915 0.2644 0.040 0.400 0.560 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.9170 0.000 1.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0000 0.8890 1.000 0.000 0.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.9170 0.000 1.000 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 1.0000 0.000 0.000 0.000 1.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.9170 0.000 1.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.2469 0.7302 0.108 0.000 0.892 0.000
#> F325847E-F046-4B67-B01C-16919C401020 3 0.4877 0.2871 0.408 0.000 0.592 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.1389 0.8818 0.952 0.000 0.048 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.9170 0.000 1.000 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 1.0000 0.000 0.000 0.000 1.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.1302 0.7487 0.044 0.000 0.956 0.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0000 0.8890 1.000 0.000 0.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.0817 0.8872 0.976 0.000 0.024 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.8890 1.000 0.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 3 0.1389 0.7580 0.000 0.048 0.952 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.2216 0.7379 0.092 0.000 0.908 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.9170 0.000 1.000 0.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.8890 1.000 0.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 3 0.5630 0.4247 0.360 0.032 0.608 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.1940 0.8473 0.924 0.000 0.000 0.076
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 1.0000 0.000 0.000 0.000 1.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.1389 0.8818 0.952 0.000 0.048 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.6542 0.1606 0.076 0.428 0.496 0.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.9170 0.000 1.000 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.1389 0.8818 0.952 0.000 0.048 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 1.0000 0.000 0.000 0.000 1.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.2739 0.7590 0.060 0.036 0.904 0.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 1.0000 0.000 0.000 0.000 1.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 3 0.4955 0.1894 0.000 0.000 0.556 0.444
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.4919 0.6923 0.752 0.200 0.048 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 3 0.1389 0.7580 0.000 0.048 0.952 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.4405 0.7888 0.800 0.000 0.048 0.152
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.0336 0.8887 0.992 0.000 0.008 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.1576 0.7586 0.004 0.048 0.948 0.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 3 0.7430 0.5068 0.260 0.228 0.512 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.6801 0.1860 0.568 0.020 0.348 0.064
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 3 0.1389 0.7580 0.000 0.048 0.952 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.6926 0.2815 0.392 0.000 0.496 0.112
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.0000 0.9170 0.000 1.000 0.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.1716 0.8564 0.936 0.000 0.000 0.064
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0000 1.0000 0.000 0.000 0.000 1.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 3 0.1389 0.7580 0.000 0.048 0.952 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.1211 0.8838 0.960 0.000 0.040 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.3616 0.8288 0.852 0.000 0.036 0.112
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.8890 1.000 0.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 2 0.6158 0.2579 0.384 0.560 0.056 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.9170 0.000 1.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.2589 0.8046 0.884 0.000 0.116 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.3679 0.8313 0.856 0.060 0.084 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.9170 0.000 1.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 3 0.3301 0.7574 0.076 0.048 0.876 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0592 0.8882 0.984 0.000 0.016 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 1.0000 0.000 0.000 0.000 1.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.9170 0.000 1.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.9170 0.000 1.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.1940 0.8473 0.924 0.000 0.000 0.076
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.8890 1.000 0.000 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.3444 0.7732 0.816 0.000 0.184 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.2926 0.8645 0.896 0.000 0.048 0.056
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 1.0000 0.000 0.000 0.000 1.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.3649 0.7270 0.796 0.000 0.204 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.9170 0.000 1.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.1389 0.8818 0.952 0.000 0.048 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.4877 0.2871 0.408 0.000 0.592 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.8890 1.000 0.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.8890 1.000 0.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0000 1.0000 0.000 0.000 0.000 1.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.2647 0.8399 0.880 0.000 0.120 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0000 1.0000 0.000 0.000 0.000 1.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.9170 0.000 1.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.1022 0.8929 0.000 0.968 0.032 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 3 0.4222 0.6008 0.000 0.272 0.728 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.8890 1.000 0.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.1867 0.8653 0.000 0.928 0.072 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0188 0.9142 0.000 0.996 0.004 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.1389 0.8818 0.952 0.000 0.048 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.6564 0.0621 0.536 0.084 0.380 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.4440 0.8003 0.804 0.000 0.060 0.136
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.8890 1.000 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.3271 0.8195 0.856 0.000 0.012 0.132
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.4830 0.3402 0.608 0.392 0.000 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.4274 0.7944 0.808 0.000 0.148 0.044
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.1398 0.8676 0.956 0.040 0.004 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.8890 1.000 0.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.1940 0.8617 0.000 0.924 0.076 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.8890 1.000 0.000 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 1.0000 0.000 0.000 0.000 1.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 3 0.1389 0.7580 0.000 0.048 0.952 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.9170 0.000 1.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.0000 0.9170 0.000 1.000 0.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.4804 0.3448 0.384 0.000 0.616 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.1792 0.8534 0.932 0.000 0.000 0.068
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.1940 0.8344 0.076 0.924 0.000 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.8890 1.000 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.9170 0.000 1.000 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.1389 0.7580 0.000 0.048 0.952 0.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 1.0000 0.000 0.000 0.000 1.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.1389 0.8818 0.952 0.000 0.048 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.9170 0.000 1.000 0.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 2 0.6004 0.4410 0.076 0.648 0.276 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.1302 0.7487 0.044 0.000 0.956 0.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 3 0.1389 0.7580 0.000 0.048 0.952 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 2 0.6738 0.2874 0.352 0.544 0.104 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.4746 0.3814 0.632 0.000 0.368 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.2589 0.8446 0.884 0.000 0.116 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 3 0.4925 0.3240 0.000 0.428 0.572 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 3 0.2760 0.7198 0.000 0.128 0.872 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0336 0.9110 0.000 0.992 0.008 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.5423 0.5007 0.332 0.028 0.640 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.1389 0.7580 0.000 0.048 0.952 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.1908 0.76618 0.908 0.000 0.092 0.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 5 0.0000 0.74360 0.000 0.000 0.000 0.000 1.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.1197 0.84269 0.000 0.952 0.000 0.000 0.048
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.1845 0.82411 0.000 0.928 0.056 0.000 0.016
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.5659 0.54597 0.000 0.100 0.320 0.000 0.580
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.3682 0.64791 0.072 0.000 0.820 0.000 0.108
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 5 0.5456 0.50945 0.000 0.080 0.328 0.000 0.592
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 5 0.0000 0.74360 0.000 0.000 0.000 0.000 1.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.3992 0.66466 0.080 0.000 0.796 0.000 0.124
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.82128 1.000 0.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.4273 0.29091 0.448 0.000 0.552 0.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 5 0.6289 0.42337 0.000 0.160 0.356 0.000 0.484
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.5426 0.44099 0.000 0.084 0.608 0.000 0.308
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.1341 0.79199 0.944 0.000 0.056 0.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.4297 0.23066 0.472 0.000 0.528 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 5 0.5696 0.49371 0.000 0.096 0.344 0.000 0.560
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.86178 0.000 1.000 0.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.1121 0.79918 0.956 0.000 0.044 0.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.86178 0.000 1.000 0.000 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 0.99750 0.000 0.000 0.000 1.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.86178 0.000 1.000 0.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.5309 0.42760 0.004 0.056 0.604 0.000 0.336
#> F325847E-F046-4B67-B01C-16919C401020 3 0.1908 0.64345 0.000 0.000 0.908 0.000 0.092
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.4192 0.21446 0.596 0.000 0.404 0.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.86178 0.000 1.000 0.000 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 0.99750 0.000 0.000 0.000 1.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.3177 0.61370 0.000 0.000 0.792 0.000 0.208
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.4302 -0.00854 0.520 0.000 0.480 0.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.2561 0.73148 0.856 0.000 0.144 0.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.82128 1.000 0.000 0.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 5 0.0000 0.74360 0.000 0.000 0.000 0.000 1.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.2424 0.64121 0.000 0.000 0.868 0.000 0.132
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.86178 0.000 1.000 0.000 0.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.82128 1.000 0.000 0.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.4404 0.55858 0.080 0.000 0.136 0.008 0.776
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.82128 1.000 0.000 0.000 0.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 0.99750 0.000 0.000 0.000 1.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.4138 0.41172 0.384 0.000 0.616 0.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 5 0.4937 0.05406 0.000 0.028 0.428 0.000 0.544
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0794 0.85187 0.000 0.972 0.028 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.3242 0.64474 0.216 0.000 0.784 0.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 0.99750 0.000 0.000 0.000 1.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.4555 0.49101 0.000 0.056 0.720 0.000 0.224
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 0.99750 0.000 0.000 0.000 1.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 3 0.6721 0.05261 0.000 0.000 0.420 0.304 0.276
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.5663 0.28755 0.532 0.084 0.384 0.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 5 0.0000 0.74360 0.000 0.000 0.000 0.000 1.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.5699 0.47822 0.264 0.000 0.608 0.128 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.4238 0.36868 0.628 0.000 0.368 0.000 0.004
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.5063 0.12300 0.000 0.056 0.632 0.000 0.312
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.4313 0.56310 0.000 0.008 0.356 0.000 0.636
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.5875 0.59915 0.708 0.016 0.048 0.088 0.140
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 5 0.0000 0.74360 0.000 0.000 0.000 0.000 1.000
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.7407 0.35091 0.076 0.000 0.272 0.160 0.492
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.6406 0.17212 0.000 0.484 0.328 0.000 0.188
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.82128 1.000 0.000 0.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0703 0.97209 0.000 0.000 0.024 0.976 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 5 0.3534 0.51588 0.000 0.256 0.000 0.000 0.744
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.4030 0.47456 0.648 0.000 0.352 0.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.7555 0.31970 0.504 0.000 0.160 0.108 0.228
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.82128 1.000 0.000 0.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.6343 0.04495 0.012 0.160 0.564 0.000 0.264
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.86178 0.000 1.000 0.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.1043 0.80276 0.960 0.000 0.000 0.000 0.040
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.4139 0.57335 0.164 0.004 0.780 0.000 0.052
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.86178 0.000 1.000 0.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.2179 0.71739 0.000 0.000 0.112 0.000 0.888
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.4171 0.11511 0.604 0.000 0.396 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 0.99750 0.000 0.000 0.000 1.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0290 0.85976 0.000 0.992 0.000 0.000 0.008
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.86178 0.000 1.000 0.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.82128 1.000 0.000 0.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.1043 0.80168 0.960 0.000 0.040 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.5376 0.48912 0.308 0.000 0.612 0.000 0.080
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.4161 0.38646 0.392 0.000 0.608 0.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 0.99750 0.000 0.000 0.000 1.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 3 0.4861 -0.03190 0.428 0.000 0.548 0.000 0.024
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0290 0.85976 0.000 0.992 0.000 0.000 0.008
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.3003 0.66213 0.188 0.000 0.812 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.3003 0.62177 0.000 0.000 0.812 0.000 0.188
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.82128 1.000 0.000 0.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.82128 1.000 0.000 0.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0000 0.99750 0.000 0.000 0.000 1.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.2992 0.66971 0.064 0.000 0.868 0.000 0.068
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0000 0.99750 0.000 0.000 0.000 1.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.86178 0.000 1.000 0.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.4047 0.47418 0.000 0.676 0.320 0.000 0.004
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.3710 0.68581 0.000 0.784 0.024 0.000 0.192
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.82128 1.000 0.000 0.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.4287 0.09522 0.000 0.540 0.000 0.000 0.460
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.86178 0.000 1.000 0.000 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.2516 0.66676 0.140 0.000 0.860 0.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 3 0.6657 -0.04685 0.352 0.000 0.416 0.000 0.232
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.4269 0.64395 0.116 0.000 0.796 0.016 0.072
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.82128 1.000 0.000 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.4974 0.55881 0.212 0.000 0.696 0.092 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.3995 0.67001 0.180 0.776 0.044 0.000 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.6219 0.06392 0.440 0.000 0.420 0.000 0.140
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.2179 0.74826 0.888 0.000 0.112 0.000 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.82128 1.000 0.000 0.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.4829 -0.00401 0.000 0.500 0.020 0.000 0.480
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.82128 1.000 0.000 0.000 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 0.99750 0.000 0.000 0.000 1.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 5 0.0000 0.74360 0.000 0.000 0.000 0.000 1.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.86178 0.000 1.000 0.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.1740 0.82642 0.000 0.932 0.056 0.000 0.012
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.3684 0.63662 0.004 0.056 0.824 0.000 0.116
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.82128 1.000 0.000 0.000 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.5559 0.42760 0.000 0.608 0.320 0.016 0.056
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.82128 1.000 0.000 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.1121 0.83753 0.000 0.956 0.044 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.1792 0.71777 0.000 0.000 0.084 0.000 0.916
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 0.99750 0.000 0.000 0.000 1.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.3366 0.57582 0.768 0.000 0.232 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0963 0.84755 0.000 0.964 0.036 0.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.5728 0.43877 0.000 0.084 0.432 0.000 0.484
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.3058 0.64373 0.000 0.044 0.860 0.000 0.096
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 5 0.0000 0.74360 0.000 0.000 0.000 0.000 1.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.1851 0.61985 0.000 0.088 0.912 0.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.2946 0.67309 0.088 0.044 0.868 0.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.3209 0.66805 0.180 0.000 0.812 0.000 0.008
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.4676 0.61165 0.000 0.720 0.072 0.000 0.208
#> A608BCEB-2C27-4927-A308-E6975F641722 5 0.1544 0.71945 0.000 0.068 0.000 0.000 0.932
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0609 0.85545 0.000 0.980 0.020 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.6255 0.26022 0.320 0.000 0.148 0.004 0.528
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 5 0.0000 0.74360 0.000 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.5187 0.4464 0.600 0.000 0.264 0.000 0.136 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 6 0.0000 0.8245 0.000 0.000 0.000 0.000 0.000 1.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.1863 0.8513 0.000 0.896 0.000 0.000 0.000 0.104
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.2594 0.8411 0.000 0.880 0.056 0.000 0.004 0.060
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.2402 0.7598 0.000 0.000 0.004 0.000 0.856 0.140
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.3000 0.7397 0.016 0.000 0.860 0.000 0.048 0.076
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 6 0.3670 0.6297 0.000 0.024 0.240 0.000 0.000 0.736
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 6 0.0000 0.8245 0.000 0.000 0.000 0.000 0.000 1.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.4085 0.6990 0.096 0.000 0.748 0.000 0.000 0.156
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.8779 1.000 0.000 0.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.3371 0.6217 0.292 0.000 0.708 0.000 0.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 6 0.4719 0.5914 0.000 0.084 0.272 0.000 0.000 0.644
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.2239 0.7501 0.000 0.048 0.908 0.000 0.020 0.024
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.1082 0.8538 0.956 0.000 0.040 0.000 0.004 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.3684 0.4984 0.372 0.000 0.628 0.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 6 0.4707 0.6167 0.000 0.080 0.252 0.000 0.004 0.664
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.9086 0.000 1.000 0.000 0.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.1082 0.8538 0.956 0.000 0.040 0.000 0.004 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.9086 0.000 1.000 0.000 0.000 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 0.9988 0.000 0.000 0.000 1.000 0.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.9086 0.000 1.000 0.000 0.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.2321 0.7534 0.008 0.032 0.904 0.000 0.004 0.052
#> F325847E-F046-4B67-B01C-16919C401020 3 0.3586 0.6622 0.000 0.000 0.796 0.000 0.124 0.080
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.3843 0.0153 0.548 0.000 0.452 0.000 0.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.9086 0.000 1.000 0.000 0.000 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 0.9988 0.000 0.000 0.000 1.000 0.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.3109 0.7162 0.000 0.004 0.812 0.000 0.016 0.168
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 3 0.2730 0.6644 0.192 0.000 0.808 0.000 0.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.3711 0.5878 0.720 0.000 0.260 0.000 0.020 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.8779 1.000 0.000 0.000 0.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 6 0.0000 0.8245 0.000 0.000 0.000 0.000 0.000 1.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.1204 0.7521 0.000 0.000 0.944 0.000 0.000 0.056
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.9086 0.000 1.000 0.000 0.000 0.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.8779 1.000 0.000 0.000 0.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.5609 0.2822 0.004 0.000 0.136 0.000 0.508 0.352
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.8779 1.000 0.000 0.000 0.000 0.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 0.9988 0.000 0.000 0.000 1.000 0.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.3390 0.6239 0.296 0.000 0.704 0.000 0.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.4263 0.2957 0.000 0.024 0.600 0.000 0.000 0.376
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.1471 0.8760 0.000 0.932 0.064 0.000 0.000 0.004
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.2941 0.6903 0.220 0.000 0.780 0.000 0.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 0.9988 0.000 0.000 0.000 1.000 0.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.3734 0.5421 0.000 0.020 0.716 0.000 0.000 0.264
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 0.9988 0.000 0.000 0.000 1.000 0.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.1967 0.7813 0.000 0.000 0.012 0.084 0.904 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.5155 0.1985 0.488 0.072 0.436 0.000 0.004 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 6 0.0000 0.8245 0.000 0.000 0.000 0.000 0.000 1.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.2511 0.7570 0.056 0.000 0.880 0.064 0.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.0858 0.8052 0.004 0.000 0.028 0.000 0.968 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.2402 0.7598 0.000 0.000 0.004 0.000 0.856 0.140
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.0260 0.8034 0.000 0.000 0.008 0.000 0.992 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.4531 0.4179 0.608 0.000 0.036 0.000 0.004 0.352
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 6 0.0000 0.8245 0.000 0.000 0.000 0.000 0.000 1.000
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.7112 0.0515 0.044 0.000 0.332 0.012 0.352 0.260
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.5869 0.1705 0.000 0.504 0.208 0.000 0.004 0.284
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.8779 1.000 0.000 0.000 0.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0405 0.9871 0.000 0.000 0.008 0.988 0.004 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 6 0.2135 0.7561 0.000 0.128 0.000 0.000 0.000 0.872
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.2489 0.7756 0.012 0.000 0.128 0.000 0.860 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.1418 0.7990 0.032 0.000 0.024 0.000 0.944 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.8779 1.000 0.000 0.000 0.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.6613 -0.0647 0.060 0.084 0.484 0.000 0.024 0.348
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.9086 0.000 1.000 0.000 0.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.2527 0.7468 0.832 0.000 0.000 0.000 0.000 0.168
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.2300 0.7664 0.000 0.000 0.144 0.000 0.856 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.9086 0.000 1.000 0.000 0.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 6 0.3744 0.6217 0.000 0.000 0.044 0.000 0.200 0.756
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.3843 0.3492 0.452 0.000 0.548 0.000 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 0.9988 0.000 0.000 0.000 1.000 0.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0909 0.8989 0.000 0.968 0.020 0.000 0.000 0.012
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.9086 0.000 1.000 0.000 0.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.8779 1.000 0.000 0.000 0.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.1082 0.8538 0.956 0.000 0.040 0.000 0.004 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.2506 0.7545 0.068 0.000 0.880 0.000 0.052 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.2066 0.7598 0.072 0.000 0.904 0.000 0.024 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 0.9988 0.000 0.000 0.000 1.000 0.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.2491 0.7642 0.000 0.000 0.164 0.000 0.836 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0363 0.9045 0.000 0.988 0.000 0.000 0.000 0.012
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.3175 0.6566 0.256 0.000 0.744 0.000 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.3126 0.6508 0.000 0.000 0.752 0.000 0.000 0.248
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.8779 1.000 0.000 0.000 0.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.8779 1.000 0.000 0.000 0.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0000 0.9988 0.000 0.000 0.000 1.000 0.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0937 0.7568 0.040 0.000 0.960 0.000 0.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0000 0.9988 0.000 0.000 0.000 1.000 0.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.9086 0.000 1.000 0.000 0.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 3 0.4141 0.1541 0.000 0.432 0.556 0.000 0.000 0.012
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.3592 0.6535 0.000 0.740 0.020 0.000 0.000 0.240
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.8779 1.000 0.000 0.000 0.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 6 0.3857 0.2119 0.000 0.468 0.000 0.000 0.000 0.532
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.9086 0.000 1.000 0.000 0.000 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.2320 0.7444 0.132 0.000 0.864 0.000 0.004 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.0000 0.8025 0.000 0.000 0.000 0.000 1.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.2149 0.8030 0.004 0.000 0.080 0.016 0.900 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.8779 1.000 0.000 0.000 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 5 0.5453 -0.0242 0.104 0.000 0.428 0.004 0.464 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.2930 0.7830 0.124 0.840 0.000 0.000 0.036 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.0777 0.8092 0.004 0.000 0.024 0.000 0.972 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 5 0.2912 0.6888 0.216 0.000 0.000 0.000 0.784 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.8779 1.000 0.000 0.000 0.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 6 0.4780 0.3393 0.000 0.392 0.056 0.000 0.000 0.552
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.8779 1.000 0.000 0.000 0.000 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 0.9988 0.000 0.000 0.000 1.000 0.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 6 0.0260 0.8185 0.000 0.000 0.000 0.000 0.008 0.992
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.9086 0.000 1.000 0.000 0.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.1801 0.8763 0.000 0.924 0.056 0.000 0.004 0.016
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.2573 0.7196 0.000 0.024 0.864 0.000 0.000 0.112
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.8779 1.000 0.000 0.000 0.000 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.5705 0.4993 0.000 0.624 0.204 0.032 0.004 0.136
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.8779 1.000 0.000 0.000 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.9086 0.000 1.000 0.000 0.000 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.2178 0.7667 0.000 0.000 0.000 0.000 0.868 0.132
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 0.9988 0.000 0.000 0.000 1.000 0.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.3023 0.6032 0.768 0.000 0.232 0.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.1327 0.8768 0.000 0.936 0.064 0.000 0.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.2260 0.7596 0.000 0.000 0.000 0.000 0.860 0.140
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.2260 0.7289 0.000 0.000 0.860 0.000 0.000 0.140
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 6 0.0000 0.8245 0.000 0.000 0.000 0.000 0.000 1.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.2867 0.6953 0.000 0.076 0.868 0.000 0.016 0.040
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.1262 0.7551 0.008 0.016 0.956 0.000 0.000 0.020
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.3175 0.6554 0.256 0.000 0.744 0.000 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.3885 0.6916 0.000 0.756 0.048 0.000 0.004 0.192
#> A608BCEB-2C27-4927-A308-E6975F641722 6 0.0000 0.8245 0.000 0.000 0.000 0.000 0.000 1.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0865 0.8922 0.000 0.964 0.036 0.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.1749 0.7958 0.036 0.000 0.024 0.000 0.932 0.008
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 6 0.0000 0.8245 0.000 0.000 0.000 0.000 0.000 1.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "mclust"]
# you can also extract it by
# res = res_list["MAD:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.189 0.145 0.712 0.3944 0.853 0.853
#> 3 3 0.280 0.463 0.733 0.3975 0.476 0.410
#> 4 4 0.550 0.696 0.832 0.2947 0.741 0.433
#> 5 5 0.586 0.486 0.717 0.0913 0.841 0.483
#> 6 6 0.594 0.492 0.708 0.0386 0.897 0.582
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.9248 -0.3875 0.660 0.340
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 1 0.7376 0.4182 0.792 0.208
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 1 0.9993 0.3835 0.516 0.484
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.9988 0.3847 0.520 0.480
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.0938 0.4228 0.988 0.012
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.0938 0.4177 0.988 0.012
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.1633 0.4248 0.976 0.024
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 1 0.9754 0.3957 0.592 0.408
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.6048 0.2277 0.852 0.148
#> 5482053D-9F48-4773-B68A-302B3A612503 1 1.0000 -0.8947 0.504 0.496
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.6343 0.1825 0.840 0.160
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.6623 0.4237 0.828 0.172
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 1 0.0938 0.4228 0.988 0.012
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.9358 -0.4370 0.648 0.352
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.6048 0.2277 0.852 0.148
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 1 0.7674 0.4181 0.776 0.224
#> DC55EE78-203F-4092-9B83-14B1A529194B 1 0.9993 0.3835 0.516 0.484
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.9358 -0.4370 0.648 0.352
#> F772EA39-E408-4908-BADD-C786D702BF9B 1 0.9993 0.3835 0.516 0.484
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 2 0.9993 0.9886 0.484 0.516
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 1 0.9993 0.3835 0.516 0.484
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 1 0.0938 0.4228 0.988 0.012
#> F325847E-F046-4B67-B01C-16919C401020 1 0.5059 0.2606 0.888 0.112
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.0376 0.4156 0.996 0.004
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 1 0.9993 0.3835 0.516 0.484
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 2 0.9993 0.9886 0.484 0.516
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 1 0.0938 0.4228 0.988 0.012
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.9044 -0.3613 0.680 0.320
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.5059 0.2930 0.888 0.112
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.9358 -0.4370 0.648 0.352
#> A8E48877-F8AB-44DD-A18B-194D87C44931 1 0.0938 0.4228 0.988 0.012
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.4562 0.3127 0.904 0.096
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 1 0.9954 0.3889 0.540 0.460
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 1.0000 -0.9032 0.500 0.500
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.8661 -0.3192 0.712 0.288
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 1.0000 -0.9032 0.500 0.500
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 2 0.9993 0.9886 0.484 0.516
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.6048 0.2277 0.852 0.148
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 1 0.1633 0.4248 0.976 0.024
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 1 0.9866 0.3939 0.568 0.432
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.6048 0.2277 0.852 0.148
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.9522 -0.5406 0.628 0.372
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 1 0.1184 0.4239 0.984 0.016
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 2 0.9993 0.9886 0.484 0.516
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.9209 -0.4527 0.664 0.336
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.7139 0.0759 0.804 0.196
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 1 0.0938 0.4228 0.988 0.012
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.6048 0.2277 0.852 0.148
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.8207 -0.0499 0.744 0.256
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 1 0.1184 0.4239 0.984 0.016
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.8207 -0.2113 0.744 0.256
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.9358 -0.4370 0.648 0.352
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.0376 0.4110 0.996 0.004
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.8909 -0.3768 0.692 0.308
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.1843 0.4248 0.972 0.028
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 2 1.0000 0.8915 0.500 0.500
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.9491 -0.5267 0.632 0.368
#> A54731AE-FC40-407F-8D10-67DDC122237D 1 0.9988 0.3842 0.520 0.480
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.7299 -0.0136 0.796 0.204
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.9909 -0.8116 0.556 0.444
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.9993 -0.8699 0.516 0.484
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.4939 0.2985 0.892 0.108
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 1 0.9993 0.3835 0.516 0.484
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.9358 -0.4370 0.648 0.352
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.0376 0.4110 0.996 0.004
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 1 0.9993 0.3835 0.516 0.484
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.8909 -0.3765 0.692 0.308
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.3879 0.3650 0.924 0.076
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 2 0.9993 0.9886 0.484 0.516
#> EE16D845-31F2-4178-800B-CA2C358841AD 1 0.9993 0.3835 0.516 0.484
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 1 0.9993 0.3835 0.516 0.484
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.9710 -0.6080 0.600 0.400
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.9323 -0.4276 0.652 0.348
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.6048 0.2277 0.852 0.148
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.3274 0.3500 0.940 0.060
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 2 0.9993 0.9886 0.484 0.516
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.6048 0.1736 0.852 0.148
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 1 0.9993 0.3835 0.516 0.484
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.6048 0.2277 0.852 0.148
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.6048 0.2277 0.852 0.148
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.8813 -0.2396 0.700 0.300
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.9358 -0.4370 0.648 0.352
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.9129 -0.4209 0.672 0.328
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.6048 0.2277 0.852 0.148
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 2 0.9993 0.9886 0.484 0.516
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 1 0.9993 0.3835 0.516 0.484
#> 322AF320-1379-4F51-AFDC-5292A060CD52 1 0.9866 0.3938 0.568 0.432
#> 53A96249-66D5-4C26-893B-ADC71481D261 1 0.9323 0.4038 0.652 0.348
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.9358 -0.4370 0.648 0.352
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 1 0.9988 0.3842 0.520 0.480
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 1 0.9993 0.3835 0.516 0.484
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.4022 0.3205 0.920 0.080
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.9286 -0.4417 0.656 0.344
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.9710 -0.6731 0.600 0.400
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.9909 -0.7599 0.556 0.444
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.9933 -0.8328 0.548 0.452
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.1843 0.4248 0.972 0.028
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.7950 -0.1545 0.760 0.240
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.7883 0.0450 0.764 0.236
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 1.0000 -0.9032 0.500 0.500
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 1 0.9988 0.3842 0.520 0.480
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.9358 -0.4370 0.648 0.352
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 2 0.9993 0.9886 0.484 0.516
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 1 0.5408 0.4266 0.876 0.124
#> 2D962371-EC83-490C-A663-478AF383BC1B 1 0.9993 0.3835 0.516 0.484
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.1843 0.4248 0.972 0.028
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 1 0.1414 0.4245 0.980 0.020
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 1.0000 -0.9032 0.500 0.500
#> A60DC925-7343-496E-900D-0DD81D5C8123 1 0.1843 0.4248 0.972 0.028
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 1.0000 -0.8947 0.504 0.496
#> 6969B6B2-7616-4664-9696-C4DACD10537B 1 0.9552 0.4004 0.624 0.376
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.0938 0.4228 0.988 0.012
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 2 0.9993 0.9886 0.484 0.516
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.6048 0.2277 0.852 0.148
#> D47D0433-2313-4A2F-B268-5AD293D7534E 1 0.9988 0.3842 0.520 0.480
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.2778 0.3658 0.952 0.048
#> 2629FEE3-A203-4411-8A70-02A796C9505C 1 0.0938 0.4228 0.988 0.012
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 1 0.9881 0.3924 0.564 0.436
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.5178 0.2835 0.884 0.116
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.6048 0.2277 0.852 0.148
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.6048 0.2277 0.852 0.148
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 1 0.2423 0.4150 0.960 0.040
#> A608BCEB-2C27-4927-A308-E6975F641722 1 0.9988 0.3842 0.520 0.480
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 1 0.9993 0.3835 0.516 0.484
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.9580 -0.6013 0.620 0.380
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 1 0.0938 0.4228 0.988 0.012
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.5517 4.45e-01 0.268 0.004 0.728
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 3 0.4931 4.46e-01 0.000 0.232 0.768
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.1031 8.37e-01 0.000 0.976 0.024
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.8562 3.34e-01 0.208 0.608 0.184
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.3918 5.67e-01 0.140 0.004 0.856
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.3425 6.00e-01 0.112 0.004 0.884
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.5285 3.21e-01 0.244 0.004 0.752
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 3 0.6274 7.25e-02 0.000 0.456 0.544
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0892 6.35e-01 0.020 0.000 0.980
#> 5482053D-9F48-4773-B68A-302B3A612503 3 0.9173 -2.60e-02 0.304 0.176 0.520
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.2711 6.21e-01 0.088 0.000 0.912
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 3 0.9964 -1.88e-01 0.296 0.336 0.368
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.0747 6.29e-01 0.000 0.016 0.984
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.9018 4.82e-01 0.548 0.176 0.276
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0892 6.35e-01 0.020 0.000 0.980
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.8503 1.08e-01 0.120 0.304 0.576
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.1643 8.31e-01 0.000 0.956 0.044
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.9018 4.82e-01 0.548 0.176 0.276
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 8.37e-01 0.000 1.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.5722 3.60e-01 0.704 0.004 0.292
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.3192 7.98e-01 0.000 0.888 0.112
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.0892 6.28e-01 0.000 0.020 0.980
#> F325847E-F046-4B67-B01C-16919C401020 3 0.6111 5.12e-02 0.396 0.000 0.604
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.5365 4.69e-01 0.252 0.004 0.744
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0237 8.37e-01 0.000 0.996 0.004
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.5722 3.60e-01 0.704 0.004 0.292
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.0475 6.35e-01 0.004 0.004 0.992
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.7660 3.45e-01 0.548 0.048 0.404
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.5553 4.44e-01 0.272 0.004 0.724
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.9018 4.82e-01 0.548 0.176 0.276
#> A8E48877-F8AB-44DD-A18B-194D87C44931 3 0.0892 6.30e-01 0.000 0.020 0.980
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.1129 6.37e-01 0.020 0.004 0.976
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.4842 5.70e-01 0.000 0.776 0.224
#> 91BA5F90-9174-4533-A050-39A28E34A94D 3 0.9129 1.63e-02 0.288 0.180 0.532
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 3 0.5404 4.42e-01 0.256 0.004 0.740
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.9192 3.64e-01 0.520 0.180 0.300
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.6421 2.33e-01 0.572 0.004 0.424
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0892 6.35e-01 0.020 0.000 0.980
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.0829 6.36e-01 0.004 0.012 0.984
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0592 8.35e-01 0.000 0.988 0.012
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.1031 6.35e-01 0.024 0.000 0.976
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.5656 3.65e-01 0.712 0.004 0.284
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.0747 6.29e-01 0.000 0.016 0.984
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.6513 1.31e-01 0.520 0.004 0.476
#> CB207A52-09AC-49D3-8240-5840CDFBB154 3 0.5404 4.42e-01 0.256 0.004 0.740
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.6274 2.50e-01 0.544 0.000 0.456
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 3 0.1620 6.32e-01 0.012 0.024 0.964
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0892 6.35e-01 0.020 0.000 0.980
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.6452 4.00e-01 0.264 0.032 0.704
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.1182 6.36e-01 0.012 0.012 0.976
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.8375 4.12e-01 0.540 0.092 0.368
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 3 0.9434 -2.73e-01 0.408 0.176 0.416
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 3 0.6154 -6.79e-05 0.408 0.000 0.592
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.6062 1.68e-01 0.384 0.000 0.616
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.9247 4.78e-01 0.524 0.200 0.276
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.9192 3.64e-01 0.520 0.180 0.300
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.5058 4.53e-01 0.756 0.000 0.244
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.4235 7.50e-01 0.000 0.824 0.176
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.5480 4.39e-01 0.264 0.004 0.732
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.5365 4.45e-01 0.252 0.004 0.744
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 3 0.9033 5.34e-02 0.272 0.180 0.548
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.6280 2.42e-01 0.540 0.000 0.460
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0237 8.36e-01 0.004 0.996 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 3 0.9212 -2.20e-02 0.304 0.180 0.516
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.3851 5.70e-01 0.136 0.004 0.860
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 8.37e-01 0.000 1.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.6295 2.41e-01 0.528 0.000 0.472
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.3941 5.67e-01 0.156 0.000 0.844
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.5722 3.60e-01 0.704 0.004 0.292
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0747 8.33e-01 0.016 0.984 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0237 8.37e-01 0.000 0.996 0.004
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.9299 3.50e-01 0.496 0.180 0.324
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.9018 4.82e-01 0.548 0.176 0.276
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0000 6.35e-01 0.000 0.000 1.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0592 6.33e-01 0.012 0.000 0.988
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.5690 3.62e-01 0.708 0.004 0.288
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.6274 2.50e-01 0.544 0.000 0.456
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0237 8.36e-01 0.004 0.996 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0892 6.35e-01 0.020 0.000 0.980
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.1031 6.35e-01 0.024 0.000 0.976
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.3879 5.71e-01 0.152 0.000 0.848
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.8659 4.88e-01 0.596 0.176 0.228
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 3 0.6510 1.97e-01 0.364 0.012 0.624
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.1031 6.35e-01 0.024 0.000 0.976
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 3 0.6521 -1.16e-01 0.496 0.004 0.500
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 8.37e-01 0.000 1.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.4931 6.96e-01 0.000 0.768 0.232
#> 53A96249-66D5-4C26-893B-ADC71481D261 3 0.2356 6.02e-01 0.000 0.072 0.928
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.8883 4.87e-01 0.568 0.176 0.256
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.4002 7.66e-01 0.000 0.840 0.160
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 8.37e-01 0.000 1.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.6286 2.34e-01 0.536 0.000 0.464
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.8666 4.44e-01 0.544 0.120 0.336
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.4346 5.34e-01 0.184 0.000 0.816
#> C2662596-6E2F-4924-B051-CEA1AC87B197 3 0.9267 -4.99e-02 0.316 0.180 0.504
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.5404 4.42e-01 0.256 0.004 0.740
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 3 0.6495 3.46e-01 0.060 0.200 0.740
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 3 0.4235 5.43e-01 0.176 0.000 0.824
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 3 0.9007 5.68e-02 0.268 0.180 0.552
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 3 0.9231 -3.83e-02 0.308 0.180 0.512
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.4235 7.50e-01 0.000 0.824 0.176
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 3 0.9343 -1.24e-01 0.348 0.176 0.476
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.5722 3.60e-01 0.704 0.004 0.292
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 3 0.0747 6.29e-01 0.000 0.016 0.984
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0237 8.37e-01 0.000 0.996 0.004
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.9247 4.78e-01 0.524 0.200 0.276
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0747 6.29e-01 0.000 0.016 0.984
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.9314 3.47e-01 0.492 0.180 0.328
#> A60DC925-7343-496E-900D-0DD81D5C8123 1 0.9576 3.44e-01 0.408 0.196 0.396
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 3 0.9231 -3.01e-02 0.308 0.180 0.512
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.5431 4.20e-01 0.000 0.716 0.284
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.4682 5.23e-01 0.192 0.004 0.804
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.5690 3.62e-01 0.708 0.004 0.288
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0892 6.35e-01 0.020 0.000 0.980
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.4002 7.66e-01 0.000 0.840 0.160
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.6299 2.35e-01 0.524 0.000 0.476
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.0747 6.29e-01 0.000 0.016 0.984
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.7969 3.07e-01 0.064 0.540 0.396
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.6215 -1.50e-02 0.428 0.000 0.572
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0000 6.35e-01 0.000 0.000 1.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.1031 6.35e-01 0.024 0.000 0.976
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 3 0.1753 6.18e-01 0.000 0.048 0.952
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.4465 7.50e-01 0.004 0.820 0.176
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.1031 8.29e-01 0.000 0.976 0.024
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.5285 4.60e-01 0.244 0.004 0.752
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.0892 6.30e-01 0.000 0.020 0.980
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.7397 0.2567 0.292 0.000 0.508 0.200
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.4985 0.2308 0.000 0.532 0.468 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0817 0.8559 0.000 0.976 0.024 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.4008 0.6135 0.000 0.756 0.000 0.244
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.5619 0.5336 0.064 0.000 0.688 0.248
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.4669 0.6342 0.036 0.000 0.764 0.200
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.5000 -0.2087 0.000 0.000 0.500 0.500
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.4356 0.6442 0.000 0.708 0.292 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.8364 0.000 0.000 1.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.3205 0.7236 0.872 0.000 0.024 0.104
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.3610 0.6839 0.200 0.000 0.800 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 4 0.5874 0.7288 0.000 0.124 0.176 0.700
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.0000 0.8364 0.000 0.000 1.000 0.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 4 0.3610 0.6591 0.200 0.000 0.000 0.800
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.8364 0.000 0.000 1.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.7270 0.3541 0.000 0.504 0.332 0.164
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0188 0.8617 0.000 0.996 0.004 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.4522 0.5219 0.680 0.000 0.000 0.320
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.8623 0.000 1.000 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.3610 0.7143 0.800 0.000 0.000 0.200
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.1716 0.8390 0.000 0.936 0.064 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.0000 0.8364 0.000 0.000 1.000 0.000
#> F325847E-F046-4B67-B01C-16919C401020 4 0.3726 0.8013 0.000 0.000 0.212 0.788
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.5448 0.5981 0.080 0.000 0.724 0.196
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.8623 0.000 1.000 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.3610 0.7143 0.800 0.000 0.000 0.200
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.0000 0.8364 0.000 0.000 1.000 0.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 4 0.4079 0.8125 0.020 0.000 0.180 0.800
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 4 0.4888 0.7598 0.036 0.000 0.224 0.740
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 4 0.3801 0.6414 0.220 0.000 0.000 0.780
#> A8E48877-F8AB-44DD-A18B-194D87C44931 3 0.0921 0.8265 0.028 0.000 0.972 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.0921 0.8265 0.028 0.000 0.972 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.8623 0.000 1.000 0.000 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.3545 0.7034 0.828 0.000 0.008 0.164
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.6917 0.4600 0.592 0.000 0.208 0.200
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0469 0.7240 0.988 0.000 0.000 0.012
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.3610 0.7143 0.800 0.000 0.000 0.200
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0000 0.8364 0.000 0.000 1.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.1118 0.8225 0.036 0.000 0.964 0.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.8623 0.000 1.000 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0000 0.8364 0.000 0.000 1.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.3610 0.7143 0.800 0.000 0.000 0.200
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.0000 0.8364 0.000 0.000 1.000 0.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.3610 0.7143 0.800 0.000 0.000 0.200
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.6650 0.5018 0.624 0.000 0.200 0.176
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 4 0.3610 0.8130 0.000 0.000 0.200 0.800
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 3 0.1118 0.8225 0.036 0.000 0.964 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0188 0.8353 0.004 0.000 0.996 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.6946 0.4549 0.588 0.000 0.212 0.200
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.3557 0.7382 0.036 0.000 0.856 0.108
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 4 0.4436 0.7994 0.052 0.000 0.148 0.800
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.3649 0.6745 0.796 0.000 0.000 0.204
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 4 0.3610 0.8130 0.000 0.000 0.200 0.800
#> AD294665-6F90-459C-90D5-3058F210225D 4 0.6286 0.6588 0.140 0.000 0.200 0.660
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 4 0.3610 0.6572 0.000 0.200 0.000 0.800
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.7210 1.000 0.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.1940 0.5489 0.076 0.000 0.000 0.924
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.3486 0.7506 0.000 0.812 0.188 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 4 0.7699 0.0154 0.380 0.000 0.220 0.400
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.7031 0.4376 0.576 0.000 0.224 0.200
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.3450 0.7081 0.836 0.000 0.008 0.156
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 4 0.3610 0.8130 0.000 0.000 0.200 0.800
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.8623 0.000 1.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.3444 0.6912 0.816 0.000 0.000 0.184
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.4994 0.6138 0.048 0.000 0.744 0.208
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.8623 0.000 1.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 4 0.3610 0.8130 0.000 0.000 0.200 0.800
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.2830 0.7827 0.040 0.000 0.900 0.060
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.3610 0.7143 0.800 0.000 0.000 0.200
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0336 0.8584 0.000 0.992 0.000 0.008
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.8623 0.000 1.000 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0707 0.7252 0.980 0.000 0.000 0.020
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 4 0.3610 0.6591 0.200 0.000 0.000 0.800
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0000 0.8364 0.000 0.000 1.000 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0000 0.8364 0.000 0.000 1.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.3610 0.7143 0.800 0.000 0.000 0.200
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 4 0.3610 0.8130 0.000 0.000 0.200 0.800
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.8623 0.000 1.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.8364 0.000 0.000 1.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0000 0.8364 0.000 0.000 1.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.2973 0.7286 0.000 0.000 0.856 0.144
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.4907 0.2455 0.580 0.000 0.000 0.420
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.4579 0.7001 0.768 0.000 0.032 0.200
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.8364 0.000 0.000 1.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.3610 0.7143 0.800 0.000 0.000 0.200
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.8623 0.000 1.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.3610 0.7361 0.000 0.800 0.200 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 3 0.0592 0.8283 0.000 0.016 0.984 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 4 0.4564 0.4853 0.328 0.000 0.000 0.672
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.3219 0.7735 0.000 0.836 0.164 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.8623 0.000 1.000 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 4 0.3610 0.8130 0.000 0.000 0.200 0.800
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 4 0.4581 0.7499 0.120 0.000 0.080 0.800
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.5990 0.4818 0.284 0.000 0.644 0.072
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.3266 0.7030 0.832 0.000 0.000 0.168
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.7481 0.2868 0.484 0.000 0.316 0.200
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 3 0.7235 0.4024 0.288 0.028 0.584 0.100
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 3 0.7561 0.0428 0.348 0.000 0.452 0.200
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.5473 0.6250 0.724 0.000 0.084 0.192
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.3032 0.7211 0.868 0.000 0.008 0.124
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.3444 0.7549 0.000 0.816 0.184 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.3219 0.7052 0.836 0.000 0.000 0.164
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.3610 0.7143 0.800 0.000 0.000 0.200
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 3 0.0000 0.8364 0.000 0.000 1.000 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.8623 0.000 1.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 4 0.3791 0.6564 0.004 0.200 0.000 0.796
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0000 0.8364 0.000 0.000 1.000 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.2814 0.7191 0.868 0.000 0.000 0.132
#> A60DC925-7343-496E-900D-0DD81D5C8123 1 0.7485 0.2713 0.440 0.180 0.000 0.380
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.3450 0.7081 0.836 0.000 0.008 0.156
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.8623 0.000 1.000 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.6756 0.4419 0.188 0.000 0.612 0.200
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.3610 0.7143 0.800 0.000 0.000 0.200
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.8364 0.000 0.000 1.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.2704 0.8040 0.000 0.876 0.124 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 4 0.3610 0.8130 0.000 0.000 0.200 0.800
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.0000 0.8364 0.000 0.000 1.000 0.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.4485 0.7173 0.000 0.772 0.200 0.028
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 4 0.3610 0.8130 0.000 0.000 0.200 0.800
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0000 0.8364 0.000 0.000 1.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.8364 0.000 0.000 1.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 3 0.5165 0.6351 0.080 0.168 0.752 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.3311 0.7666 0.000 0.828 0.172 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.8623 0.000 1.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.7609 -0.0937 0.396 0.000 0.404 0.200
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.0000 0.8364 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.5652 0.23976 0.164 0.000 0.700 0.080 0.056
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 5 0.4235 0.26159 0.000 0.336 0.008 0.000 0.656
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0162 0.89439 0.000 0.996 0.004 0.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.3053 0.76867 0.164 0.828 0.008 0.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.6509 0.23604 0.316 0.024 0.048 0.040 0.572
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 5 0.7038 -0.10063 0.344 0.000 0.248 0.012 0.396
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 5 0.7248 0.21874 0.324 0.028 0.196 0.004 0.448
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 5 0.5375 0.00765 0.000 0.368 0.064 0.000 0.568
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.4304 0.26710 0.000 0.000 0.516 0.000 0.484
#> 5482053D-9F48-4773-B68A-302B3A612503 4 0.8463 0.17452 0.284 0.000 0.268 0.288 0.160
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.5889 0.24761 0.000 0.020 0.512 0.056 0.412
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.6077 0.14647 0.512 0.392 0.016 0.000 0.080
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 5 0.1981 0.60967 0.000 0.028 0.048 0.000 0.924
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.66786 1.000 0.000 0.000 0.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.4304 0.26710 0.000 0.000 0.516 0.000 0.484
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 5 0.6947 0.04477 0.132 0.356 0.040 0.000 0.472
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0579 0.89250 0.000 0.984 0.008 0.000 0.008
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.2909 0.55803 0.848 0.000 0.012 0.140 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.1478 0.89547 0.000 0.936 0.064 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 0.78604 0.000 0.000 0.000 1.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.1331 0.88386 0.000 0.952 0.008 0.000 0.040
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 5 0.1579 0.62439 0.000 0.032 0.024 0.000 0.944
#> F325847E-F046-4B67-B01C-16919C401020 1 0.5535 0.36658 0.568 0.000 0.352 0.000 0.080
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.5287 0.28984 0.012 0.000 0.592 0.036 0.360
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.1478 0.89547 0.000 0.936 0.064 0.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 0.78604 0.000 0.000 0.000 1.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 5 0.3238 0.51891 0.000 0.028 0.136 0.000 0.836
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.2690 0.68451 0.844 0.000 0.156 0.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.3563 0.65808 0.780 0.000 0.208 0.012 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0162 0.66646 0.996 0.000 0.000 0.004 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 5 0.1483 0.62040 0.000 0.028 0.008 0.012 0.952
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.4610 0.27511 0.000 0.000 0.556 0.012 0.432
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.1704 0.89389 0.000 0.928 0.068 0.000 0.004
#> 91BA5F90-9174-4533-A050-39A28E34A94D 3 0.6765 -0.28012 0.344 0.000 0.384 0.272 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 3 0.7146 0.00687 0.188 0.000 0.512 0.252 0.048
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 4 0.3724 0.68831 0.204 0.000 0.020 0.776 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0703 0.78141 0.000 0.000 0.024 0.976 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.4305 0.25356 0.000 0.000 0.512 0.000 0.488
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 5 0.1982 0.62195 0.000 0.028 0.028 0.012 0.932
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0290 0.89244 0.000 0.992 0.008 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.4304 0.26710 0.000 0.000 0.516 0.000 0.484
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 0.78604 0.000 0.000 0.000 1.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 5 0.1582 0.62150 0.000 0.028 0.028 0.000 0.944
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.3424 0.66324 0.000 0.000 0.240 0.760 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.7619 0.20856 0.392 0.000 0.236 0.320 0.052
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.3109 0.66305 0.800 0.000 0.200 0.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 5 0.1808 0.62099 0.000 0.044 0.008 0.012 0.936
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.4268 0.26718 0.000 0.000 0.556 0.000 0.444
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.6308 0.03710 0.196 0.000 0.552 0.248 0.004
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.3178 0.60209 0.032 0.036 0.036 0.012 0.884
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.2775 0.69720 0.884 0.000 0.076 0.004 0.036
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.4874 0.17409 0.632 0.000 0.040 0.328 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.3950 0.65827 0.796 0.000 0.068 0.000 0.136
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.4140 0.66898 0.792 0.000 0.148 0.012 0.048
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.3333 0.62319 0.788 0.208 0.004 0.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 4 0.5253 0.65773 0.200 0.000 0.124 0.676 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.4498 0.51067 0.688 0.000 0.032 0.280 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.3388 0.75444 0.000 0.792 0.008 0.000 0.200
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.6692 0.43452 0.532 0.000 0.308 0.124 0.036
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.6648 0.17366 0.180 0.000 0.612 0.136 0.072
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 3 0.6908 -0.28196 0.340 0.000 0.380 0.276 0.004
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.3109 0.66305 0.800 0.000 0.200 0.000 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0404 0.89206 0.000 0.988 0.012 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.5797 0.12277 0.592 0.000 0.132 0.276 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.7161 0.01899 0.284 0.000 0.180 0.044 0.492
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.1478 0.89547 0.000 0.936 0.064 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.3909 0.66890 0.800 0.000 0.148 0.004 0.048
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.4383 0.28871 0.004 0.000 0.572 0.000 0.424
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 0.78604 0.000 0.000 0.000 1.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0798 0.88948 0.008 0.976 0.016 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.1478 0.89547 0.000 0.936 0.064 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 4 0.5434 0.64297 0.208 0.000 0.136 0.656 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.66786 1.000 0.000 0.000 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 5 0.4262 -0.16531 0.000 0.000 0.440 0.000 0.560
#> 392897E4-6009-422C-B461-649F4DDF260C 5 0.3999 0.10005 0.000 0.000 0.344 0.000 0.656
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0404 0.78266 0.000 0.000 0.012 0.988 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.3177 0.66172 0.792 0.000 0.208 0.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0404 0.89206 0.000 0.988 0.012 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.4448 0.26993 0.004 0.000 0.516 0.000 0.480
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.4304 0.26710 0.000 0.000 0.516 0.000 0.484
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.4574 0.29174 0.012 0.000 0.576 0.000 0.412
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.3731 0.48999 0.800 0.000 0.040 0.160 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.7315 0.32706 0.000 0.192 0.096 0.540 0.172
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.4304 0.26710 0.000 0.000 0.516 0.000 0.484
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0000 0.78604 0.000 0.000 0.000 1.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0290 0.89244 0.000 0.992 0.008 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.5447 0.31856 0.000 0.500 0.060 0.000 0.440
#> 53A96249-66D5-4C26-893B-ADC71481D261 5 0.3401 0.59283 0.000 0.096 0.064 0.000 0.840
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.2193 0.61594 0.912 0.000 0.028 0.060 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.2193 0.85408 0.000 0.900 0.008 0.000 0.092
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.1478 0.89547 0.000 0.936 0.064 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.3636 0.60755 0.728 0.000 0.272 0.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.1408 0.69103 0.948 0.000 0.044 0.000 0.008
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.7407 0.27330 0.128 0.000 0.536 0.140 0.196
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.6483 -0.10713 0.484 0.000 0.216 0.300 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.6974 0.15636 0.180 0.000 0.580 0.156 0.084
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 5 0.5839 0.40960 0.000 0.192 0.072 0.060 0.676
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 3 0.7580 0.25763 0.160 0.000 0.468 0.088 0.284
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.7455 -0.20824 0.360 0.008 0.360 0.252 0.020
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 3 0.6934 -0.33210 0.308 0.000 0.376 0.312 0.004
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.3388 0.75444 0.000 0.792 0.008 0.000 0.200
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 4 0.5049 0.20720 0.484 0.000 0.032 0.484 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 0.78604 0.000 0.000 0.000 1.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 5 0.0794 0.62339 0.000 0.028 0.000 0.000 0.972
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.1478 0.89547 0.000 0.936 0.064 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.3010 0.64263 0.824 0.172 0.004 0.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 5 0.1195 0.62373 0.000 0.028 0.012 0.000 0.960
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 4 0.6301 0.46172 0.308 0.000 0.180 0.512 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 1 0.5882 0.52161 0.620 0.252 0.012 0.116 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 3 0.7018 -0.29340 0.328 0.000 0.380 0.284 0.008
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.1544 0.89462 0.000 0.932 0.068 0.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.6528 0.21552 0.200 0.000 0.100 0.080 0.620
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 0.78604 0.000 0.000 0.000 1.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.4304 0.26710 0.000 0.000 0.516 0.000 0.484
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.3291 0.85775 0.000 0.848 0.064 0.000 0.088
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.3794 0.65236 0.800 0.000 0.048 0.000 0.152
#> 2629FEE3-A203-4411-8A70-02A796C9505C 5 0.1750 0.61742 0.000 0.028 0.036 0.000 0.936
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.4933 0.71133 0.084 0.712 0.004 0.000 0.200
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.3305 0.65112 0.776 0.000 0.224 0.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 5 0.4262 -0.12765 0.000 0.000 0.440 0.000 0.560
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.4304 0.26710 0.000 0.000 0.516 0.000 0.484
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 5 0.5497 0.33598 0.000 0.264 0.064 0.020 0.652
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.3462 0.75784 0.000 0.792 0.012 0.000 0.196
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.1981 0.89035 0.000 0.920 0.064 0.000 0.016
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.6372 0.21202 0.168 0.000 0.644 0.108 0.080
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 5 0.0794 0.62339 0.000 0.028 0.000 0.000 0.972
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 5 0.7278 0.6594 0.184 0.000 0.304 0.128 0.384 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 6 0.5072 0.7053 0.000 0.120 0.068 0.028 0.048 0.736
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.1176 0.8101 0.000 0.956 0.000 0.000 0.024 0.020
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.5917 0.4227 0.248 0.500 0.000 0.000 0.248 0.004
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.8324 -0.1637 0.340 0.000 0.116 0.084 0.184 0.276
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.7175 -0.2193 0.360 0.000 0.396 0.068 0.156 0.020
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.7246 0.0724 0.372 0.000 0.208 0.000 0.108 0.312
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 6 0.3972 0.6496 0.000 0.144 0.012 0.000 0.068 0.776
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.7890 0.000 0.000 1.000 0.000 0.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.7539 -0.4933 0.324 0.000 0.148 0.256 0.272 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.6346 0.5355 0.016 0.000 0.584 0.072 0.100 0.228
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.6821 0.2563 0.480 0.180 0.000 0.000 0.252 0.088
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 6 0.3198 0.7034 0.000 0.000 0.260 0.000 0.000 0.740
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0937 0.4712 0.960 0.000 0.000 0.000 0.040 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.7890 0.000 0.000 1.000 0.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 6 0.6188 0.2979 0.048 0.272 0.004 0.000 0.124 0.552
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0603 0.8055 0.000 0.980 0.000 0.000 0.016 0.004
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.4041 0.3244 0.764 0.000 0.000 0.172 0.044 0.020
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.2930 0.8015 0.000 0.840 0.000 0.000 0.124 0.036
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 0.7161 0.000 0.000 0.000 1.000 0.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.1745 0.7894 0.000 0.924 0.000 0.000 0.020 0.056
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 6 0.2454 0.7115 0.000 0.000 0.160 0.000 0.000 0.840
#> F325847E-F046-4B67-B01C-16919C401020 1 0.4593 0.3086 0.604 0.000 0.352 0.000 0.004 0.040
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.5111 0.4878 0.132 0.000 0.708 0.076 0.084 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.2930 0.8015 0.000 0.840 0.000 0.000 0.124 0.036
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0260 0.7146 0.000 0.000 0.000 0.992 0.008 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 6 0.3804 0.4400 0.000 0.000 0.424 0.000 0.000 0.576
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.2630 0.4724 0.872 0.000 0.064 0.000 0.064 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.5753 0.3545 0.680 0.000 0.120 0.060 0.112 0.028
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.1453 0.4674 0.944 0.000 0.000 0.008 0.040 0.008
#> A8E48877-F8AB-44DD-A18B-194D87C44931 6 0.4450 0.7093 0.000 0.000 0.136 0.060 0.048 0.756
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.5481 0.5989 0.016 0.000 0.696 0.060 0.108 0.120
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.4507 0.7407 0.000 0.692 0.000 0.020 0.248 0.040
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.6125 -0.3968 0.356 0.000 0.000 0.320 0.324 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.6219 0.6702 0.184 0.000 0.016 0.312 0.484 0.004
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 4 0.3201 0.5850 0.208 0.000 0.000 0.780 0.012 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0909 0.7094 0.012 0.000 0.000 0.968 0.020 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.1082 0.7778 0.000 0.000 0.956 0.000 0.040 0.004
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 6 0.4640 0.6923 0.016 0.000 0.096 0.060 0.060 0.768
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.2230 0.7965 0.000 0.892 0.000 0.000 0.084 0.024
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0000 0.7890 0.000 0.000 1.000 0.000 0.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0260 0.7146 0.000 0.000 0.000 0.992 0.008 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 6 0.3175 0.7070 0.000 0.000 0.256 0.000 0.000 0.744
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.3201 0.4877 0.012 0.000 0.000 0.780 0.208 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 4 0.6513 -0.4081 0.336 0.000 0.012 0.364 0.284 0.004
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.3637 0.4443 0.792 0.000 0.124 0.000 0.084 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 6 0.4141 0.7026 0.004 0.000 0.084 0.060 0.056 0.796
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.3469 0.6391 0.008 0.000 0.764 0.004 0.220 0.004
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.7021 0.7130 0.196 0.000 0.088 0.296 0.420 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 6 0.5314 0.6646 0.020 0.004 0.088 0.060 0.104 0.724
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.2346 0.4621 0.868 0.000 0.008 0.000 0.124 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.5080 0.1222 0.612 0.000 0.000 0.308 0.060 0.020
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.4036 0.4364 0.772 0.000 0.008 0.000 0.100 0.120
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.3245 0.4274 0.796 0.000 0.016 0.000 0.184 0.004
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.5682 0.2967 0.528 0.224 0.000 0.000 0.248 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 4 0.3261 0.5886 0.204 0.000 0.000 0.780 0.016 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.4484 0.4130 0.688 0.000 0.000 0.252 0.048 0.012
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.3189 0.6766 0.000 0.796 0.000 0.000 0.020 0.184
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.6752 -0.2332 0.476 0.000 0.072 0.192 0.260 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.7080 0.8048 0.192 0.000 0.112 0.216 0.476 0.004
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.6119 -0.4041 0.356 0.000 0.000 0.340 0.304 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.3650 0.4471 0.792 0.000 0.116 0.000 0.092 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.2094 0.7901 0.024 0.908 0.000 0.000 0.064 0.004
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.5020 -0.0424 0.548 0.000 0.000 0.372 0.080 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.7893 -0.3236 0.372 0.000 0.228 0.100 0.260 0.040
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.2815 0.8024 0.000 0.848 0.000 0.000 0.120 0.032
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.3245 0.4274 0.796 0.000 0.016 0.000 0.184 0.004
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.1390 0.7745 0.016 0.000 0.948 0.004 0.032 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0260 0.7146 0.000 0.000 0.000 0.992 0.008 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.4401 0.6422 0.040 0.656 0.000 0.000 0.300 0.004
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.2930 0.8015 0.000 0.840 0.000 0.000 0.124 0.036
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 4 0.4228 0.5155 0.228 0.000 0.000 0.708 0.064 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0363 0.4746 0.988 0.000 0.000 0.000 0.012 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.3938 0.5172 0.000 0.000 0.728 0.000 0.044 0.228
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.5877 0.3269 0.016 0.000 0.544 0.000 0.172 0.268
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0363 0.7127 0.000 0.000 0.000 0.988 0.012 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.4052 0.4383 0.752 0.000 0.192 0.000 0.016 0.040
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.1501 0.7936 0.000 0.924 0.000 0.000 0.076 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.7890 0.000 0.000 1.000 0.000 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0000 0.7890 0.000 0.000 1.000 0.000 0.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.2789 0.7348 0.044 0.000 0.864 0.004 0.088 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.3404 0.4082 0.832 0.000 0.000 0.096 0.052 0.020
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.7356 0.0966 0.016 0.052 0.036 0.452 0.120 0.324
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.7890 0.000 0.000 1.000 0.000 0.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0363 0.7115 0.012 0.000 0.000 0.988 0.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0458 0.8052 0.000 0.984 0.000 0.000 0.016 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 6 0.4545 0.2659 0.000 0.344 0.008 0.000 0.032 0.616
#> 53A96249-66D5-4C26-893B-ADC71481D261 6 0.3000 0.7103 0.000 0.032 0.124 0.000 0.004 0.840
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.2670 0.4458 0.884 0.000 0.000 0.044 0.052 0.020
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.3054 0.7270 0.000 0.828 0.000 0.000 0.036 0.136
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.2887 0.8018 0.000 0.844 0.000 0.000 0.120 0.036
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.4109 0.4300 0.736 0.000 0.212 0.000 0.012 0.040
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.1124 0.4785 0.956 0.000 0.008 0.000 0.036 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.7163 0.7429 0.156 0.000 0.228 0.144 0.468 0.004
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.5720 -0.2116 0.472 0.000 0.000 0.356 0.172 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 5 0.7023 0.8036 0.184 0.000 0.108 0.220 0.484 0.004
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 6 0.8429 0.4219 0.016 0.176 0.168 0.100 0.112 0.428
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.7688 0.7324 0.172 0.000 0.188 0.148 0.456 0.036
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.6206 -0.4407 0.392 0.000 0.004 0.312 0.292 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 4 0.6483 -0.2811 0.332 0.000 0.016 0.348 0.304 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.4174 0.6519 0.000 0.732 0.000 0.000 0.084 0.184
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.5168 -0.1543 0.480 0.000 0.000 0.456 0.044 0.020
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0146 0.7156 0.000 0.000 0.000 0.996 0.004 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 6 0.3151 0.7089 0.000 0.000 0.252 0.000 0.000 0.748
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.2815 0.8024 0.000 0.848 0.000 0.000 0.120 0.032
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.5625 0.3109 0.540 0.216 0.000 0.000 0.244 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 6 0.3151 0.7089 0.000 0.000 0.252 0.000 0.000 0.748
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 4 0.5379 0.1705 0.336 0.000 0.000 0.536 0.128 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 1 0.7124 0.2813 0.436 0.224 0.000 0.108 0.232 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.6552 -0.4235 0.344 0.000 0.020 0.316 0.320 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.5187 0.6804 0.012 0.608 0.000 0.024 0.320 0.036
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 6 0.8800 -0.3850 0.192 0.000 0.136 0.156 0.248 0.268
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 0.7161 0.000 0.000 0.000 1.000 0.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.7890 0.000 0.000 1.000 0.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.4121 0.7605 0.000 0.748 0.000 0.000 0.116 0.136
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.3364 0.4329 0.780 0.000 0.000 0.000 0.196 0.024
#> 2629FEE3-A203-4411-8A70-02A796C9505C 6 0.3175 0.7070 0.000 0.000 0.256 0.000 0.000 0.744
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.7223 0.3644 0.108 0.384 0.000 0.000 0.304 0.204
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.4253 0.4283 0.732 0.000 0.208 0.000 0.020 0.040
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.5462 0.4097 0.016 0.000 0.600 0.000 0.120 0.264
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.7890 0.000 0.000 1.000 0.000 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 6 0.4766 0.6457 0.016 0.060 0.048 0.000 0.124 0.752
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.4634 0.6443 0.016 0.712 0.000 0.000 0.084 0.188
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.3172 0.7961 0.000 0.832 0.000 0.000 0.092 0.076
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.7152 0.8019 0.168 0.000 0.160 0.188 0.480 0.004
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 6 0.3420 0.7129 0.000 0.000 0.240 0.000 0.012 0.748
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "NMF"]
# you can also extract it by
# res = res_list["MAD:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.901 0.930 0.970 0.4605 0.533 0.533
#> 3 3 0.682 0.772 0.903 0.4489 0.736 0.532
#> 4 4 0.637 0.601 0.812 0.1033 0.847 0.593
#> 5 5 0.646 0.574 0.748 0.0740 0.894 0.637
#> 6 6 0.705 0.619 0.775 0.0427 0.922 0.675
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.0000 0.9825 1.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.0000 0.9431 0.000 1.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.9431 0.000 1.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0000 0.9431 0.000 1.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.8555 0.5929 0.720 0.280
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.0000 0.9825 1.000 0.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 2 0.9815 0.3293 0.420 0.580
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0000 0.9431 0.000 1.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.0000 0.9825 1.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.9825 1.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.0000 0.9825 1.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.0000 0.9431 0.000 1.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.2236 0.9198 0.036 0.964
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.9825 1.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.0000 0.9825 1.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.0000 0.9431 0.000 1.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.9431 0.000 1.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0000 0.9825 1.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.9431 0.000 1.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.0000 0.9825 1.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.9431 0.000 1.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.7602 0.7300 0.220 0.780
#> F325847E-F046-4B67-B01C-16919C401020 1 0.0000 0.9825 1.000 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.0000 0.9825 1.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.9431 0.000 1.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.0000 0.9825 1.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 1 0.3584 0.9136 0.932 0.068
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0000 0.9825 1.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.0000 0.9825 1.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.9825 1.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.6712 0.7882 0.176 0.824
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.0000 0.9825 1.000 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.9431 0.000 1.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.9825 1.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.0000 0.9825 1.000 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.9825 1.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.0000 0.9825 1.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.0000 0.9825 1.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 1.0000 0.0778 0.496 0.504
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.9431 0.000 1.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.0000 0.9825 1.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.0000 0.9825 1.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.0376 0.9409 0.004 0.996
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.0000 0.9825 1.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.0000 0.9825 1.000 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.0000 0.9825 1.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.1184 0.9336 0.016 0.984
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.0000 0.9825 1.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.0000 0.9825 1.000 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.0000 0.9431 0.000 1.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0000 0.9825 1.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0000 0.9825 1.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.0672 0.9751 0.992 0.008
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.0000 0.9825 1.000 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.2236 0.9202 0.036 0.964
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.9825 1.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0000 0.9825 1.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.9431 0.000 1.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.0000 0.9825 1.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0000 0.9825 1.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.9825 1.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.0000 0.9825 1.000 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.9431 0.000 1.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0000 0.9825 1.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.0672 0.9751 0.992 0.008
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.9431 0.000 1.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.0000 0.9825 1.000 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0000 0.9825 1.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.0000 0.9825 1.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.9431 0.000 1.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.9431 0.000 1.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.9825 1.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.9825 1.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.0000 0.9825 1.000 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.0000 0.9825 1.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.0000 0.9825 1.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0000 0.9825 1.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.9431 0.000 1.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.0000 0.9825 1.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.0000 0.9825 1.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.9825 1.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.9825 1.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.7139 0.7453 0.804 0.196
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.0000 0.9825 1.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.0000 0.9825 1.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.9431 0.000 1.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0000 0.9431 0.000 1.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0000 0.9431 0.000 1.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.9825 1.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.9431 0.000 1.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.9431 0.000 1.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0000 0.9825 1.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0000 0.9825 1.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.0000 0.9825 1.000 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.9825 1.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.0000 0.9825 1.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.8386 0.6171 0.732 0.268
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.0000 0.9825 1.000 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.0000 0.9825 1.000 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.9825 1.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.9431 0.000 1.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.9825 1.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.0000 0.9825 1.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.0000 0.9431 0.000 1.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.9431 0.000 1.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.4161 0.8816 0.084 0.916
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.9710 0.3864 0.400 0.600
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.9825 1.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.8207 0.6760 0.256 0.744
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.9825 1.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.9431 0.000 1.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.7528 0.7109 0.784 0.216
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.0000 0.9825 1.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.0000 0.9825 1.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.9431 0.000 1.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.7883 0.6767 0.764 0.236
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.7056 0.7680 0.192 0.808
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0000 0.9431 0.000 1.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.0000 0.9825 1.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.0000 0.9825 1.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.0000 0.9825 1.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0000 0.9431 0.000 1.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.9431 0.000 1.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.9431 0.000 1.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.0000 0.9825 1.000 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.5946 0.8246 0.144 0.856
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.2625 0.8110 0.084 0.000 0.916
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.0000 0.9345 0.000 1.000 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.9345 0.000 1.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0000 0.9345 0.000 1.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.2448 0.8144 0.000 0.076 0.924
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.0000 0.8642 0.000 0.000 1.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.0424 0.8602 0.000 0.008 0.992
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0000 0.9345 0.000 1.000 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.8642 0.000 0.000 1.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0592 0.8539 0.988 0.000 0.012
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.2878 0.7987 0.904 0.000 0.096
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.1031 0.9196 0.000 0.976 0.024
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.6180 0.2973 0.000 0.416 0.584
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 3 0.6308 -0.1028 0.492 0.000 0.508
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.8642 0.000 0.000 1.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.1031 0.9208 0.000 0.976 0.024
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.9345 0.000 1.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.2959 0.8065 0.900 0.000 0.100
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.9345 0.000 1.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.0000 0.8578 1.000 0.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.9345 0.000 1.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.9792 0.0461 0.352 0.408 0.240
#> F325847E-F046-4B67-B01C-16919C401020 3 0.0000 0.8642 0.000 0.000 1.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.8642 0.000 0.000 1.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.9345 0.000 1.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.0000 0.8578 1.000 0.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.3686 0.7646 0.000 0.140 0.860
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 3 0.4605 0.6579 0.204 0.000 0.796
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.0000 0.8642 0.000 0.000 1.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.6111 0.3985 0.604 0.000 0.396
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.0000 0.9345 0.000 1.000 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.2165 0.8284 0.064 0.000 0.936
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.5216 0.6174 0.260 0.740 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.4555 0.7218 0.800 0.000 0.200
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 3 0.6140 0.3683 0.404 0.000 0.596
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.8578 1.000 0.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.0000 0.8578 1.000 0.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0237 0.8626 0.004 0.000 0.996
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.6180 0.2904 0.000 0.584 0.416
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.9345 0.000 1.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0000 0.8642 0.000 0.000 1.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.0000 0.8578 1.000 0.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.6307 0.0773 0.000 0.488 0.512
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.0000 0.8578 1.000 0.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.4399 0.7341 0.812 0.000 0.188
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.0000 0.8642 0.000 0.000 1.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.1964 0.8935 0.000 0.944 0.056
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.4887 0.6733 0.228 0.000 0.772
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.6192 0.1779 0.580 0.000 0.420
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.4654 0.7068 0.000 0.792 0.208
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 3 0.0000 0.8642 0.000 0.000 1.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.0000 0.8578 1.000 0.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 3 0.0000 0.8642 0.000 0.000 1.000
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.0237 0.8624 0.004 0.000 0.996
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.1289 0.9127 0.000 0.968 0.032
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.8578 1.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.5859 0.5062 0.656 0.000 0.344
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.9345 0.000 1.000 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.0000 0.8642 0.000 0.000 1.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.5058 0.6514 0.244 0.000 0.756
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0424 0.8554 0.992 0.000 0.008
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.0000 0.8642 0.000 0.000 1.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.9345 0.000 1.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.2261 0.8268 0.932 0.000 0.068
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.0000 0.8642 0.000 0.000 1.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.9345 0.000 1.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 3 0.0000 0.8642 0.000 0.000 1.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.4796 0.6866 0.220 0.000 0.780
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.0000 0.8578 1.000 0.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.9345 0.000 1.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.9345 0.000 1.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.8578 1.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 3 0.5905 0.3642 0.352 0.000 0.648
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0000 0.8642 0.000 0.000 1.000
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.4605 0.6703 0.796 0.000 0.204
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.0000 0.8578 1.000 0.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 3 0.0000 0.8642 0.000 0.000 1.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.9345 0.000 1.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.8642 0.000 0.000 1.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0000 0.8642 0.000 0.000 1.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.4605 0.7078 0.204 0.000 0.796
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.5058 0.6693 0.756 0.000 0.244
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.0000 0.8578 1.000 0.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.8642 0.000 0.000 1.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.0000 0.8578 1.000 0.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.9345 0.000 1.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0000 0.9345 0.000 1.000 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0000 0.9345 0.000 1.000 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.6111 0.3985 0.604 0.000 0.396
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.9345 0.000 1.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.9345 0.000 1.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.0000 0.8642 0.000 0.000 1.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 3 0.0000 0.8642 0.000 0.000 1.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.4062 0.7276 0.836 0.000 0.164
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.4931 0.6838 0.768 0.000 0.232
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.3267 0.7881 0.116 0.000 0.884
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.5968 0.3930 0.636 0.364 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 3 0.6140 0.3683 0.404 0.000 0.596
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.2866 0.8109 0.916 0.076 0.008
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.8578 1.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.9345 0.000 1.000 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.8578 1.000 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.0000 0.8578 1.000 0.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.0000 0.9345 0.000 1.000 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.9345 0.000 1.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.5798 0.7189 0.044 0.780 0.176
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.2448 0.8760 0.000 0.924 0.076
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.8578 1.000 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 1 0.5138 0.6105 0.748 0.252 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.4399 0.7339 0.812 0.000 0.188
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.3116 0.8354 0.108 0.892 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.8333 0.4102 0.328 0.100 0.572
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.0000 0.8578 1.000 0.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.8642 0.000 0.000 1.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.9345 0.000 1.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 3 0.3267 0.7803 0.000 0.116 0.884
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.6095 0.3562 0.000 0.392 0.608
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0000 0.9345 0.000 1.000 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.0000 0.8642 0.000 0.000 1.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0237 0.8626 0.004 0.000 0.996
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.8642 0.000 0.000 1.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 1 0.6305 0.0437 0.516 0.484 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.9345 0.000 1.000 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.9345 0.000 1.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.5706 0.5356 0.320 0.000 0.680
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.5810 0.4592 0.000 0.664 0.336
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.6136 0.2844 0.632 0.000 0.288 0.080
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.0657 0.8877 0.012 0.984 0.004 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0188 0.8910 0.000 0.996 0.004 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.1940 0.8494 0.076 0.924 0.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.6701 0.5575 0.296 0.120 0.584 0.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.3486 0.7242 0.188 0.000 0.812 0.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.3907 0.7335 0.140 0.032 0.828 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0524 0.8892 0.008 0.988 0.004 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.1743 0.7589 0.056 0.000 0.940 0.004
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.5355 0.3308 0.620 0.000 0.020 0.360
#> A31D342D-C67C-428B-BAED-C6E844277A09 4 0.6708 -0.0857 0.448 0.000 0.088 0.464
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.1677 0.8680 0.040 0.948 0.012 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.1716 0.7414 0.000 0.064 0.936 0.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.2563 0.5747 0.908 0.000 0.020 0.072
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.1211 0.7628 0.040 0.000 0.960 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.2401 0.8417 0.004 0.904 0.092 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0188 0.8910 0.000 0.996 0.004 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.1867 0.5854 0.928 0.000 0.000 0.072
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0188 0.8899 0.004 0.996 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0592 0.7679 0.016 0.000 0.000 0.984
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.8908 0.000 1.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.7816 0.3634 0.040 0.252 0.560 0.148
#> F325847E-F046-4B67-B01C-16919C401020 3 0.1716 0.7642 0.064 0.000 0.936 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.1867 0.7631 0.072 0.000 0.928 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0524 0.8871 0.004 0.988 0.000 0.008
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0592 0.7679 0.016 0.000 0.000 0.984
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.0188 0.7656 0.004 0.000 0.996 0.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.4540 0.4579 0.772 0.000 0.196 0.032
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.4655 0.6195 0.312 0.000 0.684 0.004
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.2060 0.5847 0.932 0.000 0.016 0.052
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.2775 0.8357 0.020 0.896 0.084 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.1042 0.7633 0.020 0.000 0.972 0.008
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.3161 0.7930 0.012 0.864 0.000 0.124
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.1661 0.5877 0.944 0.000 0.004 0.052
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 3 0.5810 0.5374 0.064 0.000 0.660 0.276
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.4998 0.1316 0.512 0.000 0.000 0.488
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0592 0.7679 0.016 0.000 0.000 0.984
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0592 0.7654 0.016 0.000 0.984 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.4741 0.6770 0.028 0.744 0.228 0.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0188 0.8910 0.000 0.996 0.004 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0707 0.7655 0.020 0.000 0.980 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0592 0.7679 0.016 0.000 0.000 0.984
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.2737 0.7055 0.008 0.104 0.888 0.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0592 0.7651 0.016 0.000 0.000 0.984
#> CB207A52-09AC-49D3-8240-5840CDFBB154 4 0.4643 0.3131 0.344 0.000 0.000 0.656
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.5126 0.4305 0.444 0.000 0.552 0.004
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.5193 0.5172 0.020 0.656 0.324 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.2313 0.7479 0.032 0.000 0.924 0.044
#> 5E343116-414B-41F2-AAEE-A3225450135A 4 0.7679 -0.0314 0.216 0.000 0.376 0.408
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.5217 0.2705 0.012 0.608 0.380 0.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.5696 -0.3758 0.492 0.000 0.484 0.024
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 4 0.5168 -0.1690 0.496 0.000 0.004 0.500
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 3 0.4605 0.6082 0.336 0.000 0.664 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.5691 0.4942 0.408 0.000 0.564 0.028
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.4262 0.6703 0.236 0.756 0.000 0.008
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.4898 0.2646 0.584 0.000 0.000 0.416
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.4776 0.2461 0.376 0.000 0.000 0.624
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0188 0.8910 0.000 0.996 0.004 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.4454 0.6398 0.308 0.000 0.692 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.6156 0.4413 0.064 0.000 0.592 0.344
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.3591 0.5394 0.824 0.000 0.008 0.168
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.5143 0.4076 0.456 0.000 0.540 0.004
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.8908 0.000 1.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.3311 0.5436 0.828 0.000 0.000 0.172
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.4250 0.6671 0.276 0.000 0.724 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.8908 0.000 1.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.6011 -0.3877 0.480 0.000 0.480 0.040
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.7629 0.1978 0.452 0.000 0.328 0.220
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0592 0.7679 0.016 0.000 0.000 0.984
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0336 0.8888 0.008 0.992 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0188 0.8899 0.004 0.996 0.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.4998 0.1167 0.512 0.000 0.000 0.488
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.2329 0.5659 0.916 0.000 0.072 0.012
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0336 0.7646 0.008 0.000 0.992 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 4 0.5137 0.5001 0.040 0.000 0.244 0.716
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0592 0.7679 0.016 0.000 0.000 0.984
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 3 0.2921 0.7458 0.140 0.000 0.860 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0188 0.8910 0.000 0.996 0.004 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.1118 0.7679 0.036 0.000 0.964 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.1302 0.7653 0.044 0.000 0.956 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.6686 0.2885 0.520 0.000 0.388 0.092
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.1978 0.5848 0.928 0.000 0.004 0.068
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0336 0.7593 0.008 0.000 0.000 0.992
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0592 0.7658 0.016 0.000 0.984 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0707 0.7645 0.020 0.000 0.000 0.980
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0188 0.8910 0.000 0.996 0.004 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0188 0.8910 0.000 0.996 0.004 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.4114 0.7719 0.016 0.812 0.164 0.008
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.1824 0.5862 0.936 0.000 0.004 0.060
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0188 0.8910 0.000 0.996 0.004 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.8908 0.000 1.000 0.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.4304 0.6498 0.284 0.000 0.716 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 3 0.4866 0.5260 0.404 0.000 0.596 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 4 0.3245 0.6728 0.064 0.000 0.056 0.880
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.1824 0.5862 0.936 0.000 0.004 0.060
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.6083 0.4355 0.056 0.000 0.584 0.360
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.7978 0.0331 0.404 0.196 0.012 0.388
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 3 0.6106 0.4306 0.060 0.000 0.592 0.348
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.5472 0.2692 0.608 0.004 0.016 0.372
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.5376 0.2740 0.588 0.000 0.016 0.396
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0469 0.8890 0.000 0.988 0.012 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.4643 0.3776 0.656 0.000 0.000 0.344
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0592 0.7679 0.016 0.000 0.000 0.984
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.5291 0.5637 0.024 0.652 0.324 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.8908 0.000 1.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.2884 0.5593 0.900 0.068 0.004 0.028
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.5392 0.2838 0.012 0.528 0.460 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.4999 0.1114 0.508 0.000 0.000 0.492
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.6203 0.3799 0.068 0.592 0.000 0.340
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.2401 0.5822 0.904 0.000 0.004 0.092
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.2149 0.8325 0.000 0.912 0.000 0.088
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.8132 0.3386 0.068 0.100 0.496 0.336
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0336 0.7645 0.008 0.000 0.000 0.992
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.1022 0.7657 0.032 0.000 0.968 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.8908 0.000 1.000 0.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.6999 -0.3527 0.460 0.088 0.444 0.008
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.1739 0.7556 0.024 0.016 0.952 0.008
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0376 0.8903 0.004 0.992 0.004 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.4776 0.5497 0.376 0.000 0.624 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0592 0.7651 0.016 0.000 0.984 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0336 0.7652 0.008 0.000 0.992 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.6837 -0.0161 0.048 0.464 0.024 0.464
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.8908 0.000 1.000 0.000 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.8908 0.000 1.000 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.5972 0.5064 0.064 0.000 0.632 0.304
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.4090 0.6659 0.032 0.140 0.824 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.6126 0.2214 0.484 0.000 0.100 0.008 0.408
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.4199 0.6780 0.000 0.772 0.068 0.000 0.160
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0609 0.8301 0.000 0.980 0.020 0.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.3289 0.7620 0.108 0.844 0.000 0.000 0.048
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.6704 0.4696 0.212 0.068 0.124 0.000 0.596
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 5 0.5778 0.2406 0.088 0.004 0.352 0.000 0.556
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.6965 0.2505 0.048 0.148 0.528 0.000 0.276
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.3620 0.7366 0.000 0.824 0.068 0.000 0.108
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.4774 0.3210 0.028 0.000 0.612 0.000 0.360
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.5034 0.6311 0.648 0.000 0.016 0.028 0.308
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.7604 0.3468 0.404 0.000 0.216 0.056 0.324
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.4222 0.7210 0.020 0.792 0.044 0.000 0.144
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.3736 0.5798 0.000 0.140 0.808 0.000 0.052
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.5268 0.3838 0.692 0.000 0.076 0.016 0.216
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.3596 0.5697 0.012 0.000 0.776 0.000 0.212
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.3916 0.6266 0.000 0.732 0.256 0.000 0.012
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0566 0.8311 0.000 0.984 0.012 0.000 0.004
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0324 0.6873 0.992 0.000 0.000 0.004 0.004
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0992 0.8277 0.008 0.968 0.000 0.000 0.024
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0324 0.9525 0.004 0.000 0.004 0.992 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0451 0.8308 0.000 0.988 0.004 0.000 0.008
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.6331 0.2763 0.004 0.104 0.568 0.020 0.304
#> F325847E-F046-4B67-B01C-16919C401020 3 0.4283 0.3532 0.008 0.000 0.644 0.000 0.348
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.3119 0.6261 0.068 0.000 0.860 0.000 0.072
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.1750 0.8196 0.036 0.936 0.000 0.000 0.028
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0290 0.9540 0.008 0.000 0.000 0.992 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.2233 0.6235 0.000 0.004 0.892 0.000 0.104
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.5289 0.2898 0.652 0.000 0.096 0.000 0.252
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.5370 0.4241 0.136 0.000 0.680 0.004 0.180
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.1365 0.6709 0.952 0.000 0.004 0.004 0.040
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.6224 0.0389 0.000 0.468 0.144 0.000 0.388
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.3300 0.5473 0.000 0.000 0.792 0.004 0.204
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.2684 0.8071 0.044 0.900 0.000 0.024 0.032
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.1704 0.6945 0.928 0.000 0.004 0.000 0.068
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.4766 0.3428 0.020 0.000 0.244 0.028 0.708
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.4897 0.5827 0.688 0.000 0.004 0.252 0.056
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0451 0.9537 0.008 0.000 0.000 0.988 0.004
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.1121 0.6523 0.000 0.000 0.956 0.000 0.044
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.5650 0.0090 0.000 0.464 0.460 0.000 0.076
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0609 0.8301 0.000 0.980 0.020 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.1732 0.6447 0.000 0.000 0.920 0.000 0.080
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0671 0.9478 0.016 0.000 0.000 0.980 0.004
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.3123 0.5623 0.000 0.160 0.828 0.000 0.012
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0451 0.9537 0.008 0.000 0.004 0.988 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 4 0.5472 0.4759 0.208 0.000 0.000 0.652 0.140
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.6855 0.0492 0.312 0.000 0.416 0.004 0.268
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 5 0.6673 0.1291 0.000 0.380 0.232 0.000 0.388
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.2280 0.6417 0.000 0.000 0.880 0.000 0.120
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.4817 0.3012 0.160 0.028 0.016 0.032 0.764
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.5505 0.3993 0.004 0.620 0.084 0.000 0.292
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.5233 0.4661 0.304 0.000 0.052 0.008 0.636
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.5038 0.6321 0.656 0.000 0.004 0.052 0.288
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 5 0.6495 0.4324 0.192 0.012 0.248 0.000 0.548
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.6520 0.4890 0.244 0.000 0.136 0.036 0.584
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.5433 0.5472 0.232 0.664 0.000 0.008 0.096
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.3769 0.6873 0.796 0.000 0.004 0.028 0.172
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.1648 0.9131 0.040 0.000 0.000 0.940 0.020
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0880 0.8268 0.000 0.968 0.032 0.000 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.5876 0.4372 0.192 0.000 0.204 0.000 0.604
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.5205 0.3976 0.008 0.000 0.200 0.096 0.696
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.3845 0.6754 0.760 0.000 0.004 0.012 0.224
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.7111 0.0219 0.336 0.004 0.392 0.008 0.260
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0865 0.8280 0.004 0.972 0.000 0.000 0.024
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.1059 0.6878 0.968 0.000 0.004 0.008 0.020
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.6366 0.3044 0.168 0.004 0.308 0.000 0.520
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0912 0.8294 0.012 0.972 0.000 0.000 0.016
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.6084 0.4804 0.284 0.000 0.120 0.012 0.584
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 5 0.6724 -0.3633 0.416 0.000 0.152 0.016 0.416
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 0.9514 0.000 0.000 0.000 1.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.1469 0.8234 0.016 0.948 0.000 0.000 0.036
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.1750 0.8196 0.036 0.936 0.000 0.000 0.028
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.4907 0.6508 0.680 0.000 0.004 0.052 0.264
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.4593 0.4851 0.756 0.000 0.076 0.008 0.160
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.1430 0.6522 0.000 0.004 0.944 0.000 0.052
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.5613 0.3528 0.000 0.000 0.592 0.308 0.100
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0451 0.9537 0.008 0.000 0.000 0.988 0.004
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.5461 0.2600 0.076 0.000 0.344 0.000 0.580
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0609 0.8301 0.000 0.980 0.020 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.3790 0.5206 0.004 0.000 0.724 0.000 0.272
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.4040 0.4840 0.012 0.000 0.712 0.000 0.276
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.6625 0.4865 0.516 0.000 0.188 0.012 0.284
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.1202 0.6757 0.960 0.000 0.004 0.004 0.032
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0486 0.9451 0.004 0.000 0.004 0.988 0.004
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.1544 0.6528 0.000 0.000 0.932 0.000 0.068
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0451 0.9537 0.008 0.000 0.000 0.988 0.004
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0609 0.8301 0.000 0.980 0.020 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0609 0.8301 0.000 0.980 0.020 0.000 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.5805 0.4927 0.000 0.624 0.148 0.004 0.224
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0955 0.6750 0.968 0.000 0.000 0.004 0.028
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0794 0.8278 0.000 0.972 0.028 0.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.1310 0.8256 0.020 0.956 0.000 0.000 0.024
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.5216 0.4063 0.092 0.000 0.660 0.000 0.248
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.5748 0.4631 0.252 0.000 0.140 0.000 0.608
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.5903 0.1751 0.036 0.000 0.048 0.340 0.576
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.1116 0.6780 0.964 0.000 0.004 0.004 0.028
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 5 0.6900 0.3240 0.016 0.000 0.212 0.300 0.472
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.6791 0.5336 0.528 0.108 0.004 0.040 0.320
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.4976 0.3844 0.012 0.000 0.172 0.088 0.728
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.5419 0.5922 0.600 0.016 0.004 0.032 0.348
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.4698 0.6310 0.664 0.000 0.004 0.028 0.304
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0963 0.8266 0.000 0.964 0.036 0.000 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.1872 0.6810 0.928 0.000 0.000 0.052 0.020
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0451 0.9537 0.008 0.000 0.004 0.988 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.6771 -0.1856 0.000 0.368 0.272 0.000 0.360
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.1106 0.8272 0.012 0.964 0.000 0.000 0.024
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.4754 0.4962 0.748 0.100 0.000 0.008 0.144
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.3796 0.4204 0.000 0.300 0.700 0.000 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.5082 0.6494 0.680 0.000 0.004 0.072 0.244
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.6600 0.0721 0.080 0.452 0.000 0.424 0.044
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.1116 0.6872 0.964 0.000 0.004 0.004 0.028
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.2611 0.8083 0.028 0.904 0.000 0.028 0.040
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.4610 0.4463 0.040 0.040 0.092 0.024 0.804
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0486 0.9451 0.004 0.000 0.004 0.988 0.004
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.2248 0.6359 0.012 0.000 0.900 0.000 0.088
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0566 0.8306 0.000 0.984 0.004 0.000 0.012
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.7420 0.3527 0.296 0.128 0.092 0.000 0.484
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.1386 0.6457 0.000 0.016 0.952 0.000 0.032
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.2514 0.7964 0.000 0.896 0.044 0.000 0.060
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.6087 0.2572 0.188 0.000 0.568 0.000 0.244
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.1282 0.6500 0.000 0.004 0.952 0.000 0.044
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.1908 0.6379 0.000 0.000 0.908 0.000 0.092
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.8015 0.2177 0.104 0.460 0.044 0.076 0.316
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.1444 0.8211 0.000 0.948 0.040 0.000 0.012
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0162 0.8309 0.000 0.996 0.004 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.4835 0.3738 0.008 0.000 0.244 0.048 0.700
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.5752 0.0903 0.000 0.092 0.524 0.000 0.384
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 6 0.5676 0.4413 0.336 0.000 0.060 0.000 0.052 0.552
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 5 0.6077 0.2818 0.000 0.368 0.004 0.000 0.408 0.220
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0820 0.7997 0.000 0.972 0.012 0.000 0.000 0.016
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.6181 0.4954 0.216 0.528 0.004 0.000 0.020 0.232
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.1409 0.6608 0.008 0.000 0.012 0.000 0.948 0.032
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 5 0.3892 0.6319 0.000 0.020 0.104 0.000 0.796 0.080
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 5 0.6428 0.1002 0.000 0.140 0.384 0.000 0.428 0.048
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.4495 0.4500 0.000 0.672 0.000 0.000 0.072 0.256
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.6517 0.2972 0.108 0.000 0.500 0.000 0.092 0.300
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.2333 0.6996 0.872 0.000 0.004 0.000 0.004 0.120
#> A31D342D-C67C-428B-BAED-C6E844277A09 6 0.5759 0.3784 0.376 0.000 0.120 0.008 0.004 0.492
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.5710 0.5916 0.012 0.612 0.012 0.000 0.208 0.156
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.4951 0.5016 0.000 0.276 0.620 0.000 0.000 0.104
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.6203 0.4254 0.612 0.000 0.136 0.004 0.120 0.128
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.5336 0.5415 0.056 0.000 0.644 0.000 0.060 0.240
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.2748 0.7259 0.000 0.848 0.128 0.000 0.000 0.024
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0508 0.8054 0.000 0.984 0.012 0.000 0.000 0.004
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.0820 0.7545 0.972 0.000 0.000 0.000 0.012 0.016
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.2378 0.7975 0.000 0.848 0.000 0.000 0.000 0.152
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 0.9429 0.000 0.000 0.000 1.000 0.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.1674 0.8087 0.000 0.924 0.004 0.000 0.004 0.068
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.6087 0.2968 0.020 0.132 0.516 0.008 0.000 0.324
#> F325847E-F046-4B67-B01C-16919C401020 5 0.5527 0.1286 0.000 0.000 0.408 0.000 0.460 0.132
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.3351 0.7086 0.084 0.000 0.840 0.000 0.028 0.048
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.4055 0.7525 0.008 0.728 0.000 0.000 0.036 0.228
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 0.9429 0.000 0.000 0.000 1.000 0.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.4562 0.6532 0.000 0.060 0.744 0.000 0.048 0.148
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.6523 0.3088 0.548 0.000 0.112 0.000 0.208 0.132
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.3975 0.6340 0.132 0.000 0.788 0.000 0.036 0.044
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.1418 0.7480 0.944 0.000 0.000 0.000 0.024 0.032
#> A8E48877-F8AB-44DD-A18B-194D87C44931 5 0.6034 0.5073 0.000 0.128 0.044 0.000 0.556 0.272
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.5026 0.5537 0.000 0.000 0.656 0.004 0.180 0.160
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.4776 0.7215 0.008 0.676 0.000 0.016 0.044 0.256
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.1088 0.7527 0.960 0.000 0.000 0.000 0.016 0.024
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 6 0.5563 -0.3706 0.016 0.000 0.064 0.008 0.452 0.460
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.1913 0.7224 0.908 0.000 0.000 0.080 0.000 0.012
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0146 0.9417 0.000 0.000 0.000 0.996 0.000 0.004
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.1480 0.7261 0.000 0.000 0.940 0.000 0.020 0.040
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.5332 0.4032 0.000 0.332 0.580 0.000 0.036 0.052
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0820 0.7997 0.000 0.972 0.012 0.000 0.000 0.016
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.1138 0.7230 0.004 0.000 0.960 0.000 0.024 0.012
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0146 0.9417 0.000 0.000 0.000 0.996 0.000 0.004
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.3795 0.6918 0.000 0.120 0.800 0.000 0.020 0.060
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 0.9429 0.000 0.000 0.000 1.000 0.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.4864 0.3829 0.008 0.004 0.000 0.364 0.584 0.040
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.5916 0.4077 0.280 0.000 0.572 0.000 0.064 0.084
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 5 0.6199 0.5128 0.000 0.108 0.072 0.000 0.552 0.268
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.1542 0.7214 0.000 0.000 0.936 0.004 0.008 0.052
#> 5E343116-414B-41F2-AAEE-A3225450135A 5 0.3971 0.4766 0.016 0.004 0.004 0.000 0.704 0.272
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.3450 0.6029 0.004 0.116 0.004 0.000 0.820 0.056
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.1218 0.6500 0.012 0.000 0.004 0.000 0.956 0.028
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.3323 0.5198 0.752 0.000 0.008 0.000 0.000 0.240
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 5 0.4233 0.6218 0.008 0.004 0.040 0.000 0.724 0.224
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.3099 0.6487 0.008 0.000 0.008 0.000 0.808 0.176
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.6879 0.3848 0.216 0.424 0.000 0.004 0.052 0.304
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.1225 0.7470 0.956 0.000 0.004 0.004 0.004 0.032
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0717 0.9267 0.000 0.000 0.008 0.976 0.000 0.016
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.1297 0.7899 0.000 0.948 0.012 0.000 0.000 0.040
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.1461 0.6648 0.000 0.000 0.044 0.000 0.940 0.016
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 5 0.4035 0.6284 0.004 0.000 0.052 0.000 0.740 0.204
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.1949 0.7228 0.904 0.000 0.004 0.000 0.004 0.088
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.6105 0.3674 0.300 0.004 0.548 0.000 0.056 0.092
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.3253 0.7808 0.000 0.788 0.000 0.000 0.020 0.192
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.1138 0.7535 0.960 0.000 0.004 0.000 0.024 0.012
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.1779 0.6620 0.000 0.000 0.064 0.000 0.920 0.016
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.2416 0.7981 0.000 0.844 0.000 0.000 0.000 0.156
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.3130 0.6552 0.000 0.004 0.008 0.004 0.808 0.176
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 6 0.5619 0.4519 0.356 0.000 0.088 0.000 0.024 0.532
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 0.9429 0.000 0.000 0.000 1.000 0.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.3535 0.7687 0.012 0.760 0.000 0.000 0.008 0.220
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.3087 0.7901 0.004 0.808 0.000 0.000 0.012 0.176
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.1542 0.7390 0.936 0.000 0.004 0.008 0.000 0.052
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.4989 0.5062 0.700 0.000 0.168 0.000 0.036 0.096
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.1630 0.7275 0.000 0.016 0.940 0.000 0.020 0.024
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.2950 0.6621 0.000 0.000 0.828 0.148 0.000 0.024
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0146 0.9417 0.000 0.000 0.000 0.996 0.000 0.004
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.1398 0.6645 0.000 0.000 0.052 0.000 0.940 0.008
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0622 0.8022 0.000 0.980 0.012 0.000 0.000 0.008
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.6089 0.3736 0.020 0.000 0.524 0.000 0.224 0.232
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.5722 0.5025 0.052 0.000 0.612 0.000 0.100 0.236
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.4474 0.4519 0.708 0.000 0.172 0.000 0.000 0.120
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.1176 0.7507 0.956 0.000 0.000 0.000 0.020 0.024
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0146 0.9408 0.000 0.000 0.000 0.996 0.000 0.004
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.1865 0.7245 0.000 0.000 0.920 0.000 0.040 0.040
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0000 0.9429 0.000 0.000 0.000 1.000 0.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0725 0.8010 0.000 0.976 0.012 0.000 0.000 0.012
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0363 0.8044 0.000 0.988 0.012 0.000 0.000 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.4280 0.5322 0.004 0.708 0.056 0.000 0.000 0.232
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.1074 0.7507 0.960 0.000 0.000 0.000 0.012 0.028
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0914 0.7990 0.000 0.968 0.016 0.000 0.000 0.016
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.3473 0.7776 0.004 0.780 0.000 0.000 0.024 0.192
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.3145 0.6912 0.028 0.000 0.856 0.000 0.060 0.056
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.1649 0.6611 0.008 0.000 0.040 0.000 0.936 0.016
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 5 0.5456 0.2528 0.016 0.000 0.024 0.044 0.576 0.340
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.1168 0.7494 0.956 0.000 0.000 0.000 0.016 0.028
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 5 0.4307 0.6363 0.000 0.000 0.068 0.016 0.744 0.172
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.5884 -0.1514 0.456 0.036 0.008 0.000 0.064 0.436
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.5234 0.2228 0.016 0.000 0.040 0.016 0.592 0.336
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.5237 -0.1162 0.480 0.004 0.000 0.000 0.080 0.436
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.2879 0.6375 0.816 0.000 0.004 0.000 0.004 0.176
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.1480 0.7870 0.000 0.940 0.040 0.000 0.000 0.020
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.1265 0.7438 0.948 0.000 0.000 0.008 0.000 0.044
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 0.9429 0.000 0.000 0.000 1.000 0.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 5 0.6864 0.3897 0.000 0.208 0.068 0.000 0.436 0.288
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.3364 0.7774 0.000 0.780 0.000 0.000 0.024 0.196
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.5878 0.3302 0.576 0.112 0.000 0.000 0.044 0.268
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.3073 0.6344 0.000 0.204 0.788 0.000 0.000 0.008
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.2766 0.6837 0.852 0.000 0.004 0.020 0.000 0.124
#> A60DC925-7343-496E-900D-0DD81D5C8123 4 0.6927 0.0691 0.036 0.256 0.000 0.420 0.012 0.276
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0508 0.7549 0.984 0.000 0.004 0.000 0.012 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.4384 0.7258 0.000 0.684 0.000 0.012 0.036 0.268
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.1606 0.6549 0.000 0.004 0.008 0.000 0.932 0.056
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0146 0.9408 0.000 0.000 0.000 0.996 0.000 0.004
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.1700 0.7176 0.012 0.000 0.936 0.000 0.024 0.028
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.2500 0.8047 0.000 0.868 0.012 0.000 0.004 0.116
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.3718 0.5841 0.020 0.016 0.024 0.000 0.812 0.128
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.3466 0.6950 0.000 0.084 0.816 0.000 0.004 0.096
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.5029 0.3409 0.000 0.612 0.000 0.000 0.276 0.112
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.4694 0.6056 0.132 0.000 0.740 0.000 0.056 0.072
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0725 0.7267 0.000 0.012 0.976 0.000 0.012 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.3237 0.6989 0.008 0.004 0.840 0.000 0.044 0.104
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 6 0.6522 0.1702 0.140 0.384 0.032 0.012 0.000 0.432
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.1333 0.7957 0.000 0.944 0.000 0.000 0.008 0.048
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0909 0.8073 0.000 0.968 0.012 0.000 0.000 0.020
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 5 0.4305 0.6157 0.012 0.000 0.044 0.000 0.712 0.232
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 5 0.6806 0.4215 0.000 0.156 0.088 0.000 0.464 0.292
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "hclust"]
# you can also extract it by
# res = res_list["ATC:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.509 0.776 0.886 0.4669 0.497 0.497
#> 3 3 0.458 0.678 0.722 0.2596 0.918 0.839
#> 4 4 0.480 0.671 0.756 0.1140 0.926 0.832
#> 5 5 0.593 0.749 0.814 0.1228 0.873 0.654
#> 6 6 0.686 0.779 0.855 0.0555 0.978 0.910
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.0000 0.920 1.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.8327 0.731 0.264 0.736
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0672 0.796 0.008 0.992
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.2948 0.889 0.948 0.052
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 2 1.0000 0.317 0.500 0.500
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.0672 0.916 0.992 0.008
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.0672 0.916 0.992 0.008
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.8327 0.731 0.264 0.736
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.0000 0.920 1.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.920 1.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 2 0.0000 0.796 0.000 1.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.2948 0.889 0.948 0.052
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.9661 0.585 0.392 0.608
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.920 1.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.0000 0.920 1.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 1 0.9896 0.247 0.560 0.440
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.796 0.000 1.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 2 0.0000 0.796 0.000 1.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.2603 0.795 0.044 0.956
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 2 0.9248 0.671 0.340 0.660
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.796 0.000 1.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.0000 0.796 0.000 1.000
#> F325847E-F046-4B67-B01C-16919C401020 1 0.0000 0.920 1.000 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.0000 0.920 1.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.796 0.000 1.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 2 0.9248 0.671 0.340 0.660
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 2 0.9866 0.501 0.432 0.568
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0000 0.920 1.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.0000 0.920 1.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.920 1.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.8327 0.731 0.264 0.736
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 2 0.9170 0.679 0.332 0.668
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.796 0.000 1.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.920 1.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 2 0.0000 0.796 0.000 1.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.920 1.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 2 0.9323 0.661 0.348 0.652
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.4939 0.832 0.892 0.108
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 1 0.5294 0.819 0.880 0.120
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.8144 0.704 0.252 0.748
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.0376 0.918 0.996 0.004
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 2 0.9323 0.661 0.348 0.652
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.9866 0.501 0.432 0.568
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 2 0.9248 0.671 0.340 0.660
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.3114 0.883 0.944 0.056
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.0000 0.920 1.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.8327 0.731 0.264 0.736
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 2 0.1633 0.796 0.024 0.976
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.6048 0.780 0.852 0.148
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.9129 0.682 0.328 0.672
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0000 0.920 1.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 2 0.0000 0.796 0.000 1.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.0000 0.920 1.000 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.0000 0.920 1.000 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.2948 0.889 0.948 0.052
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.920 1.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0000 0.920 1.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.796 0.000 1.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.9970 -0.243 0.532 0.468
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0000 0.920 1.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.2043 0.903 0.968 0.032
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 2 0.0000 0.796 0.000 1.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.796 0.000 1.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 2 0.9323 0.661 0.348 0.652
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 1.0000 -0.328 0.504 0.496
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.796 0.000 1.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.1843 0.906 0.972 0.028
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0000 0.920 1.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 2 0.9248 0.671 0.340 0.660
#> EE16D845-31F2-4178-800B-CA2C358841AD 1 0.9896 0.247 0.560 0.440
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.796 0.000 1.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.920 1.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.920 1.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.4815 0.836 0.896 0.104
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.6148 0.772 0.848 0.152
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 2 0.9323 0.661 0.348 0.652
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0000 0.920 1.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.796 0.000 1.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.0000 0.920 1.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.1184 0.912 0.984 0.016
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.920 1.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.920 1.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.9209 0.675 0.336 0.664
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.0000 0.920 1.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 2 0.9209 0.675 0.336 0.664
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.796 0.000 1.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.2603 0.795 0.044 0.956
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.6712 0.764 0.176 0.824
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.920 1.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.796 0.000 1.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.796 0.000 1.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0000 0.920 1.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0000 0.920 1.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.4815 0.836 0.896 0.104
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.920 1.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.0000 0.920 1.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.6048 0.780 0.852 0.148
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 2 0.0000 0.796 0.000 1.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.6048 0.780 0.852 0.148
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.2043 0.903 0.968 0.032
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.796 0.000 1.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 2 0.9323 0.661 0.348 0.652
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 2 0.9552 0.619 0.376 0.624
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.8327 0.731 0.264 0.736
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.796 0.000 1.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.2948 0.889 0.948 0.052
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.9522 0.622 0.372 0.628
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.920 1.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.0000 0.796 0.000 1.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.920 1.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.796 0.000 1.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 2 1.0000 0.317 0.500 0.500
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 2 0.9248 0.671 0.340 0.660
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.0000 0.920 1.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.2423 0.796 0.040 0.960
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.0000 0.920 1.000 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.7056 0.760 0.192 0.808
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 1 0.9896 0.247 0.560 0.440
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.0000 0.920 1.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.3431 0.876 0.936 0.064
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.0000 0.920 1.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0000 0.796 0.000 1.000
#> A608BCEB-2C27-4927-A308-E6975F641722 1 0.9896 0.247 0.560 0.440
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.2603 0.795 0.044 0.956
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.0000 0.920 1.000 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.8327 0.731 0.264 0.736
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.4887 0.824 0.772 0.000 0.228
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.6931 0.693 0.032 0.640 0.328
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.3573 0.322 0.004 0.876 0.120
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.3619 0.740 0.864 0.000 0.136
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 2 0.9579 0.505 0.200 0.432 0.368
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.2066 0.785 0.940 0.000 0.060
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.2066 0.785 0.940 0.000 0.060
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.6931 0.693 0.032 0.640 0.328
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.4796 0.826 0.780 0.000 0.220
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.4796 0.826 0.780 0.000 0.220
#> A31D342D-C67C-428B-BAED-C6E844277A09 2 0.1753 0.488 0.000 0.952 0.048
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.3619 0.740 0.864 0.000 0.136
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.8477 0.664 0.096 0.524 0.380
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.769 1.000 0.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.4796 0.826 0.780 0.000 0.220
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 1 0.9588 0.271 0.476 0.240 0.284
#> DC55EE78-203F-4092-9B83-14B1A529194B 3 0.6180 1.000 0.000 0.416 0.584
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 2 0.3482 0.313 0.000 0.872 0.128
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.1964 0.506 0.000 0.944 0.056
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 2 0.7451 0.694 0.040 0.564 0.396
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 3 0.6180 1.000 0.000 0.416 0.584
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.1753 0.488 0.000 0.952 0.048
#> F325847E-F046-4B67-B01C-16919C401020 1 0.0000 0.769 1.000 0.000 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.4796 0.826 0.780 0.000 0.220
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.2261 0.371 0.000 0.932 0.068
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 2 0.7451 0.694 0.040 0.564 0.396
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 2 0.8927 0.622 0.128 0.488 0.384
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.1411 0.770 0.964 0.000 0.036
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.4796 0.826 0.780 0.000 0.220
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.769 1.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.6931 0.693 0.032 0.640 0.328
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 2 0.7794 0.693 0.060 0.572 0.368
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.1031 0.430 0.000 0.976 0.024
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.4796 0.826 0.780 0.000 0.220
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 2 0.3619 0.285 0.000 0.864 0.136
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.4842 0.825 0.776 0.000 0.224
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 2 0.7476 0.692 0.040 0.556 0.404
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.7442 0.699 0.604 0.048 0.348
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 1 0.7705 0.698 0.604 0.064 0.332
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.8436 0.435 0.160 0.616 0.224
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.4842 0.825 0.776 0.000 0.224
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 2 0.7476 0.692 0.040 0.556 0.404
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.8927 0.622 0.128 0.488 0.384
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 2 0.7451 0.694 0.040 0.564 0.396
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.6798 0.777 0.696 0.048 0.256
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.1411 0.770 0.964 0.000 0.036
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.6931 0.693 0.032 0.640 0.328
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 2 0.3340 0.544 0.000 0.880 0.120
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.8022 0.625 0.544 0.068 0.388
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.7903 0.692 0.068 0.576 0.356
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.1411 0.770 0.964 0.000 0.036
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 2 0.3619 0.238 0.000 0.864 0.136
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.1411 0.770 0.964 0.000 0.036
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.4887 0.824 0.772 0.000 0.228
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.3619 0.740 0.864 0.000 0.136
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.4796 0.826 0.780 0.000 0.220
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.1411 0.770 0.964 0.000 0.036
#> A54731AE-FC40-407F-8D10-67DDC122237D 3 0.6180 1.000 0.000 0.416 0.584
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 2 0.9795 0.423 0.256 0.428 0.316
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.4887 0.824 0.772 0.000 0.228
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.5497 0.794 0.708 0.000 0.292
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 2 0.3482 0.313 0.000 0.872 0.128
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 3 0.6180 1.000 0.000 0.416 0.584
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 2 0.7476 0.692 0.040 0.556 0.404
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 2 0.9641 0.490 0.212 0.432 0.356
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.2261 0.371 0.000 0.932 0.068
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.2959 0.755 0.900 0.000 0.100
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.4796 0.826 0.780 0.000 0.220
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 2 0.7451 0.694 0.040 0.564 0.396
#> EE16D845-31F2-4178-800B-CA2C358841AD 1 0.9588 0.271 0.476 0.240 0.284
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.2261 0.371 0.000 0.932 0.068
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.4796 0.826 0.780 0.000 0.220
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.769 1.000 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.7401 0.706 0.612 0.048 0.340
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.8264 0.628 0.556 0.088 0.356
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 2 0.7476 0.692 0.040 0.556 0.404
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0000 0.769 1.000 0.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 3 0.6180 1.000 0.000 0.416 0.584
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.4796 0.826 0.780 0.000 0.220
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.5541 0.809 0.740 0.008 0.252
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.4796 0.826 0.780 0.000 0.220
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.769 1.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.7438 0.695 0.040 0.568 0.392
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.4796 0.826 0.780 0.000 0.220
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 2 0.7438 0.695 0.040 0.568 0.392
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 3 0.6180 1.000 0.000 0.416 0.584
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.3500 0.470 0.004 0.880 0.116
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.5119 0.612 0.028 0.812 0.160
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.769 1.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 3 0.6180 1.000 0.000 0.416 0.584
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0592 0.436 0.000 0.988 0.012
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0000 0.769 1.000 0.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0000 0.769 1.000 0.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.7348 0.704 0.608 0.044 0.348
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.4796 0.826 0.780 0.000 0.220
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.4796 0.826 0.780 0.000 0.220
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.8022 0.625 0.544 0.068 0.388
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 2 0.1753 0.488 0.000 0.952 0.048
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.8022 0.625 0.544 0.068 0.388
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.5497 0.794 0.708 0.000 0.292
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 3 0.6180 1.000 0.000 0.416 0.584
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 2 0.7476 0.692 0.040 0.556 0.404
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 2 0.8097 0.679 0.072 0.540 0.388
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.6931 0.693 0.032 0.640 0.328
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.2261 0.371 0.000 0.932 0.068
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.3619 0.740 0.864 0.000 0.136
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.8257 0.677 0.084 0.544 0.372
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.4842 0.825 0.776 0.000 0.224
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.3412 0.331 0.000 0.876 0.124
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.4796 0.826 0.780 0.000 0.220
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.2261 0.371 0.000 0.932 0.068
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 2 0.9579 0.505 0.200 0.432 0.368
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 2 0.7451 0.694 0.040 0.564 0.396
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.4796 0.826 0.780 0.000 0.220
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.1860 0.502 0.000 0.948 0.052
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.1411 0.770 0.964 0.000 0.036
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.6226 0.666 0.028 0.720 0.252
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 1 0.9588 0.271 0.476 0.240 0.284
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.0000 0.769 1.000 0.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.6869 0.771 0.688 0.048 0.264
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.4796 0.826 0.780 0.000 0.220
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.2261 0.371 0.000 0.932 0.068
#> A608BCEB-2C27-4927-A308-E6975F641722 1 0.9588 0.271 0.476 0.240 0.284
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.1964 0.506 0.000 0.944 0.056
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.4887 0.824 0.772 0.000 0.228
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.6931 0.693 0.032 0.640 0.328
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.0376 0.7440 0.992 0.000 0.004 0.004
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 4 0.4707 0.7567 0.204 0.036 0.000 0.760
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 4 0.6082 0.1191 0.008 0.192 0.104 0.696
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.6961 0.6073 0.496 0.000 0.388 0.116
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 4 0.5320 0.6428 0.416 0.000 0.012 0.572
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.5184 0.6965 0.672 0.000 0.304 0.024
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.5184 0.6965 0.672 0.000 0.304 0.024
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 4 0.4707 0.7567 0.204 0.036 0.000 0.760
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.0000 0.7459 1.000 0.000 0.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.7459 1.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 2 0.4832 0.7638 0.000 0.768 0.056 0.176
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.6961 0.6073 0.496 0.000 0.388 0.116
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 4 0.4746 0.7683 0.304 0.000 0.008 0.688
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.4585 0.6759 0.668 0.000 0.332 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.0000 0.7459 1.000 0.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 1 0.9266 0.1663 0.372 0.088 0.236 0.304
#> DC55EE78-203F-4092-9B83-14B1A529194B 3 0.7289 0.9824 0.000 0.252 0.536 0.212
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 4 0.5690 0.0948 0.000 0.216 0.084 0.700
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.5755 0.6693 0.040 0.664 0.008 0.288
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.4134 0.7927 0.260 0.000 0.000 0.740
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 3 0.7331 0.9804 0.000 0.260 0.528 0.212
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.4832 0.7638 0.000 0.768 0.056 0.176
#> F325847E-F046-4B67-B01C-16919C401020 1 0.4564 0.6774 0.672 0.000 0.328 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.0000 0.7459 1.000 0.000 0.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.3056 0.7260 0.000 0.888 0.072 0.040
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.4134 0.7927 0.260 0.000 0.000 0.740
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 4 0.5057 0.7428 0.340 0.000 0.012 0.648
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.5478 0.6710 0.628 0.000 0.344 0.028
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.0188 0.7461 0.996 0.000 0.004 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.4564 0.6774 0.672 0.000 0.328 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 4 0.4707 0.7567 0.204 0.036 0.000 0.760
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 4 0.4452 0.7878 0.260 0.008 0.000 0.732
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.2814 0.7750 0.000 0.868 0.000 0.132
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.7459 1.000 0.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 4 0.6549 -0.1257 0.000 0.268 0.120 0.612
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0188 0.7445 0.996 0.000 0.000 0.004
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.4452 0.7921 0.260 0.000 0.008 0.732
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.3718 0.5901 0.820 0.000 0.012 0.168
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 1 0.3718 0.5854 0.820 0.000 0.012 0.168
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 4 0.5919 0.3670 0.044 0.036 0.204 0.716
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.0188 0.7447 0.996 0.000 0.000 0.004
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.4452 0.7921 0.260 0.000 0.008 0.732
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 4 0.5057 0.7428 0.340 0.000 0.012 0.648
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.4134 0.7927 0.260 0.000 0.000 0.740
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.2402 0.6844 0.912 0.000 0.012 0.076
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.5478 0.6710 0.628 0.000 0.344 0.028
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 4 0.4707 0.7567 0.204 0.036 0.000 0.760
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 2 0.4927 0.7219 0.012 0.728 0.012 0.248
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.4319 0.4987 0.760 0.000 0.012 0.228
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 4 0.4576 0.7838 0.260 0.012 0.000 0.728
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.5478 0.6710 0.628 0.000 0.344 0.028
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 4 0.6178 -0.0338 0.000 0.228 0.112 0.660
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.5478 0.6710 0.628 0.000 0.344 0.028
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.0376 0.7440 0.992 0.000 0.004 0.004
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.6961 0.6073 0.496 0.000 0.388 0.116
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.7459 1.000 0.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.5478 0.6710 0.628 0.000 0.344 0.028
#> A54731AE-FC40-407F-8D10-67DDC122237D 3 0.7171 0.9754 0.000 0.232 0.556 0.212
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 4 0.5402 0.5594 0.472 0.000 0.012 0.516
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0376 0.7440 0.992 0.000 0.004 0.004
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.2124 0.7019 0.924 0.000 0.008 0.068
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 4 0.5690 0.0948 0.000 0.216 0.084 0.700
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 3 0.7331 0.9804 0.000 0.260 0.528 0.212
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 4 0.4452 0.7921 0.260 0.000 0.008 0.732
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 4 0.5345 0.6268 0.428 0.000 0.012 0.560
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.3056 0.7260 0.000 0.888 0.072 0.040
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.6543 0.6361 0.544 0.000 0.372 0.084
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0000 0.7459 1.000 0.000 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.4134 0.7927 0.260 0.000 0.000 0.740
#> EE16D845-31F2-4178-800B-CA2C358841AD 1 0.9266 0.1663 0.372 0.088 0.236 0.304
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.3229 0.7251 0.000 0.880 0.072 0.048
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.7459 1.000 0.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.4564 0.6774 0.672 0.000 0.328 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.3545 0.5989 0.828 0.000 0.008 0.164
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.4212 0.5071 0.772 0.000 0.012 0.216
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.4452 0.7921 0.260 0.000 0.008 0.732
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.4564 0.6774 0.672 0.000 0.328 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 3 0.7171 0.9754 0.000 0.232 0.556 0.212
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.0000 0.7459 1.000 0.000 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.1706 0.7267 0.948 0.000 0.016 0.036
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.7459 1.000 0.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.4564 0.6774 0.672 0.000 0.328 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.4103 0.7922 0.256 0.000 0.000 0.744
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.0000 0.7459 1.000 0.000 0.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.4103 0.7922 0.256 0.000 0.000 0.744
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 3 0.7289 0.9824 0.000 0.252 0.536 0.212
#> 322AF320-1379-4F51-AFDC-5292A060CD52 4 0.5936 0.3344 0.044 0.188 0.044 0.724
#> 53A96249-66D5-4C26-893B-ADC71481D261 4 0.6959 0.5643 0.176 0.200 0.008 0.616
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.4564 0.6774 0.672 0.000 0.328 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 3 0.7331 0.9804 0.000 0.260 0.528 0.212
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.3444 0.7700 0.000 0.816 0.000 0.184
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.4564 0.6774 0.672 0.000 0.328 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.4564 0.6774 0.672 0.000 0.328 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.3672 0.5936 0.824 0.000 0.012 0.164
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.7459 1.000 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.0000 0.7459 1.000 0.000 0.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.4319 0.4987 0.760 0.000 0.012 0.228
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 2 0.4832 0.7638 0.000 0.768 0.056 0.176
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.4319 0.4987 0.760 0.000 0.012 0.228
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.2124 0.7019 0.924 0.000 0.008 0.068
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 3 0.7171 0.9754 0.000 0.232 0.556 0.212
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 4 0.4452 0.7921 0.260 0.000 0.008 0.732
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.4673 0.7805 0.292 0.000 0.008 0.700
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 4 0.4707 0.7567 0.204 0.036 0.000 0.760
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.3056 0.7260 0.000 0.888 0.072 0.040
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.6961 0.6073 0.496 0.000 0.388 0.116
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 4 0.4621 0.7772 0.284 0.000 0.008 0.708
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0188 0.7445 0.996 0.000 0.000 0.004
#> A60DC925-7343-496E-900D-0DD81D5C8123 4 0.5585 0.1340 0.000 0.204 0.084 0.712
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.7459 1.000 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.3056 0.7260 0.000 0.888 0.072 0.040
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 4 0.5320 0.6428 0.416 0.000 0.012 0.572
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.4134 0.7927 0.260 0.000 0.000 0.740
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.0000 0.7459 1.000 0.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.5730 0.6712 0.040 0.668 0.008 0.284
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.5478 0.6710 0.628 0.000 0.344 0.028
#> 2629FEE3-A203-4411-8A70-02A796C9505C 4 0.5828 0.6813 0.180 0.104 0.004 0.712
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 1 0.9266 0.1663 0.372 0.088 0.236 0.304
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.4564 0.6774 0.672 0.000 0.328 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.2542 0.6765 0.904 0.000 0.012 0.084
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.0000 0.7459 1.000 0.000 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.3056 0.7260 0.000 0.888 0.072 0.040
#> A608BCEB-2C27-4927-A308-E6975F641722 1 0.9266 0.1663 0.372 0.088 0.236 0.304
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.5755 0.6693 0.040 0.664 0.008 0.288
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.0376 0.7440 0.992 0.000 0.004 0.004
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 4 0.4707 0.7567 0.204 0.036 0.000 0.760
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.0451 0.9111 0.004 0.000 0.988 0.000 0.008
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 5 0.3964 0.7708 0.000 0.032 0.160 0.012 0.796
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 5 0.6063 0.1470 0.016 0.076 0.000 0.416 0.492
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.1587 0.7686 0.952 0.012 0.020 0.008 0.008
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.4527 0.5703 0.012 0.000 0.392 0.000 0.596
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.4039 0.7305 0.720 0.004 0.268 0.000 0.008
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.4039 0.7305 0.720 0.004 0.268 0.000 0.008
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 5 0.3964 0.7708 0.000 0.032 0.160 0.012 0.796
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.9138 0.000 0.000 1.000 0.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 3 0.0000 0.9138 0.000 0.000 1.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 2 0.2338 0.7550 0.004 0.884 0.000 0.000 0.112
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.1587 0.7686 0.952 0.012 0.020 0.008 0.008
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 5 0.4159 0.7447 0.008 0.000 0.268 0.008 0.716
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.2966 0.8401 0.816 0.000 0.184 0.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.9138 0.000 0.000 1.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 1 0.4931 0.2787 0.600 0.012 0.000 0.372 0.016
#> DC55EE78-203F-4092-9B83-14B1A529194B 4 0.2411 0.9329 0.008 0.108 0.000 0.884 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 5 0.5406 0.2573 0.008 0.072 0.000 0.280 0.640
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.5532 0.6429 0.000 0.636 0.004 0.100 0.260
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 5 0.3210 0.7932 0.000 0.000 0.212 0.000 0.788
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 4 0.2127 0.9315 0.000 0.108 0.000 0.892 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.2338 0.7550 0.004 0.884 0.000 0.000 0.112
#> F325847E-F046-4B67-B01C-16919C401020 1 0.3231 0.8384 0.800 0.000 0.196 0.000 0.004
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0290 0.9083 0.008 0.000 0.992 0.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.3039 0.7317 0.000 0.808 0.000 0.192 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 5 0.3210 0.7932 0.000 0.000 0.212 0.000 0.788
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 5 0.4213 0.6997 0.012 0.000 0.308 0.000 0.680
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.2424 0.8410 0.868 0.000 0.132 0.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.0404 0.9066 0.012 0.000 0.988 0.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.3231 0.8384 0.800 0.000 0.196 0.000 0.004
#> A8E48877-F8AB-44DD-A18B-194D87C44931 5 0.3964 0.7708 0.000 0.032 0.160 0.012 0.796
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 5 0.3643 0.7902 0.004 0.008 0.212 0.000 0.776
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.3532 0.7696 0.000 0.832 0.000 0.092 0.076
#> 91BA5F90-9174-4533-A050-39A28E34A94D 3 0.0000 0.9138 0.000 0.000 1.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.6431 0.0515 0.012 0.160 0.000 0.284 0.544
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 3 0.0162 0.9133 0.000 0.000 0.996 0.000 0.004
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 5 0.3487 0.7917 0.008 0.000 0.212 0.000 0.780
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.3391 0.7411 0.012 0.000 0.800 0.000 0.188
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.3391 0.7440 0.012 0.000 0.800 0.000 0.188
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 5 0.7125 0.1859 0.268 0.012 0.008 0.248 0.464
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0324 0.9121 0.004 0.000 0.992 0.000 0.004
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 5 0.3487 0.7917 0.008 0.000 0.212 0.000 0.780
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 5 0.4213 0.6997 0.012 0.000 0.308 0.000 0.680
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 5 0.3210 0.7932 0.000 0.000 0.212 0.000 0.788
#> CB207A52-09AC-49D3-8240-5840CDFBB154 3 0.2361 0.8481 0.012 0.000 0.892 0.000 0.096
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.2424 0.8410 0.868 0.000 0.132 0.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 5 0.3964 0.7708 0.000 0.032 0.160 0.012 0.796
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 2 0.3751 0.7134 0.000 0.772 0.004 0.012 0.212
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.3890 0.6302 0.012 0.000 0.736 0.000 0.252
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.3659 0.7846 0.000 0.012 0.220 0.000 0.768
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.2424 0.8410 0.868 0.000 0.132 0.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 5 0.5549 0.1596 0.008 0.068 0.000 0.324 0.600
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.2424 0.8410 0.868 0.000 0.132 0.000 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.0324 0.9123 0.004 0.000 0.992 0.000 0.004
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.1587 0.7686 0.952 0.012 0.020 0.008 0.008
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 3 0.0000 0.9138 0.000 0.000 1.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.2424 0.8410 0.868 0.000 0.132 0.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 4 0.0798 0.8976 0.016 0.008 0.000 0.976 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 5 0.4632 0.4659 0.012 0.000 0.448 0.000 0.540
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.0324 0.9123 0.004 0.000 0.992 0.000 0.004
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 3 0.1830 0.8690 0.008 0.000 0.924 0.000 0.068
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 5 0.5406 0.2573 0.008 0.072 0.000 0.280 0.640
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 4 0.2127 0.9315 0.000 0.108 0.000 0.892 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 5 0.3487 0.7917 0.008 0.000 0.212 0.000 0.780
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.4557 0.5491 0.012 0.000 0.404 0.000 0.584
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.3039 0.7317 0.000 0.808 0.000 0.192 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.2400 0.8029 0.908 0.008 0.068 0.008 0.008
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.0000 0.9138 0.000 0.000 1.000 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 5 0.3210 0.7932 0.000 0.000 0.212 0.000 0.788
#> EE16D845-31F2-4178-800B-CA2C358841AD 1 0.4931 0.2787 0.600 0.012 0.000 0.372 0.016
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.3318 0.7308 0.000 0.800 0.000 0.192 0.008
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 3 0.0000 0.9138 0.000 0.000 1.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.3231 0.8384 0.800 0.000 0.196 0.000 0.004
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.3246 0.7511 0.008 0.000 0.808 0.000 0.184
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.3779 0.6604 0.012 0.000 0.752 0.000 0.236
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 5 0.3487 0.7917 0.008 0.000 0.212 0.000 0.780
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.3231 0.8384 0.800 0.000 0.196 0.000 0.004
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 4 0.0798 0.8976 0.016 0.008 0.000 0.976 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.9138 0.000 0.000 1.000 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.2491 0.8564 0.068 0.000 0.896 0.000 0.036
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.0000 0.9138 0.000 0.000 1.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.3231 0.8384 0.800 0.000 0.196 0.000 0.004
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 5 0.3177 0.7939 0.000 0.000 0.208 0.000 0.792
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.9138 0.000 0.000 1.000 0.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 5 0.3177 0.7939 0.000 0.000 0.208 0.000 0.792
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 4 0.2411 0.9329 0.008 0.108 0.000 0.884 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 5 0.6208 0.3538 0.016 0.112 0.004 0.276 0.592
#> 53A96249-66D5-4C26-893B-ADC71481D261 5 0.6737 0.5905 0.000 0.144 0.136 0.104 0.616
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.3231 0.8384 0.800 0.000 0.196 0.000 0.004
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 4 0.2127 0.9315 0.000 0.108 0.000 0.892 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.4262 0.7606 0.000 0.776 0.000 0.100 0.124
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.3231 0.8384 0.800 0.000 0.196 0.000 0.004
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.3231 0.8384 0.800 0.000 0.196 0.000 0.004
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.3355 0.7456 0.012 0.000 0.804 0.000 0.184
#> C2662596-6E2F-4924-B051-CEA1AC87B197 3 0.0000 0.9138 0.000 0.000 1.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.0000 0.9138 0.000 0.000 1.000 0.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 3 0.3890 0.6302 0.012 0.000 0.736 0.000 0.252
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 2 0.2338 0.7550 0.004 0.884 0.000 0.000 0.112
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 3 0.3890 0.6302 0.012 0.000 0.736 0.000 0.252
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 3 0.1830 0.8690 0.008 0.000 0.924 0.000 0.068
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 4 0.0798 0.8976 0.016 0.008 0.000 0.976 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 5 0.3487 0.7917 0.008 0.000 0.212 0.000 0.780
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 5 0.3728 0.7737 0.008 0.000 0.244 0.000 0.748
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 5 0.3964 0.7708 0.000 0.032 0.160 0.012 0.796
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.3039 0.7317 0.000 0.808 0.000 0.192 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.1587 0.7686 0.952 0.012 0.020 0.008 0.008
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 5 0.3975 0.7698 0.008 0.000 0.240 0.008 0.744
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 3 0.0162 0.9133 0.000 0.000 0.996 0.000 0.004
#> A60DC925-7343-496E-900D-0DD81D5C8123 5 0.5483 0.2813 0.008 0.088 0.000 0.256 0.648
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 3 0.0000 0.9138 0.000 0.000 1.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.3039 0.7317 0.000 0.808 0.000 0.192 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.4527 0.5703 0.012 0.000 0.392 0.000 0.596
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 5 0.3210 0.7932 0.000 0.000 0.212 0.000 0.788
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.9138 0.000 0.000 1.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.5555 0.6448 0.000 0.636 0.004 0.104 0.256
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.2424 0.8410 0.868 0.000 0.132 0.000 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 5 0.5411 0.6995 0.000 0.100 0.140 0.040 0.720
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 1 0.4931 0.2787 0.600 0.012 0.000 0.372 0.016
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.3231 0.8384 0.800 0.000 0.196 0.000 0.004
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.2470 0.8406 0.012 0.000 0.884 0.000 0.104
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.9138 0.000 0.000 1.000 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.3039 0.7317 0.000 0.808 0.000 0.192 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 1 0.4931 0.2787 0.600 0.012 0.000 0.372 0.016
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.5532 0.6429 0.000 0.636 0.004 0.100 0.260
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.0324 0.9123 0.004 0.000 0.992 0.000 0.004
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 5 0.3964 0.7708 0.000 0.032 0.160 0.012 0.796
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.0405 0.903 0.000 0.004 0.988 0.008 0.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 4 0.2375 0.820 0.000 0.088 0.012 0.888 0.012 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 4 0.6227 0.168 0.088 0.068 0.000 0.484 0.360 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 6 0.0862 0.733 0.016 0.008 0.000 0.000 0.004 0.972
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 4 0.3463 0.678 0.000 0.004 0.240 0.748 0.000 0.008
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 6 0.3591 0.703 0.004 0.000 0.256 0.004 0.004 0.732
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 6 0.3591 0.703 0.004 0.000 0.256 0.004 0.004 0.732
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 4 0.2375 0.820 0.000 0.088 0.012 0.888 0.012 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.905 0.000 0.000 1.000 0.000 0.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 3 0.0000 0.905 0.000 0.000 1.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 2 0.0806 0.732 0.008 0.972 0.000 0.020 0.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 6 0.0862 0.733 0.016 0.008 0.000 0.000 0.004 0.972
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 4 0.2674 0.803 0.000 0.008 0.108 0.868 0.008 0.008
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 6 0.3312 0.806 0.028 0.000 0.180 0.000 0.000 0.792
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.905 0.000 0.000 1.000 0.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 6 0.5574 0.275 0.116 0.008 0.000 0.008 0.284 0.584
#> DC55EE78-203F-4092-9B83-14B1A529194B 5 0.0603 0.883 0.004 0.016 0.000 0.000 0.980 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.1950 0.926 0.912 0.000 0.000 0.064 0.024 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.4992 0.621 0.012 0.668 0.000 0.208 0.112 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0458 0.847 0.000 0.000 0.016 0.984 0.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 5 0.0717 0.881 0.008 0.016 0.000 0.000 0.976 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.0806 0.732 0.008 0.972 0.000 0.020 0.000 0.000
#> F325847E-F046-4B67-B01C-16919C401020 6 0.3722 0.802 0.036 0.000 0.196 0.004 0.000 0.764
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0260 0.899 0.000 0.000 0.992 0.000 0.000 0.008
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.3534 0.746 0.008 0.716 0.000 0.000 0.276 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0458 0.847 0.000 0.000 0.016 0.984 0.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 4 0.2613 0.776 0.000 0.000 0.140 0.848 0.000 0.012
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 6 0.1957 0.809 0.000 0.000 0.112 0.000 0.000 0.888
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.0363 0.898 0.000 0.000 0.988 0.000 0.000 0.012
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 6 0.3722 0.802 0.036 0.000 0.196 0.004 0.000 0.764
#> A8E48877-F8AB-44DD-A18B-194D87C44931 4 0.2375 0.820 0.000 0.088 0.012 0.888 0.012 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 4 0.1788 0.845 0.000 0.028 0.040 0.928 0.000 0.004
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.2494 0.773 0.000 0.864 0.000 0.016 0.120 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 3 0.0000 0.905 0.000 0.000 1.000 0.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.3904 0.822 0.792 0.132 0.000 0.032 0.044 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 3 0.0146 0.905 0.000 0.000 0.996 0.004 0.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0748 0.847 0.000 0.004 0.016 0.976 0.000 0.004
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.3301 0.753 0.000 0.004 0.772 0.216 0.000 0.008
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.3583 0.697 0.000 0.004 0.728 0.260 0.000 0.008
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 4 0.7217 0.175 0.072 0.008 0.008 0.460 0.200 0.252
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0291 0.903 0.000 0.000 0.992 0.004 0.000 0.004
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0748 0.847 0.000 0.004 0.016 0.976 0.000 0.004
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 4 0.2613 0.776 0.000 0.000 0.140 0.848 0.000 0.012
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0458 0.847 0.000 0.000 0.016 0.984 0.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 3 0.2920 0.797 0.000 0.004 0.820 0.168 0.000 0.008
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 6 0.1957 0.809 0.000 0.000 0.112 0.000 0.000 0.888
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 4 0.2375 0.820 0.000 0.088 0.012 0.888 0.012 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 2 0.2877 0.687 0.000 0.820 0.000 0.168 0.012 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.3693 0.663 0.000 0.004 0.708 0.280 0.000 0.008
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 4 0.2328 0.836 0.000 0.052 0.056 0.892 0.000 0.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 6 0.1957 0.809 0.000 0.000 0.112 0.000 0.000 0.888
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.1989 0.895 0.916 0.004 0.000 0.028 0.052 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 6 0.1957 0.809 0.000 0.000 0.112 0.000 0.000 0.888
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.0291 0.904 0.000 0.004 0.992 0.004 0.000 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 6 0.0862 0.733 0.016 0.008 0.000 0.000 0.004 0.972
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 3 0.0000 0.905 0.000 0.000 1.000 0.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 6 0.1957 0.809 0.000 0.000 0.112 0.000 0.000 0.888
#> A54731AE-FC40-407F-8D10-67DDC122237D 5 0.2631 0.788 0.180 0.000 0.000 0.000 0.820 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 4 0.3772 0.611 0.000 0.004 0.296 0.692 0.000 0.008
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.0291 0.904 0.000 0.004 0.992 0.004 0.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 3 0.1674 0.866 0.000 0.004 0.924 0.068 0.000 0.004
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.1950 0.926 0.912 0.000 0.000 0.064 0.024 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 5 0.0717 0.881 0.008 0.016 0.000 0.000 0.976 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 4 0.0748 0.847 0.000 0.004 0.016 0.976 0.000 0.004
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 4 0.3536 0.664 0.000 0.004 0.252 0.736 0.000 0.008
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.3534 0.746 0.008 0.716 0.000 0.000 0.276 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 6 0.1836 0.769 0.012 0.008 0.048 0.000 0.004 0.928
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.0000 0.905 0.000 0.000 1.000 0.000 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0458 0.847 0.000 0.000 0.016 0.984 0.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 6 0.5574 0.275 0.116 0.008 0.000 0.008 0.284 0.584
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.3779 0.745 0.008 0.708 0.000 0.008 0.276 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 3 0.0000 0.905 0.000 0.000 1.000 0.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 6 0.3722 0.802 0.036 0.000 0.196 0.004 0.000 0.764
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.3163 0.760 0.000 0.004 0.780 0.212 0.000 0.004
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.3606 0.689 0.000 0.004 0.724 0.264 0.000 0.008
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0748 0.847 0.000 0.004 0.016 0.976 0.000 0.004
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 6 0.3722 0.802 0.036 0.000 0.196 0.004 0.000 0.764
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 5 0.2631 0.788 0.180 0.000 0.000 0.000 0.820 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.905 0.000 0.000 1.000 0.000 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.2308 0.850 0.000 0.000 0.892 0.040 0.000 0.068
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.0000 0.905 0.000 0.000 1.000 0.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 6 0.3722 0.802 0.036 0.000 0.196 0.004 0.000 0.764
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.0717 0.847 0.000 0.008 0.016 0.976 0.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.905 0.000 0.000 1.000 0.000 0.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0717 0.847 0.000 0.008 0.016 0.976 0.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 5 0.0603 0.883 0.004 0.016 0.000 0.000 0.980 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 4 0.6072 0.401 0.048 0.132 0.000 0.560 0.260 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 4 0.4912 0.615 0.012 0.172 0.004 0.696 0.116 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 6 0.3722 0.802 0.036 0.000 0.196 0.004 0.000 0.764
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 5 0.0717 0.881 0.008 0.016 0.000 0.000 0.976 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.3313 0.762 0.000 0.816 0.000 0.060 0.124 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 6 0.3722 0.802 0.036 0.000 0.196 0.004 0.000 0.764
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 6 0.3722 0.802 0.036 0.000 0.196 0.004 0.000 0.764
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.3273 0.756 0.000 0.004 0.776 0.212 0.000 0.008
#> C2662596-6E2F-4924-B051-CEA1AC87B197 3 0.0000 0.905 0.000 0.000 1.000 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.0000 0.905 0.000 0.000 1.000 0.000 0.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 3 0.3693 0.663 0.000 0.004 0.708 0.280 0.000 0.008
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 2 0.0806 0.732 0.008 0.972 0.000 0.020 0.000 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 3 0.3693 0.663 0.000 0.004 0.708 0.280 0.000 0.008
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 3 0.1674 0.866 0.000 0.004 0.924 0.068 0.000 0.004
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 5 0.2631 0.788 0.180 0.000 0.000 0.000 0.820 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 4 0.0748 0.847 0.000 0.004 0.016 0.976 0.000 0.004
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.1364 0.838 0.000 0.004 0.048 0.944 0.000 0.004
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 4 0.2375 0.820 0.000 0.088 0.012 0.888 0.012 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.3534 0.746 0.008 0.716 0.000 0.000 0.276 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 6 0.0862 0.733 0.016 0.008 0.000 0.000 0.004 0.972
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 4 0.2686 0.822 0.000 0.024 0.080 0.880 0.008 0.008
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 3 0.0146 0.905 0.000 0.000 0.996 0.004 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 1 0.2555 0.917 0.888 0.032 0.000 0.064 0.016 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 3 0.0000 0.905 0.000 0.000 1.000 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.3534 0.746 0.008 0.716 0.000 0.000 0.276 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 4 0.3463 0.678 0.000 0.004 0.240 0.748 0.000 0.008
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0458 0.847 0.000 0.000 0.016 0.984 0.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.905 0.000 0.000 1.000 0.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.5005 0.624 0.012 0.668 0.000 0.204 0.116 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 6 0.1957 0.809 0.000 0.000 0.112 0.000 0.000 0.888
#> 2629FEE3-A203-4411-8A70-02A796C9505C 4 0.3304 0.748 0.000 0.140 0.004 0.816 0.040 0.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 6 0.5574 0.275 0.116 0.008 0.000 0.008 0.284 0.584
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 6 0.3722 0.802 0.036 0.000 0.196 0.004 0.000 0.764
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.2989 0.791 0.000 0.004 0.812 0.176 0.000 0.008
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.905 0.000 0.000 1.000 0.000 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.3534 0.746 0.008 0.716 0.000 0.000 0.276 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 6 0.5574 0.275 0.116 0.008 0.000 0.008 0.284 0.584
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.4992 0.621 0.012 0.668 0.000 0.208 0.112 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.0291 0.904 0.000 0.004 0.992 0.004 0.000 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 4 0.2375 0.820 0.000 0.088 0.012 0.888 0.012 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "kmeans"]
# you can also extract it by
# res = res_list["ATC:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.612 0.897 0.929 0.4727 0.514 0.514
#> 3 3 0.544 0.671 0.790 0.3305 0.712 0.511
#> 4 4 0.567 0.422 0.626 0.1380 0.755 0.447
#> 5 5 0.754 0.759 0.828 0.0816 0.841 0.505
#> 6 6 0.774 0.776 0.839 0.0425 0.943 0.755
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.2603 0.950 0.956 0.044
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.3733 0.915 0.072 0.928
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.900 0.000 1.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.3733 0.905 0.928 0.072
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.2603 0.950 0.956 0.044
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.3733 0.905 0.928 0.072
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.3733 0.905 0.928 0.072
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.3733 0.915 0.072 0.928
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.2603 0.950 0.956 0.044
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.2603 0.950 0.956 0.044
#> A31D342D-C67C-428B-BAED-C6E844277A09 2 0.3733 0.915 0.072 0.928
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.3733 0.905 0.928 0.072
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.8267 0.728 0.260 0.740
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.3733 0.905 0.928 0.072
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.2603 0.950 0.956 0.044
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.2603 0.871 0.044 0.956
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0376 0.901 0.004 0.996
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 2 0.0000 0.900 0.000 1.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.3733 0.915 0.072 0.928
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 2 0.9286 0.590 0.344 0.656
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0376 0.901 0.004 0.996
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.3733 0.915 0.072 0.928
#> F325847E-F046-4B67-B01C-16919C401020 1 0.3733 0.905 0.928 0.072
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.0000 0.934 1.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.3733 0.915 0.072 0.928
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 2 0.9286 0.590 0.344 0.656
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 1 0.2778 0.947 0.952 0.048
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.3733 0.905 0.928 0.072
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.0000 0.934 1.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.3733 0.905 0.928 0.072
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.8081 0.745 0.248 0.752
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 2 0.9129 0.620 0.328 0.672
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.3733 0.915 0.072 0.928
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.2603 0.950 0.956 0.044
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 2 0.3733 0.915 0.072 0.928
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.2603 0.950 0.956 0.044
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.2778 0.947 0.952 0.048
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.2603 0.950 0.956 0.044
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 1 0.2603 0.950 0.956 0.044
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.9954 0.175 0.460 0.540
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.2603 0.950 0.956 0.044
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.2778 0.949 0.952 0.048
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 1 0.2778 0.947 0.952 0.048
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.2603 0.950 0.956 0.044
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.0000 0.934 1.000 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.3733 0.905 0.928 0.072
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.0376 0.901 0.004 0.996
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 2 0.3733 0.915 0.072 0.928
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.2603 0.950 0.956 0.044
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.4562 0.900 0.096 0.904
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.3733 0.905 0.928 0.072
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 2 0.0376 0.901 0.004 0.996
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.3733 0.905 0.928 0.072
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.0000 0.934 1.000 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.3733 0.905 0.928 0.072
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.2603 0.950 0.956 0.044
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.3733 0.905 0.928 0.072
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.900 0.000 1.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.2603 0.950 0.956 0.044
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.2603 0.950 0.956 0.044
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.2603 0.950 0.956 0.044
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 2 0.2603 0.871 0.044 0.956
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.900 0.000 1.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.2603 0.950 0.956 0.044
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.2603 0.950 0.956 0.044
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.3733 0.915 0.072 0.928
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.3733 0.905 0.928 0.072
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.2603 0.950 0.956 0.044
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 2 0.3733 0.915 0.072 0.928
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.2603 0.871 0.044 0.956
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.3733 0.915 0.072 0.928
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.2603 0.950 0.956 0.044
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.3733 0.905 0.928 0.072
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.2603 0.950 0.956 0.044
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.2603 0.950 0.956 0.044
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.2603 0.950 0.956 0.044
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.3733 0.905 0.928 0.072
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.1633 0.885 0.024 0.976
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.2603 0.950 0.956 0.044
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.2603 0.950 0.956 0.044
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.2603 0.950 0.956 0.044
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.3733 0.905 0.928 0.072
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.3733 0.915 0.072 0.928
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.2603 0.950 0.956 0.044
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.2603 0.950 0.956 0.044
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.900 0.000 1.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.2603 0.910 0.044 0.956
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.3733 0.915 0.072 0.928
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.3733 0.905 0.928 0.072
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.900 0.000 1.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.3733 0.915 0.072 0.928
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.3733 0.905 0.928 0.072
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.3733 0.905 0.928 0.072
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.2603 0.950 0.956 0.044
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.2603 0.950 0.956 0.044
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.2603 0.950 0.956 0.044
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.2603 0.950 0.956 0.044
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 2 0.3733 0.915 0.072 0.928
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.2603 0.950 0.956 0.044
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.2603 0.950 0.956 0.044
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.2603 0.871 0.044 0.956
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.2603 0.950 0.956 0.044
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.2603 0.950 0.956 0.044
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.3733 0.915 0.072 0.928
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.3733 0.915 0.072 0.928
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.3733 0.905 0.928 0.072
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 1 0.9661 0.281 0.608 0.392
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.2603 0.950 0.956 0.044
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.0376 0.901 0.004 0.996
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.2603 0.950 0.956 0.044
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.3733 0.915 0.072 0.928
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.2603 0.950 0.956 0.044
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 2 0.9044 0.635 0.320 0.680
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.2603 0.950 0.956 0.044
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.3733 0.915 0.072 0.928
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.3733 0.905 0.928 0.072
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.3733 0.915 0.072 0.928
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.2603 0.871 0.044 0.956
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.3733 0.905 0.928 0.072
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.2603 0.950 0.956 0.044
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.2603 0.950 0.956 0.044
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.3733 0.915 0.072 0.928
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.2603 0.871 0.044 0.956
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.3733 0.915 0.072 0.928
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.2603 0.950 0.956 0.044
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.9358 0.573 0.352 0.648
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.6280 0.630 0.540 0.000 0.460
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 1 0.3425 0.417 0.884 0.112 0.004
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0424 0.777 0.008 0.992 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 3 0.8508 0.387 0.232 0.160 0.608
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.4002 0.642 0.840 0.000 0.160
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.6225 -0.432 0.432 0.000 0.568
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.0237 0.860 0.004 0.000 0.996
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.5178 0.807 0.256 0.744 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.6280 0.630 0.540 0.000 0.460
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.6280 0.630 0.540 0.000 0.460
#> A31D342D-C67C-428B-BAED-C6E844277A09 2 0.4931 0.805 0.232 0.768 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 3 0.8284 0.425 0.224 0.148 0.628
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 1 0.0475 0.599 0.992 0.004 0.004
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 3 0.0237 0.860 0.004 0.000 0.996
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.6280 0.630 0.540 0.000 0.460
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.9594 0.342 0.244 0.476 0.280
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0424 0.777 0.008 0.992 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 2 0.6045 0.741 0.380 0.620 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.6451 0.709 0.436 0.560 0.004
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.0424 0.602 0.992 0.008 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0237 0.777 0.004 0.996 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.4002 0.823 0.160 0.840 0.000
#> F325847E-F046-4B67-B01C-16919C401020 3 0.0237 0.860 0.004 0.000 0.996
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.6309 0.570 0.504 0.000 0.496
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.3816 0.824 0.148 0.852 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.0424 0.602 0.992 0.008 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 1 0.2165 0.623 0.936 0.000 0.064
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 3 0.0237 0.860 0.004 0.000 0.996
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.6302 0.598 0.520 0.000 0.480
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 3 0.0237 0.860 0.004 0.000 0.996
#> A8E48877-F8AB-44DD-A18B-194D87C44931 1 0.0475 0.599 0.992 0.004 0.004
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.0475 0.599 0.992 0.004 0.004
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.3879 0.824 0.152 0.848 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.6280 0.630 0.540 0.000 0.460
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 2 0.3941 0.824 0.156 0.844 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.6244 0.643 0.560 0.000 0.440
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.0661 0.607 0.988 0.004 0.008
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.4654 0.649 0.792 0.000 0.208
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 1 0.3941 0.641 0.844 0.000 0.156
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 1 0.9724 -0.150 0.452 0.268 0.280
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.6260 0.639 0.552 0.000 0.448
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.3412 0.639 0.876 0.000 0.124
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 1 0.0592 0.605 0.988 0.000 0.012
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.1529 0.619 0.960 0.000 0.040
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.6280 0.630 0.540 0.000 0.460
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.0237 0.860 0.004 0.000 0.996
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.6468 0.702 0.444 0.552 0.004
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 2 0.6045 0.746 0.380 0.620 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.6140 0.655 0.596 0.000 0.404
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 1 0.1129 0.585 0.976 0.020 0.004
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 3 0.0237 0.860 0.004 0.000 0.996
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 2 0.4062 0.824 0.164 0.836 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 3 0.0237 0.860 0.004 0.000 0.996
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.6280 0.630 0.540 0.000 0.460
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 3 0.4750 0.586 0.216 0.000 0.784
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.6280 0.630 0.540 0.000 0.460
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 3 0.0424 0.860 0.008 0.000 0.992
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0424 0.777 0.008 0.992 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.6168 0.654 0.588 0.000 0.412
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.6260 0.639 0.552 0.000 0.448
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.6168 0.654 0.588 0.000 0.412
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.9825 -0.138 0.244 0.368 0.388
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0237 0.777 0.004 0.996 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.3879 0.643 0.848 0.000 0.152
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.4842 0.648 0.776 0.000 0.224
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.3816 0.824 0.148 0.852 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 3 0.1031 0.839 0.024 0.000 0.976
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.6280 0.630 0.540 0.000 0.460
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 2 0.6267 0.695 0.452 0.548 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.9411 0.397 0.240 0.508 0.252
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.3816 0.824 0.148 0.852 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.6260 0.639 0.552 0.000 0.448
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 3 0.0237 0.860 0.004 0.000 0.996
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.6168 0.654 0.588 0.000 0.412
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.4002 0.644 0.840 0.000 0.160
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.1529 0.619 0.960 0.000 0.040
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 3 0.0237 0.860 0.004 0.000 0.996
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0424 0.777 0.008 0.992 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.6280 0.630 0.540 0.000 0.460
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.6267 0.636 0.548 0.000 0.452
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.6260 0.639 0.552 0.000 0.448
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 3 0.0237 0.860 0.004 0.000 0.996
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.6260 0.699 0.448 0.552 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.6280 0.630 0.540 0.000 0.460
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.0592 0.609 0.988 0.000 0.012
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0424 0.777 0.008 0.992 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.5365 0.707 0.252 0.744 0.004
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.4796 0.813 0.220 0.780 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 3 0.0237 0.860 0.004 0.000 0.996
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0424 0.777 0.008 0.992 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.3941 0.825 0.156 0.844 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.0237 0.860 0.004 0.000 0.996
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 3 0.0237 0.860 0.004 0.000 0.996
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.6140 0.655 0.596 0.000 0.404
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.6280 0.630 0.540 0.000 0.460
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.6280 0.630 0.540 0.000 0.460
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.0592 0.609 0.988 0.000 0.012
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 2 0.4796 0.811 0.220 0.780 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.0592 0.609 0.988 0.000 0.012
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.6180 0.653 0.584 0.000 0.416
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0661 0.777 0.008 0.988 0.004
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0592 0.609 0.988 0.000 0.012
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.6140 0.655 0.596 0.000 0.404
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 1 0.1129 0.585 0.976 0.020 0.004
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.3816 0.824 0.148 0.852 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 3 0.0237 0.860 0.004 0.000 0.996
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 1 0.0424 0.602 0.992 0.000 0.008
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.6244 0.643 0.560 0.000 0.440
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.6095 0.735 0.392 0.608 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.6280 0.630 0.540 0.000 0.460
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.3816 0.824 0.148 0.852 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.3941 0.643 0.844 0.000 0.156
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.0424 0.602 0.992 0.008 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.6280 0.630 0.540 0.000 0.460
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.3879 0.824 0.152 0.848 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 3 0.0237 0.860 0.004 0.000 0.996
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.4399 0.820 0.188 0.812 0.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.9411 0.397 0.240 0.508 0.252
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.0237 0.860 0.004 0.000 0.996
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.6168 0.654 0.588 0.000 0.412
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.6280 0.630 0.540 0.000 0.460
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.3816 0.824 0.148 0.852 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.9543 0.344 0.236 0.484 0.280
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.6244 0.708 0.440 0.560 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.6168 0.654 0.588 0.000 0.412
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 1 0.0475 0.599 0.992 0.004 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.7603 0.5377 0.436 0.000 0.360 0.204
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 3 0.3972 0.4795 0.080 0.080 0.840 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.1837 0.6982 0.028 0.944 0.028 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 4 0.5054 0.7190 0.028 0.088 0.084 0.800
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.1902 0.4896 0.064 0.000 0.932 0.004
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.7910 -0.4126 0.308 0.000 0.360 0.332
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 4 0.0336 0.9578 0.000 0.000 0.008 0.992
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 3 0.7250 -0.0913 0.316 0.168 0.516 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.7603 0.5377 0.436 0.000 0.360 0.204
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.7603 0.5377 0.436 0.000 0.360 0.204
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.6949 -0.3601 0.528 0.124 0.348 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 4 0.4786 0.7443 0.028 0.072 0.084 0.816
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.1004 0.5192 0.024 0.004 0.972 0.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 4 0.0000 0.9604 0.000 0.000 0.000 1.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.7603 0.5377 0.436 0.000 0.360 0.204
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.7160 0.4948 0.032 0.604 0.268 0.096
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.1022 0.7103 0.032 0.968 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.7512 -0.4573 0.496 0.236 0.268 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 3 0.6805 0.0214 0.148 0.260 0.592 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 3 0.4454 0.3980 0.308 0.000 0.692 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.1302 0.7108 0.044 0.956 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.7047 0.5658 0.440 0.440 0.120 0.000
#> F325847E-F046-4B67-B01C-16919C401020 4 0.0188 0.9597 0.004 0.000 0.000 0.996
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.7720 0.5150 0.412 0.000 0.360 0.228
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.5815 0.6362 0.428 0.540 0.032 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 3 0.4454 0.3980 0.308 0.000 0.692 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.1489 0.5066 0.044 0.004 0.952 0.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 4 0.0000 0.9604 0.000 0.000 0.000 1.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.7603 0.5377 0.436 0.000 0.360 0.204
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 4 0.0188 0.9597 0.004 0.000 0.000 0.996
#> A8E48877-F8AB-44DD-A18B-194D87C44931 3 0.1209 0.5301 0.032 0.004 0.964 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.3444 0.4873 0.184 0.000 0.816 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.7006 0.5755 0.428 0.456 0.116 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.7603 0.5377 0.436 0.000 0.360 0.204
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.6031 -0.6122 0.536 0.420 0.044 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.7466 0.4977 0.436 0.000 0.388 0.176
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 3 0.3400 0.5085 0.180 0.000 0.820 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.5738 -0.2010 0.432 0.000 0.540 0.028
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.1489 0.5066 0.044 0.004 0.952 0.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 3 0.6915 -0.2376 0.028 0.444 0.480 0.048
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.7469 0.4892 0.432 0.000 0.392 0.176
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 3 0.3311 0.5119 0.172 0.000 0.828 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.1489 0.5066 0.044 0.004 0.952 0.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 3 0.1940 0.5237 0.076 0.000 0.924 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.7625 0.5347 0.432 0.000 0.360 0.208
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 4 0.0188 0.9606 0.000 0.000 0.004 0.996
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 3 0.6860 0.0469 0.164 0.244 0.592 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.6235 -0.3106 0.524 0.056 0.420 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.6748 -0.3274 0.432 0.000 0.476 0.092
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.2197 0.5245 0.080 0.004 0.916 0.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 4 0.0188 0.9606 0.000 0.000 0.004 0.996
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.6599 -0.6048 0.488 0.432 0.080 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 4 0.0188 0.9606 0.000 0.000 0.004 0.996
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.7625 0.5347 0.432 0.000 0.360 0.208
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 4 0.3533 0.8083 0.024 0.008 0.104 0.864
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.7603 0.5377 0.436 0.000 0.360 0.204
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0188 0.9606 0.000 0.000 0.004 0.996
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0895 0.7052 0.004 0.976 0.020 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.6840 -0.3445 0.432 0.000 0.468 0.100
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.7550 0.5238 0.436 0.000 0.372 0.192
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 3 0.6840 -0.3445 0.432 0.000 0.468 0.100
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 2 0.8918 0.1905 0.072 0.404 0.188 0.336
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.1302 0.7108 0.044 0.956 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 3 0.2589 0.5181 0.116 0.000 0.884 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.5236 -0.1631 0.432 0.000 0.560 0.008
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.5659 0.6621 0.368 0.600 0.032 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 4 0.0817 0.9429 0.000 0.000 0.024 0.976
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.7603 0.5377 0.436 0.000 0.360 0.204
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 3 0.5288 0.0991 0.472 0.008 0.520 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.7111 0.5044 0.032 0.612 0.260 0.096
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.5742 0.6611 0.368 0.596 0.036 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.7550 0.5238 0.436 0.000 0.372 0.192
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 4 0.0000 0.9604 0.000 0.000 0.000 1.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.6842 -0.3558 0.436 0.000 0.464 0.100
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.5353 -0.1716 0.432 0.000 0.556 0.012
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 3 0.3311 0.5119 0.172 0.000 0.828 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 4 0.0188 0.9597 0.004 0.000 0.000 0.996
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0469 0.7059 0.000 0.988 0.012 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.7603 0.5377 0.436 0.000 0.360 0.204
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.7369 0.4559 0.432 0.000 0.408 0.160
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.7550 0.5238 0.436 0.000 0.372 0.192
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 4 0.0592 0.9474 0.016 0.000 0.000 0.984
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 3 0.5105 0.1545 0.432 0.004 0.564 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.7603 0.5377 0.436 0.000 0.360 0.204
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 3 0.0592 0.5254 0.016 0.000 0.984 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.1022 0.7103 0.032 0.968 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.5688 0.2467 0.024 0.512 0.464 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 1 0.7003 -0.3514 0.460 0.116 0.424 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 4 0.0000 0.9604 0.000 0.000 0.000 1.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.1022 0.7103 0.032 0.968 0.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.6733 0.6326 0.324 0.564 0.112 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 4 0.0188 0.9597 0.004 0.000 0.000 0.996
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 4 0.0188 0.9597 0.004 0.000 0.000 0.996
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.6748 -0.3274 0.432 0.000 0.476 0.092
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.7603 0.5377 0.436 0.000 0.360 0.204
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.7603 0.5377 0.436 0.000 0.360 0.204
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 3 0.1557 0.5022 0.056 0.000 0.944 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.7189 -0.3991 0.532 0.168 0.300 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 3 0.1637 0.4992 0.060 0.000 0.940 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 3 0.6928 -0.3746 0.436 0.000 0.456 0.108
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.1733 0.6983 0.024 0.948 0.028 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 3 0.3444 0.5084 0.184 0.000 0.816 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 3 0.6748 -0.3274 0.432 0.000 0.476 0.092
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 3 0.2266 0.5236 0.084 0.004 0.912 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.4855 0.6761 0.352 0.644 0.004 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 4 0.0336 0.9578 0.000 0.000 0.008 0.992
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0779 0.5296 0.016 0.004 0.980 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.7488 0.5038 0.436 0.000 0.384 0.180
#> A60DC925-7343-496E-900D-0DD81D5C8123 1 0.7363 -0.4014 0.492 0.176 0.332 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.7603 0.5377 0.436 0.000 0.360 0.204
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.5815 0.6362 0.428 0.540 0.032 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.3088 0.4147 0.128 0.000 0.864 0.008
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 3 0.4967 0.1732 0.452 0.000 0.548 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.7603 0.5377 0.436 0.000 0.360 0.204
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.4175 0.7070 0.200 0.784 0.016 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 4 0.0188 0.9606 0.000 0.000 0.004 0.996
#> 2629FEE3-A203-4411-8A70-02A796C9505C 1 0.7332 -0.3808 0.480 0.164 0.356 0.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.7111 0.5044 0.032 0.612 0.260 0.096
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 4 0.0188 0.9597 0.004 0.000 0.000 0.996
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.6926 -0.3630 0.432 0.000 0.460 0.108
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.7603 0.5377 0.436 0.000 0.360 0.204
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.5815 0.6362 0.428 0.540 0.032 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.7111 0.5044 0.032 0.612 0.260 0.096
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 3 0.6656 0.0941 0.256 0.136 0.608 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.6840 -0.3445 0.432 0.000 0.468 0.100
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.0524 0.5274 0.008 0.004 0.988 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.0912 0.9416 0.016 0.000 0.972 0.000 0.012
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 4 0.3955 0.7337 0.020 0.016 0.120 0.824 0.020
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.2888 0.7458 0.020 0.888 0.000 0.056 0.036
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.3521 0.7380 0.820 0.140 0.000 0.040 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 4 0.3652 0.7066 0.004 0.000 0.200 0.784 0.012
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.4070 0.6485 0.256 0.000 0.728 0.004 0.012
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.1341 0.9574 0.944 0.000 0.056 0.000 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 4 0.4617 0.5816 0.016 0.044 0.000 0.744 0.196
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0510 0.9435 0.016 0.000 0.984 0.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 3 0.0510 0.9435 0.016 0.000 0.984 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 5 0.1908 0.6231 0.000 0.000 0.000 0.092 0.908
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.2054 0.8563 0.920 0.052 0.000 0.028 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 4 0.3405 0.7357 0.004 0.008 0.136 0.836 0.016
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.2166 0.9604 0.912 0.004 0.072 0.012 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0510 0.9435 0.016 0.000 0.984 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.4761 0.6906 0.052 0.748 0.000 0.176 0.024
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.2516 0.6995 0.000 0.860 0.000 0.000 0.140
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 5 0.6047 0.4832 0.028 0.128 0.000 0.204 0.640
#> F772EA39-E408-4908-BADD-C786D702BF9B 4 0.4335 0.6382 0.020 0.044 0.004 0.792 0.140
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.4627 0.5823 0.032 0.000 0.008 0.704 0.256
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.2648 0.6866 0.000 0.848 0.000 0.000 0.152
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 5 0.3562 0.6276 0.000 0.196 0.000 0.016 0.788
#> F325847E-F046-4B67-B01C-16919C401020 1 0.2429 0.9598 0.904 0.004 0.072 0.016 0.004
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.2349 0.8952 0.084 0.000 0.900 0.004 0.012
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 5 0.3519 0.6182 0.000 0.216 0.000 0.008 0.776
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.4627 0.5823 0.032 0.000 0.008 0.704 0.256
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 4 0.3354 0.7340 0.004 0.004 0.152 0.828 0.012
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.1608 0.9592 0.928 0.000 0.072 0.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.1235 0.9399 0.016 0.004 0.964 0.004 0.012
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.2527 0.9584 0.900 0.004 0.072 0.020 0.004
#> A8E48877-F8AB-44DD-A18B-194D87C44931 4 0.3023 0.7401 0.004 0.008 0.132 0.852 0.004
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 4 0.2674 0.7031 0.008 0.000 0.020 0.888 0.084
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 5 0.3710 0.6284 0.000 0.192 0.000 0.024 0.784
#> 91BA5F90-9174-4533-A050-39A28E34A94D 3 0.0510 0.9435 0.016 0.000 0.984 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.2886 0.5897 0.004 0.016 0.000 0.116 0.864
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 3 0.0290 0.9423 0.008 0.000 0.992 0.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.4615 0.6153 0.032 0.000 0.020 0.736 0.212
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.3099 0.8343 0.008 0.000 0.848 0.132 0.012
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 4 0.3396 0.7315 0.004 0.004 0.156 0.824 0.012
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 4 0.5510 0.2954 0.040 0.352 0.000 0.588 0.020
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.1597 0.9247 0.000 0.000 0.940 0.048 0.012
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.4615 0.6153 0.032 0.000 0.020 0.736 0.212
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 4 0.3239 0.7327 0.004 0.000 0.156 0.828 0.012
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.4814 0.6620 0.032 0.000 0.076 0.764 0.128
#> CB207A52-09AC-49D3-8240-5840CDFBB154 3 0.1612 0.9370 0.016 0.000 0.948 0.024 0.012
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.1410 0.9592 0.940 0.000 0.060 0.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 4 0.2896 0.6778 0.020 0.052 0.004 0.892 0.032
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 5 0.4252 0.4713 0.020 0.000 0.000 0.280 0.700
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.1478 0.9149 0.000 0.000 0.936 0.064 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 4 0.3401 0.7392 0.004 0.004 0.116 0.844 0.032
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.1410 0.9592 0.940 0.000 0.060 0.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 5 0.5455 0.5109 0.020 0.132 0.000 0.148 0.700
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.1341 0.9574 0.944 0.000 0.056 0.000 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.1525 0.9326 0.036 0.000 0.948 0.004 0.012
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.2745 0.8825 0.896 0.028 0.024 0.052 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 3 0.0510 0.9435 0.016 0.000 0.984 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.1478 0.9597 0.936 0.000 0.064 0.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.2591 0.7530 0.020 0.904 0.000 0.032 0.044
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.1628 0.9191 0.000 0.000 0.936 0.056 0.008
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.0510 0.9435 0.016 0.000 0.984 0.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 3 0.1341 0.9207 0.000 0.000 0.944 0.056 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 2 0.6729 0.4622 0.284 0.556 0.000 0.060 0.100
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.2648 0.6866 0.000 0.848 0.000 0.000 0.152
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 4 0.4691 0.6238 0.032 0.000 0.028 0.740 0.200
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.4044 0.6503 0.004 0.000 0.732 0.252 0.012
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 5 0.4161 0.5612 0.000 0.280 0.000 0.016 0.704
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.1597 0.9384 0.940 0.000 0.048 0.012 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.0510 0.9435 0.016 0.000 0.984 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.4520 0.5479 0.032 0.000 0.000 0.684 0.284
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.4761 0.6906 0.052 0.748 0.000 0.176 0.024
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 5 0.4431 0.5975 0.000 0.216 0.000 0.052 0.732
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 3 0.0510 0.9435 0.016 0.000 0.984 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.2270 0.9601 0.908 0.004 0.072 0.016 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.1670 0.9201 0.000 0.000 0.936 0.052 0.012
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.4080 0.7028 0.016 0.000 0.760 0.212 0.012
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.4584 0.6168 0.032 0.000 0.020 0.740 0.208
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.2429 0.9598 0.904 0.004 0.072 0.016 0.004
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.1682 0.7445 0.004 0.940 0.000 0.012 0.044
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0510 0.9435 0.016 0.000 0.984 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.1597 0.9247 0.000 0.000 0.940 0.048 0.012
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.0510 0.9435 0.016 0.000 0.984 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.2589 0.9553 0.896 0.004 0.076 0.020 0.004
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.4086 0.6339 0.024 0.000 0.000 0.736 0.240
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0912 0.9416 0.016 0.000 0.972 0.000 0.012
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.3935 0.7385 0.016 0.000 0.140 0.808 0.036
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.2516 0.6995 0.000 0.860 0.000 0.000 0.140
#> 322AF320-1379-4F51-AFDC-5292A060CD52 4 0.5469 0.0694 0.020 0.428 0.000 0.524 0.028
#> 53A96249-66D5-4C26-893B-ADC71481D261 5 0.4900 0.1447 0.000 0.024 0.000 0.464 0.512
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.2270 0.9601 0.908 0.004 0.072 0.016 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.2516 0.6995 0.000 0.860 0.000 0.000 0.140
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 5 0.6659 0.4382 0.012 0.196 0.000 0.280 0.512
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.2429 0.9598 0.904 0.004 0.072 0.016 0.004
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.2429 0.9598 0.904 0.004 0.072 0.016 0.004
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.1942 0.9080 0.000 0.000 0.920 0.068 0.012
#> C2662596-6E2F-4924-B051-CEA1AC87B197 3 0.0510 0.9435 0.016 0.000 0.984 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.0510 0.9435 0.016 0.000 0.984 0.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 4 0.3519 0.7011 0.008 0.000 0.216 0.776 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.1410 0.6302 0.000 0.000 0.000 0.060 0.940
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 4 0.3612 0.6906 0.008 0.000 0.228 0.764 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 3 0.0880 0.9282 0.000 0.000 0.968 0.032 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.2086 0.7535 0.020 0.924 0.000 0.048 0.008
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 4 0.4646 0.6137 0.032 0.000 0.020 0.732 0.216
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 3 0.2124 0.8992 0.028 0.000 0.916 0.056 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 4 0.3608 0.7272 0.004 0.008 0.084 0.844 0.060
#> 2D962371-EC83-490C-A663-478AF383BC1B 5 0.4088 0.5327 0.000 0.304 0.000 0.008 0.688
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.1270 0.9547 0.948 0.000 0.052 0.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 4 0.3114 0.7392 0.004 0.004 0.140 0.844 0.008
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 3 0.0510 0.9435 0.016 0.000 0.984 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 5 0.6290 0.4591 0.032 0.124 0.000 0.236 0.608
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 3 0.0510 0.9435 0.016 0.000 0.984 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 5 0.3242 0.6188 0.000 0.216 0.000 0.000 0.784
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 4 0.3845 0.6863 0.004 0.000 0.224 0.760 0.012
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.4700 0.5676 0.032 0.000 0.008 0.692 0.268
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0510 0.9435 0.016 0.000 0.984 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 5 0.6777 0.1128 0.012 0.404 0.000 0.176 0.408
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.1410 0.9592 0.940 0.000 0.060 0.000 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 5 0.4746 0.3275 0.000 0.024 0.000 0.376 0.600
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.4761 0.6906 0.052 0.748 0.000 0.176 0.024
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.2429 0.9598 0.904 0.004 0.072 0.016 0.004
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.1740 0.9177 0.000 0.000 0.932 0.056 0.012
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0510 0.9435 0.016 0.000 0.984 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 5 0.3242 0.6188 0.000 0.216 0.000 0.000 0.784
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.4761 0.6906 0.052 0.748 0.000 0.176 0.024
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 4 0.4335 0.6382 0.020 0.044 0.004 0.792 0.140
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.1628 0.9191 0.000 0.000 0.936 0.056 0.008
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 4 0.3023 0.7401 0.004 0.008 0.132 0.852 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.0767 0.9034 0.004 0.000 0.976 0.008 0.000 0.012
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 6 0.2429 0.7895 0.000 0.064 0.028 0.008 0.004 0.896
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.2032 0.7623 0.000 0.920 0.000 0.020 0.024 0.036
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.4495 0.6508 0.716 0.208 0.000 0.056 0.000 0.020
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 6 0.2121 0.7497 0.000 0.000 0.096 0.012 0.000 0.892
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.4707 0.5793 0.284 0.004 0.660 0.028 0.000 0.024
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.0810 0.9251 0.976 0.008 0.004 0.008 0.000 0.004
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 6 0.3649 0.7315 0.000 0.112 0.000 0.004 0.084 0.800
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0291 0.9041 0.004 0.000 0.992 0.004 0.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 3 0.1554 0.9012 0.004 0.004 0.940 0.044 0.008 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 5 0.4508 0.5322 0.000 0.012 0.000 0.280 0.668 0.040
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.3343 0.8006 0.824 0.128 0.000 0.032 0.000 0.016
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 6 0.2328 0.7945 0.000 0.052 0.056 0.000 0.000 0.892
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.1994 0.9241 0.920 0.008 0.016 0.052 0.000 0.004
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0405 0.9041 0.004 0.000 0.988 0.008 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.3231 0.7367 0.024 0.848 0.000 0.052 0.000 0.076
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.3725 0.6717 0.000 0.676 0.000 0.000 0.316 0.008
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 4 0.3786 0.6049 0.004 0.072 0.000 0.812 0.092 0.020
#> F772EA39-E408-4908-BADD-C786D702BF9B 6 0.3286 0.7548 0.000 0.112 0.000 0.012 0.044 0.832
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.3464 0.7784 0.000 0.000 0.000 0.688 0.000 0.312
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.3668 0.6603 0.000 0.668 0.000 0.000 0.328 0.004
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 5 0.1682 0.8153 0.000 0.000 0.000 0.052 0.928 0.020
#> F325847E-F046-4B67-B01C-16919C401020 1 0.2930 0.9149 0.876 0.012 0.016 0.072 0.004 0.020
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.3393 0.8273 0.124 0.000 0.824 0.028 0.000 0.024
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 5 0.1353 0.8142 0.000 0.012 0.000 0.024 0.952 0.012
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.3464 0.7784 0.000 0.000 0.000 0.688 0.000 0.312
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 6 0.1812 0.7683 0.000 0.000 0.080 0.008 0.000 0.912
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0458 0.9248 0.984 0.000 0.016 0.000 0.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.1920 0.8959 0.008 0.004 0.932 0.024 0.008 0.024
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.2985 0.9122 0.872 0.012 0.016 0.076 0.004 0.020
#> A8E48877-F8AB-44DD-A18B-194D87C44931 6 0.2519 0.7941 0.000 0.056 0.048 0.008 0.000 0.888
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 6 0.2491 0.6577 0.000 0.000 0.000 0.164 0.000 0.836
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 5 0.1564 0.8177 0.000 0.000 0.000 0.040 0.936 0.024
#> 91BA5F90-9174-4533-A050-39A28E34A94D 3 0.1554 0.9012 0.004 0.004 0.940 0.044 0.008 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 4 0.4289 0.2807 0.004 0.024 0.000 0.632 0.340 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 3 0.1554 0.9012 0.004 0.004 0.940 0.044 0.008 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.3547 0.7677 0.000 0.000 0.000 0.668 0.000 0.332
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.3189 0.8118 0.000 0.000 0.796 0.020 0.000 0.184
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 6 0.1913 0.7661 0.000 0.000 0.080 0.012 0.000 0.908
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 6 0.5053 0.4495 0.016 0.356 0.000 0.052 0.000 0.576
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.2651 0.8733 0.000 0.000 0.860 0.028 0.000 0.112
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.3499 0.7757 0.000 0.000 0.000 0.680 0.000 0.320
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 6 0.1812 0.7680 0.000 0.000 0.080 0.008 0.000 0.912
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.4331 0.5161 0.000 0.000 0.020 0.516 0.000 0.464
#> CB207A52-09AC-49D3-8240-5840CDFBB154 3 0.2686 0.8803 0.008 0.000 0.868 0.024 0.000 0.100
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.0653 0.9215 0.980 0.004 0.004 0.012 0.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 6 0.3150 0.7424 0.000 0.120 0.000 0.052 0.000 0.828
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 4 0.4056 0.5658 0.004 0.016 0.000 0.756 0.192 0.032
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.3232 0.8877 0.000 0.004 0.844 0.056 0.008 0.088
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 6 0.2669 0.7936 0.000 0.056 0.036 0.008 0.012 0.888
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0748 0.9216 0.976 0.004 0.004 0.016 0.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 4 0.3743 0.5601 0.004 0.072 0.000 0.788 0.136 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.0436 0.9240 0.988 0.004 0.004 0.004 0.000 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.2747 0.8830 0.040 0.000 0.880 0.024 0.000 0.056
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.2645 0.8642 0.884 0.044 0.000 0.056 0.000 0.016
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 3 0.1554 0.9012 0.004 0.004 0.940 0.044 0.008 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0862 0.9221 0.972 0.004 0.008 0.016 0.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.2361 0.7570 0.000 0.884 0.000 0.000 0.088 0.028
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.2301 0.8861 0.000 0.000 0.884 0.020 0.000 0.096
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.1554 0.9012 0.004 0.004 0.940 0.044 0.008 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 3 0.3181 0.8892 0.000 0.004 0.848 0.056 0.008 0.084
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 2 0.5669 0.5078 0.188 0.600 0.000 0.192 0.000 0.020
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.3668 0.6603 0.000 0.668 0.000 0.000 0.328 0.004
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 4 0.3446 0.7805 0.000 0.000 0.000 0.692 0.000 0.308
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.4121 0.4795 0.000 0.000 0.604 0.016 0.000 0.380
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 5 0.1225 0.7898 0.000 0.036 0.000 0.000 0.952 0.012
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.1053 0.9169 0.964 0.012 0.004 0.020 0.000 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.1484 0.9016 0.004 0.004 0.944 0.040 0.008 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.3265 0.7819 0.000 0.000 0.000 0.748 0.004 0.248
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.3203 0.7351 0.020 0.848 0.000 0.052 0.000 0.080
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 5 0.1370 0.8014 0.000 0.012 0.000 0.004 0.948 0.036
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 3 0.1554 0.9012 0.004 0.004 0.940 0.044 0.008 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.2338 0.9213 0.900 0.012 0.016 0.068 0.000 0.004
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.2170 0.8832 0.000 0.000 0.888 0.012 0.000 0.100
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.3534 0.7351 0.000 0.000 0.740 0.016 0.000 0.244
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.3756 0.7429 0.000 0.000 0.004 0.644 0.000 0.352
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.2985 0.9136 0.872 0.012 0.016 0.076 0.004 0.020
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.2743 0.7422 0.000 0.828 0.000 0.000 0.164 0.008
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.1413 0.9019 0.004 0.004 0.948 0.036 0.008 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.2573 0.8742 0.000 0.000 0.864 0.024 0.000 0.112
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.1554 0.9012 0.004 0.004 0.940 0.044 0.008 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.2985 0.9122 0.872 0.012 0.016 0.076 0.004 0.020
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 6 0.3829 0.6966 0.000 0.036 0.000 0.112 0.048 0.804
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0551 0.9037 0.004 0.000 0.984 0.004 0.000 0.008
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 6 0.2331 0.7312 0.000 0.000 0.032 0.080 0.000 0.888
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.3725 0.6717 0.000 0.676 0.000 0.000 0.316 0.008
#> 322AF320-1379-4F51-AFDC-5292A060CD52 6 0.3710 0.6200 0.000 0.292 0.000 0.012 0.000 0.696
#> 53A96249-66D5-4C26-893B-ADC71481D261 6 0.5565 0.4635 0.000 0.100 0.000 0.028 0.280 0.592
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.2095 0.9237 0.916 0.012 0.016 0.052 0.000 0.004
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.3725 0.6717 0.000 0.676 0.000 0.000 0.316 0.008
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 5 0.5508 -0.1312 0.000 0.128 0.000 0.000 0.444 0.428
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.2985 0.9136 0.872 0.012 0.016 0.076 0.004 0.020
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.2930 0.9149 0.876 0.012 0.016 0.072 0.004 0.020
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.2450 0.8745 0.000 0.000 0.868 0.016 0.000 0.116
#> C2662596-6E2F-4924-B051-CEA1AC87B197 3 0.1554 0.9012 0.004 0.004 0.940 0.044 0.008 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.0291 0.9044 0.004 0.000 0.992 0.004 0.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 6 0.2361 0.7525 0.000 0.000 0.088 0.028 0.000 0.884
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.4215 0.5658 0.000 0.012 0.000 0.276 0.688 0.024
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 6 0.2679 0.7384 0.000 0.000 0.096 0.032 0.004 0.868
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 3 0.1621 0.9007 0.000 0.004 0.936 0.048 0.008 0.004
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.1401 0.7637 0.000 0.948 0.000 0.004 0.020 0.028
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 4 0.3464 0.7795 0.000 0.000 0.000 0.688 0.000 0.312
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 3 0.3904 0.8223 0.000 0.000 0.784 0.112 0.008 0.096
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 6 0.2701 0.7914 0.000 0.056 0.028 0.008 0.020 0.888
#> 2D962371-EC83-490C-A663-478AF383BC1B 5 0.1367 0.7765 0.000 0.044 0.000 0.000 0.944 0.012
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.0964 0.9176 0.968 0.012 0.004 0.016 0.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 6 0.1285 0.7802 0.000 0.000 0.052 0.004 0.000 0.944
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 3 0.1554 0.9012 0.004 0.004 0.940 0.044 0.008 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 4 0.3951 0.6318 0.004 0.068 0.000 0.808 0.080 0.040
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 3 0.1554 0.9012 0.004 0.004 0.940 0.044 0.008 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 5 0.1726 0.8182 0.000 0.012 0.000 0.044 0.932 0.012
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 6 0.2312 0.7312 0.000 0.000 0.112 0.012 0.000 0.876
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.3337 0.7840 0.000 0.000 0.000 0.736 0.004 0.260
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0291 0.9041 0.004 0.000 0.992 0.004 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 6 0.5803 0.0117 0.000 0.180 0.000 0.000 0.408 0.412
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.0436 0.9240 0.988 0.004 0.004 0.004 0.000 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 6 0.6236 0.3716 0.000 0.080 0.000 0.096 0.284 0.540
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.3231 0.7367 0.024 0.848 0.000 0.052 0.000 0.076
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.2930 0.9149 0.876 0.012 0.016 0.072 0.004 0.020
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.2357 0.8758 0.000 0.000 0.872 0.012 0.000 0.116
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0291 0.9041 0.004 0.000 0.992 0.004 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 5 0.1726 0.8182 0.000 0.012 0.000 0.044 0.932 0.012
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.3168 0.7380 0.024 0.852 0.000 0.048 0.000 0.076
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 6 0.3241 0.7567 0.000 0.108 0.000 0.012 0.044 0.836
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.2716 0.8878 0.000 0.000 0.868 0.028 0.008 0.096
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 6 0.2456 0.7942 0.000 0.052 0.048 0.008 0.000 0.892
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "skmeans"]
# you can also extract it by
# res = res_list["ATC:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.974 0.990 0.5007 0.499 0.499
#> 3 3 0.900 0.864 0.937 0.3164 0.768 0.566
#> 4 4 0.971 0.918 0.967 0.1283 0.855 0.606
#> 5 5 0.874 0.876 0.932 0.0602 0.899 0.640
#> 6 6 0.833 0.755 0.835 0.0394 0.948 0.766
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.0000 0.993 1.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.0000 0.985 0.000 1.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.985 0.000 1.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.4022 0.906 0.080 0.920
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.0000 0.993 1.000 0.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.0000 0.993 1.000 0.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.0000 0.993 1.000 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0000 0.985 0.000 1.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.0000 0.993 1.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.993 1.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 2 0.0000 0.985 0.000 1.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.0000 0.993 1.000 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.0000 0.985 0.000 1.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.993 1.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.0000 0.993 1.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.0000 0.985 0.000 1.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.985 0.000 1.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 2 0.0000 0.985 0.000 1.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.985 0.000 1.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 2 0.0000 0.985 0.000 1.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.985 0.000 1.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.0000 0.985 0.000 1.000
#> F325847E-F046-4B67-B01C-16919C401020 1 0.0000 0.993 1.000 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.0000 0.993 1.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.985 0.000 1.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 2 0.0000 0.985 0.000 1.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 2 0.5946 0.830 0.144 0.856
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0000 0.993 1.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.0000 0.993 1.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.993 1.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.0000 0.985 0.000 1.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 2 0.0000 0.985 0.000 1.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.985 0.000 1.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.993 1.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 2 0.0000 0.985 0.000 1.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.993 1.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 2 0.5178 0.865 0.116 0.884
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.0000 0.993 1.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 1 0.0000 0.993 1.000 0.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.985 0.000 1.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.0000 0.993 1.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.0000 0.993 1.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.0672 0.978 0.008 0.992
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.0000 0.993 1.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.0000 0.993 1.000 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.0000 0.993 1.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.0000 0.985 0.000 1.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 2 0.0000 0.985 0.000 1.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.0000 0.993 1.000 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.0000 0.985 0.000 1.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0000 0.993 1.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 2 0.0000 0.985 0.000 1.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.0000 0.993 1.000 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.0000 0.993 1.000 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.0000 0.993 1.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.993 1.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0000 0.993 1.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.985 0.000 1.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.0000 0.993 1.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0000 0.993 1.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.993 1.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 2 0.0000 0.985 0.000 1.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.985 0.000 1.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0376 0.990 0.996 0.004
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.0000 0.993 1.000 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.985 0.000 1.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.0000 0.993 1.000 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0000 0.993 1.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 2 0.0000 0.985 0.000 1.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.985 0.000 1.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.985 0.000 1.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.993 1.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.993 1.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.0000 0.993 1.000 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.0000 0.993 1.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.0000 0.993 1.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0000 0.993 1.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.985 0.000 1.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.0000 0.993 1.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.0000 0.993 1.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.993 1.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.993 1.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.0000 0.985 0.000 1.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.0000 0.993 1.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 2 0.0000 0.985 0.000 1.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.985 0.000 1.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0000 0.985 0.000 1.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0000 0.985 0.000 1.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.993 1.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.985 0.000 1.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.985 0.000 1.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0000 0.993 1.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0000 0.993 1.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.0000 0.993 1.000 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.993 1.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.0000 0.993 1.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.9988 0.069 0.480 0.520
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 2 0.0000 0.985 0.000 1.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.2423 0.953 0.960 0.040
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.993 1.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.985 0.000 1.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.9661 0.344 0.608 0.392
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.0000 0.993 1.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.0000 0.985 0.000 1.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.985 0.000 1.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.0000 0.993 1.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.0000 0.985 0.000 1.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.993 1.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.0000 0.985 0.000 1.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.993 1.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.985 0.000 1.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.0000 0.993 1.000 0.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 2 0.0000 0.985 0.000 1.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.0000 0.993 1.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.985 0.000 1.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.0000 0.993 1.000 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.0000 0.985 0.000 1.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0000 0.985 0.000 1.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.0000 0.993 1.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.0000 0.993 1.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.0000 0.993 1.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0000 0.985 0.000 1.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.985 0.000 1.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.985 0.000 1.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.0000 0.993 1.000 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.0000 0.985 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.0000 0.9430 0.000 0.000 1.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.0000 0.9434 0.000 1.000 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.9434 0.000 1.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.1643 0.8864 0.956 0.044 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.0000 0.9430 0.000 0.000 1.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.1643 0.9254 0.956 0.000 0.044
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.1643 0.9254 0.956 0.000 0.044
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0000 0.9434 0.000 1.000 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.9430 0.000 0.000 1.000
#> 5482053D-9F48-4773-B68A-302B3A612503 3 0.0000 0.9430 0.000 0.000 1.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 2 0.1643 0.9252 0.044 0.956 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.1765 0.9224 0.956 0.004 0.040
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.0000 0.9434 0.000 1.000 0.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.1643 0.9254 0.956 0.000 0.044
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.9430 0.000 0.000 1.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.6307 0.0596 0.488 0.512 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.9434 0.000 1.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 2 0.1289 0.9314 0.032 0.968 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.9434 0.000 1.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 2 0.1643 0.9252 0.044 0.956 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.9434 0.000 1.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.0237 0.9425 0.004 0.996 0.000
#> F325847E-F046-4B67-B01C-16919C401020 1 0.1643 0.9254 0.956 0.000 0.044
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.5216 0.6785 0.740 0.000 0.260
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0237 0.9425 0.004 0.996 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 2 0.1643 0.9252 0.044 0.956 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.6140 0.3458 0.000 0.404 0.596
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.1643 0.9254 0.956 0.000 0.044
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.6309 0.1390 0.500 0.000 0.500
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.1643 0.9254 0.956 0.000 0.044
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.0000 0.9434 0.000 1.000 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 2 0.1643 0.9252 0.044 0.956 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0237 0.9425 0.004 0.996 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 3 0.0000 0.9430 0.000 0.000 1.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 2 0.1289 0.9314 0.032 0.968 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 3 0.0000 0.9430 0.000 0.000 1.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 2 0.7442 0.3243 0.044 0.588 0.368
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0000 0.9430 0.000 0.000 1.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 1 0.6483 0.2910 0.544 0.004 0.452
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 1 0.5835 0.4482 0.660 0.340 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0000 0.9430 0.000 0.000 1.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.0000 0.8901 1.000 0.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.2261 0.8819 0.000 0.068 0.932
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 3 0.1643 0.9091 0.044 0.000 0.956
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.1964 0.9168 0.944 0.000 0.056
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.1643 0.9254 0.956 0.000 0.044
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.0000 0.9434 0.000 1.000 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 2 0.1643 0.9252 0.044 0.956 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.0000 0.9430 0.000 0.000 1.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.0000 0.9434 0.000 1.000 0.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.1643 0.9254 0.956 0.000 0.044
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 2 0.1289 0.9314 0.032 0.968 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.1643 0.9254 0.956 0.000 0.044
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.1643 0.9254 0.956 0.000 0.044
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.1643 0.9254 0.956 0.000 0.044
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 3 0.0000 0.9430 0.000 0.000 1.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.1643 0.9254 0.956 0.000 0.044
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.9434 0.000 1.000 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.0000 0.9430 0.000 0.000 1.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.0000 0.9430 0.000 0.000 1.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 3 0.0000 0.9430 0.000 0.000 1.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.4504 0.7092 0.804 0.196 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.9434 0.000 1.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.6209 0.3653 0.628 0.004 0.368
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.0000 0.9430 0.000 0.000 1.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.9434 0.000 1.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.1643 0.9254 0.956 0.000 0.044
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.0000 0.9430 0.000 0.000 1.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 2 0.1643 0.9252 0.044 0.956 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.6154 0.3115 0.408 0.592 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.9434 0.000 1.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 3 0.0000 0.9430 0.000 0.000 1.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.1643 0.9254 0.956 0.000 0.044
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0000 0.9430 0.000 0.000 1.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0000 0.9430 0.000 0.000 1.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 3 0.1643 0.9091 0.044 0.000 0.956
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.1643 0.9254 0.956 0.000 0.044
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.9434 0.000 1.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.9430 0.000 0.000 1.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.3686 0.7809 0.140 0.000 0.860
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.0000 0.9430 0.000 0.000 1.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.1643 0.9254 0.956 0.000 0.044
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.1529 0.9274 0.040 0.960 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.9430 0.000 0.000 1.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 3 0.7328 0.4177 0.044 0.344 0.612
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.9434 0.000 1.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0000 0.9434 0.000 1.000 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0000 0.9434 0.000 1.000 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.1643 0.9254 0.956 0.000 0.044
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.9434 0.000 1.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.9434 0.000 1.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.1643 0.9254 0.956 0.000 0.044
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.1643 0.9254 0.956 0.000 0.044
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.0000 0.9430 0.000 0.000 1.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 3 0.0000 0.9430 0.000 0.000 1.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.0000 0.9430 0.000 0.000 1.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 3 0.1832 0.9102 0.036 0.008 0.956
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 2 0.1643 0.9252 0.044 0.956 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 3 0.1643 0.9091 0.044 0.000 0.956
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 3 0.0000 0.9430 0.000 0.000 1.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.9434 0.000 1.000 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 3 0.8592 0.2999 0.108 0.360 0.532
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 3 0.0237 0.9401 0.004 0.000 0.996
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.0000 0.9434 0.000 1.000 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.9434 0.000 1.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.1643 0.9254 0.956 0.000 0.044
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.0000 0.9434 0.000 1.000 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 3 0.0000 0.9430 0.000 0.000 1.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.1289 0.9314 0.032 0.968 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 3 0.0000 0.9430 0.000 0.000 1.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0237 0.9425 0.004 0.996 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.0000 0.9430 0.000 0.000 1.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 3 0.7459 0.3477 0.044 0.372 0.584
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.9430 0.000 0.000 1.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.9434 0.000 1.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.1643 0.9254 0.956 0.000 0.044
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.0237 0.9425 0.004 0.996 0.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.6154 0.3115 0.408 0.592 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.1643 0.9254 0.956 0.000 0.044
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0000 0.9430 0.000 0.000 1.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.9430 0.000 0.000 1.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0237 0.9425 0.004 0.996 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.6260 0.1956 0.448 0.552 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.9434 0.000 1.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.0000 0.9430 0.000 0.000 1.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.0000 0.9434 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.0188 0.964 0.000 0.996 0.000 0.004
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0188 0.964 0.000 0.996 0.000 0.004
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 4 0.0592 0.906 0.000 0.016 0.000 0.984
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.0188 0.964 0.000 0.996 0.000 0.004
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 4 0.1211 0.897 0.000 0.040 0.000 0.960
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 0.907 0.000 0.000 0.000 1.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 4 0.4898 0.392 0.000 0.416 0.000 0.584
#> F325847E-F046-4B67-B01C-16919C401020 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.1557 0.910 0.000 0.944 0.000 0.056
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 0.907 0.000 0.000 0.000 1.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 2 0.4898 0.273 0.416 0.584 0.000 0.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.4454 0.544 0.308 0.000 0.692 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.0188 0.964 0.000 0.996 0.000 0.004
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 4 0.0336 0.907 0.000 0.008 0.000 0.992
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 4 0.5000 0.159 0.000 0.496 0.000 0.504
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 4 0.1022 0.900 0.000 0.032 0.000 0.968
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 0.907 0.000 0.000 0.000 1.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.4746 0.401 0.368 0.632 0.000 0.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.0469 0.968 0.988 0.000 0.012 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0188 0.904 0.000 0.000 0.004 0.996
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 1 0.4477 0.529 0.688 0.312 0.000 0.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 0.907 0.000 0.000 0.000 1.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 3 0.0188 0.974 0.004 0.000 0.996 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 4 0.0592 0.906 0.000 0.016 0.000 0.984
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.0188 0.964 0.000 0.996 0.000 0.004
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 4 0.1118 0.900 0.000 0.036 0.000 0.964
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.4281 0.736 0.000 0.180 0.792 0.028
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 4 0.0188 0.904 0.000 0.000 0.004 0.996
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0188 0.964 0.000 0.996 0.000 0.004
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 0.907 0.000 0.000 0.000 1.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0188 0.964 0.000 0.996 0.000 0.004
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 0.907 0.000 0.000 0.000 1.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.4679 0.445 0.648 0.000 0.352 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.1302 0.893 0.000 0.044 0.000 0.956
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0000 0.907 0.000 0.000 0.000 1.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0188 0.964 0.000 0.996 0.000 0.004
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0188 0.964 0.000 0.996 0.000 0.004
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 4 0.0592 0.906 0.000 0.016 0.000 0.984
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 4 0.0000 0.907 0.000 0.000 0.000 1.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.1118 0.947 0.964 0.000 0.000 0.036
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.0188 0.964 0.000 0.996 0.000 0.004
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0188 0.964 0.000 0.996 0.000 0.004
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.0188 0.964 0.000 0.996 0.000 0.004
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 4 0.0817 0.905 0.000 0.024 0.000 0.976
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 4 0.4898 0.392 0.000 0.416 0.000 0.584
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 0.907 0.000 0.000 0.000 1.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.2149 0.872 0.000 0.912 0.000 0.088
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.0000 0.978 0.000 0.000 1.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 4 0.4898 0.392 0.000 0.416 0.000 0.584
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0188 0.964 0.000 0.996 0.000 0.004
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.0000 0.979 1.000 0.000 0.000 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.0188 0.964 0.000 0.996 0.000 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 5 0.0609 0.859 0.000 0.020 0.000 0.000 0.980
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.948 0.000 1.000 0.000 0.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.2773 0.800 0.836 0.164 0.000 0.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.2020 0.885 0.000 0.000 0.900 0.000 0.100
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 5 0.2329 0.893 0.000 0.124 0.000 0.000 0.876
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 5 0.3912 0.693 0.000 0.020 0.000 0.228 0.752
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.0162 0.973 0.996 0.004 0.000 0.000 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 5 0.0510 0.860 0.000 0.016 0.000 0.000 0.984
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.0000 0.948 0.000 1.000 0.000 0.000 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0510 0.945 0.000 0.984 0.000 0.000 0.016
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 4 0.4905 0.241 0.000 0.476 0.000 0.500 0.024
#> F772EA39-E408-4908-BADD-C786D702BF9B 5 0.2377 0.891 0.000 0.128 0.000 0.000 0.872
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 0.828 0.000 0.000 0.000 1.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0794 0.937 0.000 0.972 0.000 0.000 0.028
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 5 0.2230 0.894 0.000 0.116 0.000 0.000 0.884
#> F325847E-F046-4B67-B01C-16919C401020 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.0162 0.972 0.996 0.000 0.004 0.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 5 0.2280 0.893 0.000 0.120 0.000 0.000 0.880
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 0.828 0.000 0.000 0.000 1.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 5 0.4251 0.470 0.000 0.012 0.316 0.000 0.672
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.3932 0.512 0.672 0.000 0.328 0.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 5 0.0510 0.860 0.000 0.016 0.000 0.000 0.984
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 4 0.3812 0.654 0.000 0.024 0.000 0.772 0.204
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 5 0.2280 0.893 0.000 0.120 0.000 0.000 0.880
#> 91BA5F90-9174-4533-A050-39A28E34A94D 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 4 0.5346 0.255 0.000 0.452 0.000 0.496 0.052
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 0.828 0.000 0.000 0.000 1.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.6813 0.222 0.040 0.488 0.356 0.000 0.116
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.948 0.000 1.000 0.000 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0510 0.958 0.016 0.000 0.984 0.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 0.828 0.000 0.000 0.000 1.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 5 0.2248 0.789 0.000 0.012 0.088 0.000 0.900
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 0.828 0.000 0.000 0.000 1.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.1043 0.934 0.960 0.000 0.040 0.000 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.0510 0.945 0.000 0.984 0.000 0.000 0.016
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 4 0.3929 0.649 0.000 0.028 0.000 0.764 0.208
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.1121 0.880 0.000 0.044 0.000 0.000 0.956
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 4 0.4905 0.241 0.000 0.476 0.000 0.500 0.024
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.0162 0.972 0.996 0.000 0.004 0.000 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0510 0.945 0.000 0.984 0.000 0.000 0.016
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 2 0.0579 0.938 0.008 0.984 0.000 0.000 0.008
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0880 0.934 0.000 0.968 0.000 0.000 0.032
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 4 0.0000 0.828 0.000 0.000 0.000 1.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.0290 0.967 0.000 0.000 0.992 0.000 0.008
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 5 0.2280 0.893 0.000 0.120 0.000 0.000 0.880
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 0.828 0.000 0.000 0.000 1.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.948 0.000 1.000 0.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 5 0.2280 0.893 0.000 0.120 0.000 0.000 0.880
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 0.828 0.000 0.000 0.000 1.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.948 0.000 1.000 0.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.4074 0.409 0.364 0.000 0.636 0.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 5 0.2574 0.836 0.000 0.012 0.000 0.112 0.876
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0794 0.813 0.000 0.000 0.000 0.972 0.028
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0510 0.945 0.000 0.984 0.000 0.000 0.016
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0290 0.947 0.000 0.992 0.000 0.000 0.008
#> 53A96249-66D5-4C26-893B-ADC71481D261 5 0.2127 0.894 0.000 0.108 0.000 0.000 0.892
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0510 0.945 0.000 0.984 0.000 0.000 0.016
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 5 0.2377 0.892 0.000 0.128 0.000 0.000 0.872
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 5 0.4015 0.404 0.000 0.000 0.348 0.000 0.652
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.2669 0.841 0.000 0.020 0.000 0.104 0.876
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 3 0.3561 0.679 0.000 0.000 0.740 0.000 0.260
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.948 0.000 1.000 0.000 0.000 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 4 0.0000 0.828 0.000 0.000 0.000 1.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.3210 0.636 0.000 0.000 0.212 0.788 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 5 0.0404 0.860 0.000 0.012 0.000 0.000 0.988
#> 2D962371-EC83-490C-A663-478AF383BC1B 5 0.2329 0.891 0.000 0.124 0.000 0.000 0.876
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 5 0.0404 0.860 0.000 0.012 0.000 0.000 0.988
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 4 0.4658 0.241 0.000 0.484 0.000 0.504 0.012
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 5 0.2488 0.890 0.000 0.124 0.000 0.004 0.872
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.2020 0.885 0.000 0.000 0.900 0.000 0.100
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 0.828 0.000 0.000 0.000 1.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 5 0.2471 0.890 0.000 0.136 0.000 0.000 0.864
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 5 0.2329 0.891 0.000 0.124 0.000 0.000 0.876
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0000 0.948 0.000 1.000 0.000 0.000 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 5 0.2329 0.891 0.000 0.124 0.000 0.000 0.876
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.948 0.000 1.000 0.000 0.000 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 5 0.2127 0.893 0.000 0.108 0.000 0.000 0.892
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.0000 0.973 0.000 0.000 1.000 0.000 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 5 0.0609 0.859 0.000 0.020 0.000 0.000 0.980
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.2823 0.8148 0.204 0.000 0.796 0.000 0.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 6 0.0405 0.6259 0.008 0.004 0.000 0.000 0.000 0.988
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.1814 0.9055 0.100 0.900 0.000 0.000 0.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 5 0.3965 0.4234 0.008 0.388 0.000 0.000 0.604 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.4871 0.4127 0.072 0.000 0.580 0.000 0.000 0.348
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 5 0.0000 0.9485 0.000 0.000 0.000 0.000 1.000 0.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 5 0.0260 0.9485 0.008 0.000 0.000 0.000 0.992 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 6 0.3748 0.3848 0.300 0.012 0.000 0.000 0.000 0.688
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.2912 0.8106 0.216 0.000 0.784 0.000 0.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 3 0.0000 0.8444 0.000 0.000 1.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.4274 0.6348 0.676 0.000 0.000 0.048 0.000 0.276
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 5 0.2257 0.8558 0.008 0.116 0.000 0.000 0.876 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 6 0.0405 0.6198 0.008 0.004 0.000 0.000 0.000 0.988
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 5 0.0260 0.9485 0.008 0.000 0.000 0.000 0.992 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.2912 0.8106 0.216 0.000 0.784 0.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.0000 0.9014 0.000 1.000 0.000 0.000 0.000 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.2346 0.8972 0.124 0.868 0.000 0.000 0.000 0.008
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.5597 0.3576 0.532 0.288 0.000 0.180 0.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 6 0.3821 0.4774 0.220 0.040 0.000 0.000 0.000 0.740
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 0.9771 0.000 0.000 0.000 1.000 0.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.2706 0.8597 0.160 0.832 0.000 0.000 0.000 0.008
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 1 0.3940 0.6328 0.640 0.012 0.000 0.000 0.000 0.348
#> F325847E-F046-4B67-B01C-16919C401020 5 0.0000 0.9485 0.000 0.000 0.000 0.000 1.000 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 5 0.2308 0.8469 0.040 0.000 0.068 0.000 0.892 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 1 0.3967 0.6256 0.632 0.012 0.000 0.000 0.000 0.356
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 0.9771 0.000 0.000 0.000 1.000 0.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 6 0.4405 0.4265 0.240 0.000 0.072 0.000 0.000 0.688
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 5 0.0260 0.9485 0.008 0.000 0.000 0.000 0.992 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 5 0.5680 0.0291 0.164 0.000 0.360 0.000 0.476 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 5 0.0000 0.9485 0.000 0.000 0.000 0.000 1.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 6 0.0405 0.6259 0.008 0.004 0.000 0.000 0.000 0.988
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.4736 0.4785 0.620 0.000 0.000 0.308 0.000 0.072
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 1 0.3967 0.6256 0.632 0.012 0.000 0.000 0.000 0.356
#> 91BA5F90-9174-4533-A050-39A28E34A94D 3 0.0458 0.8406 0.016 0.000 0.984 0.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.5131 0.5156 0.652 0.160 0.000 0.180 0.000 0.008
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 3 0.0146 0.8437 0.004 0.000 0.996 0.000 0.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 0.9771 0.000 0.000 0.000 1.000 0.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.2912 0.8106 0.216 0.000 0.784 0.000 0.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 6 0.5522 0.3934 0.236 0.064 0.068 0.000 0.000 0.632
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.9014 0.000 1.000 0.000 0.000 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.3720 0.7841 0.236 0.000 0.736 0.000 0.028 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 0.9771 0.000 0.000 0.000 1.000 0.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 6 0.3420 0.4676 0.240 0.000 0.012 0.000 0.000 0.748
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 0.9771 0.000 0.000 0.000 1.000 0.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.0632 0.9302 0.000 0.000 0.024 0.000 0.976 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 5 0.0260 0.9485 0.008 0.000 0.000 0.000 0.992 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.2257 0.9012 0.116 0.876 0.000 0.000 0.000 0.008
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.4476 0.5170 0.664 0.000 0.000 0.272 0.000 0.064
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.1588 0.8168 0.072 0.000 0.924 0.000 0.000 0.004
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 6 0.3528 0.4064 0.296 0.004 0.000 0.000 0.000 0.700
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 5 0.0260 0.9485 0.008 0.000 0.000 0.000 0.992 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.5597 0.3581 0.532 0.288 0.000 0.180 0.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 5 0.0260 0.9485 0.008 0.000 0.000 0.000 0.992 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 5 0.0260 0.9439 0.000 0.000 0.008 0.000 0.992 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 5 0.0405 0.9468 0.008 0.004 0.000 0.000 0.988 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 3 0.0000 0.8444 0.000 0.000 1.000 0.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 5 0.0260 0.9485 0.008 0.000 0.000 0.000 0.992 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.2257 0.9017 0.116 0.876 0.000 0.000 0.000 0.008
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.1204 0.8262 0.056 0.000 0.944 0.000 0.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.0000 0.8444 0.000 0.000 1.000 0.000 0.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 3 0.1588 0.8168 0.072 0.000 0.924 0.000 0.000 0.004
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 2 0.0458 0.8951 0.016 0.984 0.000 0.000 0.000 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.2706 0.8597 0.160 0.832 0.000 0.000 0.000 0.008
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 4 0.1327 0.9345 0.064 0.000 0.000 0.936 0.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.1983 0.8086 0.072 0.000 0.908 0.000 0.000 0.020
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 1 0.4015 0.6025 0.616 0.012 0.000 0.000 0.000 0.372
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 5 0.0260 0.9485 0.008 0.000 0.000 0.000 0.992 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.2562 0.8230 0.172 0.000 0.828 0.000 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 0.9771 0.000 0.000 0.000 1.000 0.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.9014 0.000 1.000 0.000 0.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 1 0.4015 0.6025 0.616 0.012 0.000 0.000 0.000 0.372
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 3 0.0000 0.8444 0.000 0.000 1.000 0.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 5 0.0000 0.9485 0.000 0.000 0.000 0.000 1.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.3050 0.8012 0.236 0.000 0.764 0.000 0.000 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.3298 0.7975 0.236 0.000 0.756 0.000 0.000 0.008
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 0.9771 0.000 0.000 0.000 1.000 0.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 5 0.0000 0.9485 0.000 0.000 0.000 0.000 1.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0547 0.9050 0.020 0.980 0.000 0.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.2883 0.8122 0.212 0.000 0.788 0.000 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.5801 0.4618 0.224 0.000 0.496 0.000 0.280 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.0000 0.8444 0.000 0.000 1.000 0.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 5 0.0000 0.9485 0.000 0.000 0.000 0.000 1.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 6 0.6125 -0.2101 0.340 0.000 0.000 0.312 0.000 0.348
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.3050 0.8012 0.236 0.000 0.764 0.000 0.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.1049 0.9487 0.032 0.000 0.000 0.960 0.000 0.008
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.2346 0.8972 0.124 0.868 0.000 0.000 0.000 0.008
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.2257 0.9017 0.116 0.876 0.000 0.000 0.000 0.008
#> 53A96249-66D5-4C26-893B-ADC71481D261 6 0.3965 0.1490 0.388 0.008 0.000 0.000 0.000 0.604
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 5 0.0000 0.9485 0.000 0.000 0.000 0.000 1.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.2346 0.8972 0.124 0.868 0.000 0.000 0.000 0.008
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 6 0.4052 0.2462 0.356 0.016 0.000 0.000 0.000 0.628
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 5 0.0000 0.9485 0.000 0.000 0.000 0.000 1.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 5 0.0000 0.9485 0.000 0.000 0.000 0.000 1.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.0790 0.8441 0.032 0.000 0.968 0.000 0.000 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 3 0.0260 0.8428 0.008 0.000 0.992 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.1714 0.8378 0.092 0.000 0.908 0.000 0.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 3 0.5387 0.1063 0.112 0.000 0.464 0.000 0.000 0.424
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.3952 0.6423 0.672 0.000 0.000 0.020 0.000 0.308
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 3 0.4608 0.5796 0.100 0.000 0.680 0.000 0.000 0.220
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 3 0.0790 0.8357 0.032 0.000 0.968 0.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.9014 0.000 1.000 0.000 0.000 0.000 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 4 0.1444 0.9271 0.072 0.000 0.000 0.928 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.1204 0.9201 0.000 0.000 0.056 0.944 0.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 6 0.0405 0.6259 0.008 0.004 0.000 0.000 0.000 0.988
#> 2D962371-EC83-490C-A663-478AF383BC1B 1 0.4015 0.6025 0.616 0.012 0.000 0.000 0.000 0.372
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 5 0.0260 0.9485 0.008 0.000 0.000 0.000 0.992 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 6 0.1219 0.6161 0.048 0.004 0.000 0.000 0.000 0.948
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 3 0.0000 0.8444 0.000 0.000 1.000 0.000 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 1 0.5716 0.3142 0.504 0.304 0.000 0.192 0.000 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 3 0.0146 0.8437 0.004 0.000 0.996 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 1 0.3921 0.6489 0.676 0.012 0.000 0.004 0.000 0.308
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.4516 0.5621 0.072 0.000 0.668 0.000 0.000 0.260
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 0.9771 0.000 0.000 0.000 1.000 0.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.2912 0.8106 0.216 0.000 0.784 0.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 6 0.4408 0.3040 0.320 0.044 0.000 0.000 0.000 0.636
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 5 0.0260 0.9485 0.008 0.000 0.000 0.000 0.992 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 1 0.3898 0.6405 0.652 0.012 0.000 0.000 0.000 0.336
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0000 0.9014 0.000 1.000 0.000 0.000 0.000 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 5 0.0000 0.9485 0.000 0.000 0.000 0.000 1.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.3050 0.8012 0.236 0.000 0.764 0.000 0.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.2912 0.8106 0.216 0.000 0.784 0.000 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 1 0.3972 0.6473 0.664 0.012 0.000 0.004 0.000 0.320
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.9014 0.000 1.000 0.000 0.000 0.000 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 6 0.3217 0.4963 0.224 0.008 0.000 0.000 0.000 0.768
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.1141 0.8279 0.052 0.000 0.948 0.000 0.000 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 6 0.0405 0.6259 0.008 0.004 0.000 0.000 0.000 0.988
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "pam"]
# you can also extract it by
# res = res_list["ATC:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.463 0.859 0.899 0.4448 0.521 0.521
#> 3 3 0.669 0.710 0.823 0.3606 0.809 0.659
#> 4 4 0.850 0.850 0.943 0.1611 0.859 0.666
#> 5 5 0.818 0.813 0.900 0.1097 0.858 0.565
#> 6 6 0.911 0.831 0.924 0.0393 0.961 0.827
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.7056 0.903 0.808 0.192
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.0000 0.937 0.000 1.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.937 0.000 1.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.7139 0.749 0.196 0.804
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 2 0.0000 0.937 0.000 1.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.7056 0.903 0.808 0.192
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 2 0.7745 0.727 0.228 0.772
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0000 0.937 0.000 1.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.7056 0.903 0.808 0.192
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.7056 0.903 0.808 0.192
#> A31D342D-C67C-428B-BAED-C6E844277A09 2 0.0000 0.937 0.000 1.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.7139 0.749 0.196 0.804
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.0000 0.937 0.000 1.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.778 1.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.7056 0.903 0.808 0.192
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.0376 0.934 0.004 0.996
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.937 0.000 1.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 2 0.0000 0.937 0.000 1.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.937 0.000 1.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 2 0.0000 0.937 0.000 1.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.937 0.000 1.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.0000 0.937 0.000 1.000
#> F325847E-F046-4B67-B01C-16919C401020 1 0.7299 0.621 0.796 0.204
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.7056 0.903 0.808 0.192
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.937 0.000 1.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 2 0.0000 0.937 0.000 1.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 2 0.0000 0.937 0.000 1.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.7528 0.611 0.784 0.216
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.7056 0.903 0.808 0.192
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.778 1.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.0000 0.937 0.000 1.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 2 0.0000 0.937 0.000 1.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.937 0.000 1.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.7056 0.903 0.808 0.192
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 2 0.0000 0.937 0.000 1.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.7056 0.903 0.808 0.192
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 2 0.0376 0.934 0.004 0.996
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.8267 0.835 0.740 0.260
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.0000 0.937 0.000 1.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0376 0.934 0.004 0.996
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.7056 0.903 0.808 0.192
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 2 0.0376 0.934 0.004 0.996
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.0000 0.937 0.000 1.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 2 0.8499 0.466 0.276 0.724
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.9286 0.713 0.656 0.344
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 2 0.9732 0.452 0.404 0.596
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.0000 0.937 0.000 1.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 2 0.0000 0.937 0.000 1.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.7139 0.901 0.804 0.196
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.0000 0.937 0.000 1.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 2 0.8081 0.707 0.248 0.752
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 2 0.0000 0.937 0.000 1.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 2 0.8267 0.695 0.260 0.740
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.7674 0.876 0.776 0.224
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.7139 0.749 0.196 0.804
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.7056 0.903 0.808 0.192
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.9998 -0.183 0.508 0.492
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.937 0.000 1.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.7139 0.901 0.804 0.196
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.7056 0.903 0.808 0.192
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.7139 0.901 0.804 0.196
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 2 0.7056 0.752 0.192 0.808
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.937 0.000 1.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 2 0.0000 0.937 0.000 1.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.9922 0.498 0.552 0.448
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.937 0.000 1.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 2 0.7056 0.752 0.192 0.808
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.7056 0.903 0.808 0.192
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 2 0.0000 0.937 0.000 1.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.937 0.000 1.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.937 0.000 1.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.7056 0.903 0.808 0.192
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.778 1.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.7056 0.903 0.808 0.192
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.7453 0.887 0.788 0.212
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 2 0.4431 0.838 0.092 0.908
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0000 0.778 1.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.937 0.000 1.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.7056 0.903 0.808 0.192
#> F3135F5E-2E90-4923-B634-E994563D17B7 2 0.7528 0.630 0.216 0.784
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.7056 0.903 0.808 0.192
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.778 1.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.0000 0.937 0.000 1.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.7056 0.903 0.808 0.192
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 2 0.0000 0.937 0.000 1.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.937 0.000 1.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0000 0.937 0.000 1.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0000 0.937 0.000 1.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.778 1.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.937 0.000 1.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.937 0.000 1.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0000 0.778 1.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.7219 0.627 0.800 0.200
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.7139 0.901 0.804 0.196
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.7056 0.903 0.808 0.192
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.7056 0.903 0.808 0.192
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.6887 0.685 0.184 0.816
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 2 0.0000 0.937 0.000 1.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 2 0.7528 0.620 0.216 0.784
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.7139 0.901 0.804 0.196
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.937 0.000 1.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 2 0.0000 0.937 0.000 1.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.7056 0.903 0.808 0.192
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.0000 0.937 0.000 1.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.937 0.000 1.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.7299 0.744 0.204 0.796
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.0000 0.937 0.000 1.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.7056 0.903 0.808 0.192
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.0000 0.937 0.000 1.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.7056 0.903 0.808 0.192
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.937 0.000 1.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 2 0.0000 0.937 0.000 1.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 2 0.6048 0.755 0.148 0.852
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.7056 0.903 0.808 0.192
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.937 0.000 1.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 2 0.9710 0.462 0.400 0.600
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.0000 0.937 0.000 1.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0000 0.937 0.000 1.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.0000 0.778 1.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.9129 0.740 0.672 0.328
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.7056 0.903 0.808 0.192
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0000 0.937 0.000 1.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.937 0.000 1.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.937 0.000 1.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.7139 0.901 0.804 0.196
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.0000 0.937 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.0237 0.8944 0.000 0.004 0.996
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.0000 0.8140 0.000 1.000 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.2796 0.7743 0.092 0.908 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.8227 0.5648 0.536 0.384 0.080
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 2 0.0000 0.8140 0.000 1.000 0.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.0424 0.8898 0.000 0.008 0.992
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.8227 0.5648 0.536 0.384 0.080
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0000 0.8140 0.000 1.000 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.8968 0.000 0.000 1.000
#> 5482053D-9F48-4773-B68A-302B3A612503 3 0.0000 0.8968 0.000 0.000 1.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 2 0.5988 0.6510 0.304 0.688 0.008
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.8227 0.5648 0.536 0.384 0.080
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.0000 0.8140 0.000 1.000 0.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.6286 0.5767 0.536 0.000 0.464
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.8968 0.000 0.000 1.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.2066 0.7694 0.000 0.940 0.060
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.6286 0.5570 0.464 0.536 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 2 0.0000 0.8140 0.000 1.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.8140 0.000 1.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 2 0.0000 0.8140 0.000 1.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.6286 0.5570 0.464 0.536 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.6026 0.6082 0.376 0.624 0.000
#> F325847E-F046-4B67-B01C-16919C401020 1 0.6286 0.5767 0.536 0.000 0.464
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.8968 0.000 0.000 1.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.6286 0.5570 0.464 0.536 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 2 0.0000 0.8140 0.000 1.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 2 0.0000 0.8140 0.000 1.000 0.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.6495 0.5795 0.536 0.004 0.460
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.0000 0.8968 0.000 0.000 1.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.6286 0.5767 0.536 0.000 0.464
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.0000 0.8140 0.000 1.000 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 2 0.0000 0.8140 0.000 1.000 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.6026 0.6082 0.376 0.624 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 3 0.0000 0.8968 0.000 0.000 1.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 2 0.6008 0.6105 0.372 0.628 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 3 0.0000 0.8968 0.000 0.000 1.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 2 0.2537 0.7499 0.000 0.920 0.080
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.4931 0.5581 0.000 0.232 0.768
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.0000 0.8140 0.000 1.000 0.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.2448 0.7537 0.000 0.924 0.076
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.2165 0.8171 0.000 0.064 0.936
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 2 0.2165 0.7675 0.000 0.936 0.064
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.0000 0.8140 0.000 1.000 0.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 2 0.0000 0.8140 0.000 1.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 2 0.5327 0.5207 0.000 0.728 0.272
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.9137 0.6317 0.536 0.188 0.276
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.0000 0.8140 0.000 1.000 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 2 0.0237 0.8132 0.004 0.996 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.2537 0.8234 0.000 0.080 0.920
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.0000 0.8140 0.000 1.000 0.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.8808 0.6195 0.536 0.332 0.132
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 2 0.0237 0.8132 0.004 0.996 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.8389 0.5813 0.536 0.372 0.092
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.6302 -0.0301 0.000 0.480 0.520
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.8227 0.5648 0.536 0.384 0.080
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 3 0.0000 0.8968 0.000 0.000 1.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.7145 0.5884 0.536 0.024 0.440
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.4399 0.7298 0.188 0.812 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.2537 0.8234 0.000 0.080 0.920
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.0000 0.8968 0.000 0.000 1.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 3 0.2537 0.8234 0.000 0.080 0.920
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 2 0.6500 -0.2614 0.464 0.532 0.004
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.6286 0.5570 0.464 0.536 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 2 0.0237 0.8116 0.000 0.996 0.004
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 2 0.0000 0.8140 0.000 1.000 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.6286 0.5570 0.464 0.536 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.7036 0.4467 0.536 0.444 0.020
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.0000 0.8968 0.000 0.000 1.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 2 0.0000 0.8140 0.000 1.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.8140 0.000 1.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.6286 0.5570 0.464 0.536 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 3 0.0000 0.8968 0.000 0.000 1.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 3 0.3816 0.6612 0.148 0.000 0.852
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.1529 0.8670 0.000 0.040 0.960
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.6008 0.2689 0.000 0.372 0.628
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 2 0.6274 0.0367 0.000 0.544 0.456
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.6286 0.5767 0.536 0.000 0.464
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.6286 0.5570 0.464 0.536 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.8968 0.000 0.000 1.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 2 0.4346 0.6472 0.000 0.816 0.184
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.0000 0.8968 0.000 0.000 1.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.6295 0.5616 0.528 0.000 0.472
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.0000 0.8140 0.000 1.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.8968 0.000 0.000 1.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 2 0.0000 0.8140 0.000 1.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.6286 0.5570 0.464 0.536 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0000 0.8140 0.000 1.000 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0237 0.8132 0.004 0.996 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.6286 0.5767 0.536 0.000 0.464
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.6286 0.5570 0.464 0.536 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.1643 0.7985 0.044 0.956 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.6286 0.5767 0.536 0.000 0.464
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.6286 0.5767 0.536 0.000 0.464
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.2959 0.7980 0.000 0.100 0.900
#> C2662596-6E2F-4924-B051-CEA1AC87B197 3 0.0000 0.8968 0.000 0.000 1.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.0000 0.8968 0.000 0.000 1.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.0000 0.8140 0.000 1.000 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 2 0.6008 0.6105 0.372 0.628 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 2 0.0000 0.8140 0.000 1.000 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 3 0.2537 0.8234 0.000 0.080 0.920
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.2796 0.7743 0.092 0.908 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 2 0.0000 0.8140 0.000 1.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 3 0.0592 0.8890 0.000 0.012 0.988
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.0000 0.8140 0.000 1.000 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.6286 0.5570 0.464 0.536 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.8389 0.5813 0.536 0.372 0.092
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.0000 0.8140 0.000 1.000 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 3 0.0000 0.8968 0.000 0.000 1.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.0237 0.8118 0.000 0.996 0.004
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 3 0.0000 0.8968 0.000 0.000 1.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.6286 0.5570 0.464 0.536 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 2 0.0000 0.8140 0.000 1.000 0.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 2 0.6291 -0.0135 0.000 0.532 0.468
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.8968 0.000 0.000 1.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.5760 0.6482 0.328 0.672 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.9122 0.6311 0.536 0.184 0.280
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.0237 0.8132 0.004 0.996 0.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0000 0.8140 0.000 1.000 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.6286 0.5767 0.536 0.000 0.464
#> B12A4446-2310-4139-897F-CA030478CBD5 2 0.6140 0.2757 0.000 0.596 0.404
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.8968 0.000 0.000 1.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.6286 0.5570 0.464 0.536 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.8140 0.000 1.000 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.8140 0.000 1.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.2537 0.8234 0.000 0.080 0.920
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.0000 0.8140 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.0336 0.936 0.992 0.000 0.008 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.4961 0.332 0.000 0.552 0.448 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 4 0.0188 0.945 0.000 0.000 0.004 0.996
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.6315 0.256 0.540 0.000 0.396 0.064
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 4 0.0000 0.949 0.000 0.000 0.000 1.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.4543 0.479 0.000 0.324 0.676 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 4 0.0000 0.949 0.000 0.000 0.000 1.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 4 0.0000 0.949 0.000 0.000 0.000 1.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0188 0.825 0.000 0.996 0.004 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 3 0.0188 0.929 0.000 0.004 0.996 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.823 0.000 1.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.4967 0.164 0.000 0.452 0.548 0.000
#> F325847E-F046-4B67-B01C-16919C401020 4 0.0000 0.949 0.000 0.000 0.000 1.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.2216 0.796 0.000 0.908 0.092 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 4 0.0000 0.949 0.000 0.000 0.000 1.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.0707 0.926 0.980 0.000 0.020 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 4 0.3764 0.694 0.216 0.000 0.000 0.784
#> A8E48877-F8AB-44DD-A18B-194D87C44931 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 3 0.4967 0.164 0.000 0.452 0.548 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 3 0.4830 0.325 0.000 0.392 0.608 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.2469 0.827 0.892 0.000 0.108 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.2408 0.836 0.896 0.000 0.104 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 3 0.0188 0.929 0.004 0.000 0.996 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 3 0.3569 0.698 0.196 0.000 0.804 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 4 0.0000 0.949 0.000 0.000 0.000 1.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.0188 0.929 0.000 0.004 0.996 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 4 0.0000 0.949 0.000 0.000 0.000 1.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 3 0.0188 0.929 0.000 0.004 0.996 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 4 0.0000 0.949 0.000 0.000 0.000 1.000
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.4222 0.581 0.272 0.000 0.728 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 4 0.0336 0.941 0.000 0.000 0.008 0.992
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 4 0.0000 0.949 0.000 0.000 0.000 1.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.4866 0.430 0.000 0.596 0.404 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 4 0.4941 0.237 0.000 0.000 0.436 0.564
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.823 0.000 1.000 0.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.823 0.000 1.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 4 0.0000 0.949 0.000 0.000 0.000 1.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.2216 0.796 0.000 0.908 0.092 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.4697 0.448 0.644 0.000 0.000 0.356
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.0707 0.926 0.980 0.000 0.020 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.4697 0.445 0.644 0.000 0.356 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 3 0.1792 0.863 0.068 0.000 0.932 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 4 0.0000 0.949 0.000 0.000 0.000 1.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0336 0.824 0.000 0.992 0.008 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.3311 0.730 0.172 0.000 0.828 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 4 0.2408 0.842 0.104 0.000 0.000 0.896
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0188 0.825 0.000 0.996 0.004 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 3 0.0336 0.926 0.000 0.008 0.992 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 4 0.1022 0.920 0.032 0.000 0.000 0.968
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.823 0.000 1.000 0.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 3 0.2345 0.832 0.000 0.100 0.900 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 4 0.0000 0.949 0.000 0.000 0.000 1.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 4 0.0000 0.949 0.000 0.000 0.000 1.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.1302 0.903 0.956 0.000 0.044 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 3 0.4948 0.199 0.000 0.440 0.560 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.4961 0.332 0.000 0.552 0.448 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.823 0.000 1.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 4 0.0000 0.949 0.000 0.000 0.000 1.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.2281 0.793 0.000 0.904 0.096 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 3 0.0336 0.925 0.008 0.000 0.992 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.4522 0.592 0.000 0.680 0.320 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 4 0.0000 0.949 0.000 0.000 0.000 1.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.0188 0.929 0.000 0.004 0.996 0.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 4 0.0000 0.949 0.000 0.000 0.000 1.000
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.2814 0.802 0.868 0.000 0.132 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0469 0.824 0.000 0.988 0.012 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.0188 0.939 0.996 0.000 0.004 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.0000 0.932 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 5 0.3305 0.854 0.000 0.000 0.000 0.224 0.776
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 5 0.3814 0.291 0.000 0.276 0.000 0.004 0.720
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.3274 0.744 0.780 0.000 0.000 0.000 0.220
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.3305 0.854 0.000 0.000 0.000 0.224 0.776
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.1764 0.887 0.064 0.000 0.928 0.000 0.008
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 5 0.3305 0.854 0.000 0.000 0.000 0.224 0.776
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 4 0.4637 0.135 0.000 0.452 0.000 0.536 0.012
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.0290 0.942 0.992 0.000 0.000 0.000 0.008
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 5 0.3305 0.854 0.000 0.000 0.000 0.224 0.776
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 5 0.0290 0.692 0.000 0.000 0.000 0.008 0.992
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0771 0.853 0.000 0.976 0.000 0.004 0.020
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 4 0.4074 0.623 0.000 0.000 0.000 0.636 0.364
#> F772EA39-E408-4908-BADD-C786D702BF9B 5 0.3461 0.853 0.000 0.004 0.000 0.224 0.772
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0162 0.886 0.000 0.000 0.000 0.996 0.004
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0451 0.852 0.000 0.988 0.000 0.004 0.008
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.3636 0.677 0.000 0.728 0.000 0.000 0.272
#> F325847E-F046-4B67-B01C-16919C401020 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.2377 0.827 0.000 0.872 0.000 0.000 0.128
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0162 0.886 0.000 0.000 0.000 0.996 0.004
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 5 0.3305 0.854 0.000 0.000 0.000 0.224 0.776
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.3242 0.698 0.784 0.000 0.216 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 5 0.3305 0.854 0.000 0.000 0.000 0.224 0.776
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 5 0.3305 0.854 0.000 0.000 0.000 0.224 0.776
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.3636 0.677 0.000 0.728 0.000 0.000 0.272
#> 91BA5F90-9174-4533-A050-39A28E34A94D 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.6024 -0.374 0.000 0.412 0.000 0.116 0.472
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0162 0.886 0.000 0.000 0.000 0.996 0.004
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.2645 0.830 0.000 0.000 0.888 0.068 0.044
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 5 0.3305 0.854 0.000 0.000 0.000 0.224 0.776
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 5 0.3305 0.854 0.000 0.000 0.000 0.224 0.776
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.1701 0.896 0.000 0.000 0.936 0.048 0.016
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0162 0.886 0.000 0.000 0.000 0.996 0.004
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 5 0.3305 0.854 0.000 0.000 0.000 0.224 0.776
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0162 0.886 0.000 0.000 0.000 0.996 0.004
#> CB207A52-09AC-49D3-8240-5840CDFBB154 5 0.5602 0.645 0.000 0.000 0.196 0.164 0.640
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 5 0.3305 0.854 0.000 0.000 0.000 0.224 0.776
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 4 0.1341 0.842 0.000 0.000 0.000 0.944 0.056
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.3305 0.854 0.000 0.000 0.000 0.224 0.776
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 4 0.3816 0.662 0.000 0.000 0.000 0.696 0.304
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.6346 -0.240 0.000 0.000 0.436 0.160 0.404
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.0162 0.943 0.996 0.000 0.000 0.004 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 5 0.3814 0.291 0.000 0.276 0.000 0.004 0.720
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.4283 0.407 0.544 0.000 0.000 0.000 0.456
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0451 0.852 0.000 0.988 0.000 0.004 0.008
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 4 0.0162 0.886 0.000 0.000 0.000 0.996 0.004
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 5 0.3305 0.854 0.000 0.000 0.000 0.224 0.776
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0162 0.886 0.000 0.000 0.000 0.996 0.004
#> EE16D845-31F2-4178-800B-CA2C358841AD 5 0.0451 0.690 0.000 0.004 0.000 0.008 0.988
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.2377 0.827 0.000 0.872 0.000 0.000 0.128
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 3 0.4030 0.453 0.352 0.000 0.648 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0290 0.941 0.000 0.000 0.992 0.000 0.008
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.4309 0.495 0.000 0.000 0.676 0.308 0.016
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0162 0.886 0.000 0.000 0.000 0.996 0.004
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.4029 0.625 0.000 0.680 0.000 0.004 0.316
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 4 0.0404 0.881 0.000 0.000 0.000 0.988 0.012
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.1608 0.878 0.928 0.000 0.072 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.1732 0.815 0.000 0.000 0.000 0.920 0.080
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.0794 0.870 0.000 0.000 0.000 0.972 0.028
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.1041 0.850 0.000 0.964 0.000 0.004 0.032
#> 322AF320-1379-4F51-AFDC-5292A060CD52 5 0.3461 0.853 0.000 0.004 0.000 0.224 0.772
#> 53A96249-66D5-4C26-893B-ADC71481D261 5 0.4737 0.801 0.000 0.068 0.000 0.224 0.708
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0880 0.919 0.968 0.000 0.032 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0955 0.847 0.000 0.968 0.000 0.004 0.028
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 5 0.4062 0.832 0.000 0.040 0.000 0.196 0.764
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.0451 0.938 0.000 0.000 0.988 0.008 0.004
#> C2662596-6E2F-4924-B051-CEA1AC87B197 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 5 0.3461 0.852 0.000 0.000 0.004 0.224 0.772
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 2 0.3684 0.665 0.000 0.720 0.000 0.000 0.280
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 5 0.3305 0.854 0.000 0.000 0.000 0.224 0.776
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 5 0.3814 0.291 0.000 0.276 0.000 0.004 0.720
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 4 0.0162 0.886 0.000 0.000 0.000 0.996 0.004
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.3336 0.603 0.000 0.000 0.228 0.772 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 5 0.3305 0.854 0.000 0.000 0.000 0.224 0.776
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 5 0.3305 0.854 0.000 0.000 0.000 0.224 0.776
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 4 0.3274 0.677 0.000 0.000 0.000 0.780 0.220
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.2377 0.827 0.000 0.872 0.000 0.000 0.128
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 5 0.3305 0.854 0.000 0.000 0.000 0.224 0.776
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0162 0.886 0.000 0.000 0.000 0.996 0.004
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 5 0.4339 0.486 0.000 0.296 0.000 0.020 0.684
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 5 0.3461 0.853 0.000 0.004 0.000 0.224 0.772
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 5 0.0290 0.692 0.000 0.000 0.000 0.008 0.992
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 5 0.4590 0.282 0.000 0.000 0.420 0.012 0.568
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 5 0.0290 0.692 0.000 0.000 0.000 0.008 0.992
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 5 0.3305 0.854 0.000 0.000 0.000 0.224 0.776
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.0000 0.948 0.000 0.000 1.000 0.000 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 5 0.3305 0.854 0.000 0.000 0.000 0.224 0.776
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 6 0.0000 0.884 0.000 0.000 0.000 0.000 0.000 1.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.680 0.000 1.000 0.000 0.000 0.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.3647 0.496 0.640 0.360 0.000 0.000 0.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 6 0.0000 0.884 0.000 0.000 0.000 0.000 0.000 1.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.1867 0.892 0.064 0.000 0.916 0.000 0.000 0.020
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.0000 0.936 1.000 0.000 0.000 0.000 0.000 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 6 0.0000 0.884 0.000 0.000 0.000 0.000 0.000 1.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 5 0.0622 0.924 0.000 0.000 0.000 0.012 0.980 0.008
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.0260 0.931 0.992 0.008 0.000 0.000 0.000 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 6 0.0000 0.884 0.000 0.000 0.000 0.000 0.000 1.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.936 1.000 0.000 0.000 0.000 0.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 6 0.3828 0.400 0.000 0.440 0.000 0.000 0.000 0.560
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.3647 0.633 0.000 0.640 0.000 0.000 0.360 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 4 0.3979 0.527 0.000 0.360 0.000 0.628 0.000 0.012
#> F772EA39-E408-4908-BADD-C786D702BF9B 6 0.0000 0.884 0.000 0.000 0.000 0.000 0.000 1.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 0.869 0.000 0.000 0.000 1.000 0.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.3756 0.579 0.000 0.600 0.000 0.000 0.400 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 5 0.0000 0.947 0.000 0.000 0.000 0.000 1.000 0.000
#> F325847E-F046-4B67-B01C-16919C401020 1 0.0000 0.936 1.000 0.000 0.000 0.000 0.000 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 5 0.0000 0.947 0.000 0.000 0.000 0.000 1.000 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 0.869 0.000 0.000 0.000 1.000 0.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 6 0.0000 0.884 0.000 0.000 0.000 0.000 0.000 1.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0000 0.936 1.000 0.000 0.000 0.000 0.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.0146 0.961 0.000 0.000 0.996 0.000 0.000 0.004
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.2912 0.667 0.784 0.000 0.216 0.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 6 0.0000 0.884 0.000 0.000 0.000 0.000 0.000 1.000
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#> 91BA5F90-9174-4533-A050-39A28E34A94D 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 5 0.3428 0.418 0.000 0.304 0.000 0.000 0.696 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 0.869 0.000 0.000 0.000 1.000 0.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.1812 0.876 0.000 0.000 0.912 0.008 0.000 0.080
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 6 0.0000 0.884 0.000 0.000 0.000 0.000 0.000 1.000
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#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.2001 0.887 0.000 0.000 0.912 0.040 0.000 0.048
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0000 0.869 0.000 0.000 0.000 1.000 0.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 6 0.0000 0.884 0.000 0.000 0.000 0.000 0.000 1.000
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.0000 0.869 0.000 0.000 0.000 1.000 0.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 6 0.2793 0.684 0.000 0.000 0.200 0.000 0.000 0.800
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.0000 0.936 1.000 0.000 0.000 0.000 0.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 6 0.0000 0.884 0.000 0.000 0.000 0.000 0.000 1.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 4 0.1387 0.825 0.000 0.000 0.000 0.932 0.000 0.068
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 6 0.0000 0.884 0.000 0.000 0.000 0.000 0.000 1.000
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0000 0.936 1.000 0.000 0.000 0.000 0.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 4 0.3647 0.535 0.000 0.360 0.000 0.640 0.000 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.0000 0.936 1.000 0.000 0.000 0.000 0.000 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 6 0.3782 0.321 0.000 0.000 0.412 0.000 0.000 0.588
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.0260 0.929 0.992 0.000 0.000 0.000 0.000 0.008
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0000 0.936 1.000 0.000 0.000 0.000 0.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.680 0.000 1.000 0.000 0.000 0.000 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.5035 0.362 0.556 0.360 0.000 0.000 0.000 0.084
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.3833 0.492 0.000 0.556 0.000 0.000 0.444 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 4 0.0000 0.869 0.000 0.000 0.000 1.000 0.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 6 0.0000 0.884 0.000 0.000 0.000 0.000 0.000 1.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 5 0.0000 0.947 0.000 0.000 0.000 0.000 1.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.0000 0.936 1.000 0.000 0.000 0.000 0.000 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 0.869 0.000 0.000 0.000 1.000 0.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 6 0.3828 0.400 0.000 0.440 0.000 0.000 0.000 0.560
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 5 0.0000 0.947 0.000 0.000 0.000 0.000 1.000 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 3 0.3659 0.437 0.364 0.000 0.636 0.000 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0790 0.935 0.000 0.000 0.968 0.000 0.000 0.032
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.4941 0.515 0.000 0.000 0.640 0.124 0.000 0.236
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 0.869 0.000 0.000 0.000 1.000 0.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0000 0.936 1.000 0.000 0.000 0.000 0.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.680 0.000 1.000 0.000 0.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 4 0.3309 0.618 0.000 0.000 0.000 0.720 0.000 0.280
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.1765 0.832 0.904 0.000 0.096 0.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.3592 0.547 0.000 0.000 0.000 0.656 0.000 0.344
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0146 0.961 0.000 0.000 0.996 0.000 0.000 0.004
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.2883 0.696 0.000 0.000 0.000 0.788 0.000 0.212
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.3647 0.633 0.000 0.640 0.000 0.000 0.360 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 6 0.1556 0.827 0.000 0.080 0.000 0.000 0.000 0.920
#> 53A96249-66D5-4C26-893B-ADC71481D261 6 0.0000 0.884 0.000 0.000 0.000 0.000 0.000 1.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0790 0.906 0.968 0.000 0.032 0.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.3647 0.633 0.000 0.640 0.000 0.000 0.360 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 6 0.1863 0.803 0.000 0.000 0.000 0.000 0.104 0.896
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0000 0.936 1.000 0.000 0.000 0.000 0.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0000 0.936 1.000 0.000 0.000 0.000 0.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.0146 0.961 0.000 0.000 0.996 0.000 0.000 0.004
#> C2662596-6E2F-4924-B051-CEA1AC87B197 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 6 0.0146 0.881 0.000 0.000 0.004 0.000 0.000 0.996
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.0000 0.947 0.000 0.000 0.000 0.000 1.000 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 6 0.0000 0.884 0.000 0.000 0.000 0.000 0.000 1.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.680 0.000 1.000 0.000 0.000 0.000 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 4 0.0000 0.869 0.000 0.000 0.000 1.000 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 0.869 0.000 0.000 0.000 1.000 0.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 6 0.0000 0.884 0.000 0.000 0.000 0.000 0.000 1.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 5 0.0000 0.947 0.000 0.000 0.000 0.000 1.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.0000 0.936 1.000 0.000 0.000 0.000 0.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 6 0.0000 0.884 0.000 0.000 0.000 0.000 0.000 1.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 4 0.2793 0.719 0.000 0.200 0.000 0.800 0.000 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 5 0.0000 0.947 0.000 0.000 0.000 0.000 1.000 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 6 0.0000 0.884 0.000 0.000 0.000 0.000 0.000 1.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0000 0.869 0.000 0.000 0.000 1.000 0.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 6 0.4300 0.587 0.000 0.080 0.000 0.000 0.208 0.712
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.0000 0.936 1.000 0.000 0.000 0.000 0.000 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 6 0.0000 0.884 0.000 0.000 0.000 0.000 0.000 1.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 6 0.3756 0.461 0.000 0.400 0.000 0.000 0.000 0.600
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.0000 0.936 1.000 0.000 0.000 0.000 0.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 6 0.3847 0.225 0.000 0.000 0.456 0.000 0.000 0.544
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 5 0.0000 0.947 0.000 0.000 0.000 0.000 1.000 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 6 0.3828 0.400 0.000 0.440 0.000 0.000 0.000 0.560
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 6 0.0000 0.884 0.000 0.000 0.000 0.000 0.000 1.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.0000 0.964 0.000 0.000 1.000 0.000 0.000 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 6 0.0000 0.884 0.000 0.000 0.000 0.000 0.000 1.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "mclust"]
# you can also extract it by
# res = res_list["ATC:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.744 0.813 0.924 0.4365 0.537 0.537
#> 3 3 0.585 0.720 0.829 0.3463 0.656 0.464
#> 4 4 0.569 0.666 0.793 0.2008 0.835 0.600
#> 5 5 0.852 0.894 0.892 0.1013 0.917 0.705
#> 6 6 0.947 0.931 0.962 0.0158 0.915 0.676
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.0000 0.835 1.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 1 0.9866 0.426 0.568 0.432
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.953 0.000 1.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.0000 0.953 0.000 1.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.9795 0.452 0.584 0.416
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 2 0.0000 0.953 0.000 1.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 2 0.0000 0.953 0.000 1.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.9795 0.079 0.416 0.584
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.0000 0.835 1.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.835 1.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 2 0.0000 0.953 0.000 1.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.0000 0.953 0.000 1.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 1 0.9866 0.426 0.568 0.432
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 2 0.0000 0.953 0.000 1.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.0000 0.835 1.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.0000 0.953 0.000 1.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.953 0.000 1.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 2 0.0000 0.953 0.000 1.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 1 0.9896 0.406 0.560 0.440
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 2 0.0000 0.953 0.000 1.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.953 0.000 1.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.0000 0.953 0.000 1.000
#> F325847E-F046-4B67-B01C-16919C401020 2 0.0000 0.953 0.000 1.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 2 0.0000 0.953 0.000 1.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.953 0.000 1.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 2 0.0000 0.953 0.000 1.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 1 0.9866 0.426 0.568 0.432
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 2 0.0000 0.953 0.000 1.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 2 0.4298 0.858 0.088 0.912
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 2 0.0000 0.953 0.000 1.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 1 0.9866 0.426 0.568 0.432
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 2 0.0000 0.953 0.000 1.000
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.953 0.000 1.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.835 1.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 2 0.0000 0.953 0.000 1.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.9427 0.468 0.640 0.360
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 2 0.0000 0.953 0.000 1.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.1414 0.823 0.980 0.020
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 1 0.9866 0.426 0.568 0.432
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.0000 0.953 0.000 1.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 2 1.0000 -0.122 0.500 0.500
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 2 0.0000 0.953 0.000 1.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 1 0.9866 0.426 0.568 0.432
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 2 0.6148 0.756 0.152 0.848
#> CB207A52-09AC-49D3-8240-5840CDFBB154 2 0.0000 0.953 0.000 1.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 2 0.0000 0.953 0.000 1.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.0000 0.953 0.000 1.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 2 0.0000 0.953 0.000 1.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.0000 0.835 1.000 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 1 0.9866 0.426 0.568 0.432
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 2 0.0000 0.953 0.000 1.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 2 0.0000 0.953 0.000 1.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 2 0.0000 0.953 0.000 1.000
#> AD294665-6F90-459C-90D5-3058F210225D 2 0.0000 0.953 0.000 1.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 2 0.0000 0.953 0.000 1.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.835 1.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 2 0.0000 0.953 0.000 1.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.953 0.000 1.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.0376 0.833 0.996 0.004
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0000 0.835 1.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.835 1.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 2 0.0000 0.953 0.000 1.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.953 0.000 1.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 2 0.0000 0.953 0.000 1.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.0000 0.835 1.000 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.953 0.000 1.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 2 0.0000 0.953 0.000 1.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0000 0.835 1.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 2 0.0000 0.953 0.000 1.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.953 0.000 1.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.953 0.000 1.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.835 1.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 2 0.0000 0.953 0.000 1.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.0000 0.835 1.000 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.0000 0.835 1.000 0.000
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 2 0.0000 0.953 0.000 1.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 2 0.0000 0.953 0.000 1.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.953 0.000 1.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.0000 0.835 1.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 2 0.0672 0.945 0.008 0.992
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.835 1.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 2 0.0000 0.953 0.000 1.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.1414 0.933 0.020 0.980
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.0000 0.835 1.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 2 0.9754 0.112 0.408 0.592
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.953 0.000 1.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.5519 0.795 0.128 0.872
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.1184 0.937 0.016 0.984
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 2 0.0000 0.953 0.000 1.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.953 0.000 1.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.9795 0.079 0.416 0.584
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 2 0.0000 0.953 0.000 1.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 2 0.0000 0.953 0.000 1.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.0000 0.835 1.000 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.835 1.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.0000 0.835 1.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.9209 0.565 0.664 0.336
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 2 0.0000 0.953 0.000 1.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.9795 0.453 0.584 0.416
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.835 1.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.953 0.000 1.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 2 0.0000 0.953 0.000 1.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 2 0.2043 0.920 0.032 0.968
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.9970 -0.135 0.468 0.532
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.953 0.000 1.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 2 0.0000 0.953 0.000 1.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 1 0.9866 0.426 0.568 0.432
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.835 1.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.0000 0.953 0.000 1.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.835 1.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.953 0.000 1.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.0000 0.835 1.000 0.000
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 2 0.0000 0.953 0.000 1.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.0000 0.835 1.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.9795 0.079 0.416 0.584
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 2 0.0000 0.953 0.000 1.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.0000 0.953 0.000 1.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0000 0.953 0.000 1.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 2 0.0000 0.953 0.000 1.000
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.0000 0.835 1.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.5294 0.767 0.880 0.120
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0000 0.953 0.000 1.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.953 0.000 1.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 1 0.9977 0.316 0.528 0.472
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.0000 0.835 1.000 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 1 0.9866 0.426 0.568 0.432
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.0000 0.8969 0.000 0.000 1.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.3752 0.4500 0.000 0.856 0.144
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.5948 0.7389 0.360 0.640 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 2 0.6225 0.6708 0.432 0.568 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.6379 0.6852 0.032 0.256 0.712
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.9188 -0.0898 0.380 0.152 0.468
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 2 0.6252 0.6555 0.444 0.556 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.3192 0.4704 0.000 0.888 0.112
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.8969 0.000 0.000 1.000
#> 5482053D-9F48-4773-B68A-302B3A612503 3 0.0000 0.8969 0.000 0.000 1.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 2 0.6079 0.7369 0.388 0.612 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 2 0.6235 0.6643 0.436 0.564 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.3686 0.4521 0.000 0.860 0.140
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.9086 1.000 0.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.8969 0.000 0.000 1.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.6154 0.7022 0.408 0.592 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.5948 0.7389 0.360 0.640 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 2 0.6154 0.7303 0.408 0.592 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.3267 0.4682 0.000 0.884 0.116
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 2 0.6154 0.7303 0.408 0.592 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.5882 0.7419 0.348 0.652 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.5882 0.7419 0.348 0.652 0.000
#> F325847E-F046-4B67-B01C-16919C401020 1 0.1753 0.8821 0.952 0.048 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 2 0.6577 0.7220 0.420 0.572 0.008
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.5882 0.7419 0.348 0.652 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 2 0.6154 0.7303 0.408 0.592 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 2 0.3752 0.4500 0.000 0.856 0.144
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0747 0.9008 0.984 0.016 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.3583 0.8315 0.056 0.044 0.900
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.9086 1.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.3752 0.4500 0.000 0.856 0.144
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 2 0.6339 0.7422 0.360 0.632 0.008
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.5882 0.7419 0.348 0.652 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 3 0.0000 0.8969 0.000 0.000 1.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 2 0.6154 0.7303 0.408 0.592 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 3 0.6339 0.3530 0.360 0.008 0.632
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 2 0.6154 0.7303 0.408 0.592 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.1905 0.8780 0.028 0.016 0.956
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.3752 0.4500 0.000 0.856 0.144
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.6140 0.7065 0.404 0.596 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.3472 0.8440 0.040 0.056 0.904
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 2 0.6154 0.7303 0.408 0.592 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.3686 0.4521 0.000 0.860 0.140
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 2 0.6769 0.7354 0.392 0.592 0.016
#> CB207A52-09AC-49D3-8240-5840CDFBB154 3 0.6894 0.5090 0.256 0.052 0.692
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.0000 0.9086 1.000 0.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.5948 0.7389 0.360 0.640 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 2 0.6154 0.7303 0.408 0.592 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.0000 0.8969 0.000 0.000 1.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.3752 0.4500 0.000 0.856 0.144
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0000 0.9086 1.000 0.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 2 0.6154 0.7303 0.408 0.592 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.1860 0.8791 0.948 0.052 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.7424 0.1829 0.388 0.040 0.572
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.6308 -0.5447 0.508 0.492 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 3 0.0000 0.8969 0.000 0.000 1.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0000 0.9086 1.000 0.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.5948 0.7389 0.360 0.640 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.1585 0.8812 0.028 0.008 0.964
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.0000 0.8969 0.000 0.000 1.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 3 0.0000 0.8969 0.000 0.000 1.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 2 0.6192 0.7263 0.420 0.580 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.5882 0.7419 0.348 0.652 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 2 0.6154 0.7303 0.408 0.592 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.1753 0.8759 0.000 0.048 0.952
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.5882 0.7419 0.348 0.652 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.5138 0.2967 0.748 0.252 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.0000 0.8969 0.000 0.000 1.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 2 0.6154 0.7303 0.408 0.592 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.6140 0.7065 0.404 0.596 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.5882 0.7419 0.348 0.652 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 3 0.0000 0.8969 0.000 0.000 1.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.9086 1.000 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0237 0.8957 0.000 0.004 0.996
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.0237 0.8957 0.000 0.004 0.996
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 2 0.6154 0.7303 0.408 0.592 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0000 0.9086 1.000 0.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.5948 0.7389 0.360 0.640 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.8969 0.000 0.000 1.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.6950 0.5146 0.252 0.056 0.692
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.0000 0.8969 0.000 0.000 1.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.9086 1.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.7481 0.6901 0.296 0.640 0.064
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0237 0.8952 0.004 0.000 0.996
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 2 0.8814 0.6330 0.312 0.548 0.140
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.5948 0.7389 0.360 0.640 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.3181 0.4973 0.024 0.912 0.064
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.4075 0.5369 0.072 0.880 0.048
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.9086 1.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.5948 0.7389 0.360 0.640 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.3116 0.4721 0.000 0.892 0.108
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0000 0.9086 1.000 0.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0000 0.9086 1.000 0.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.0000 0.8969 0.000 0.000 1.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 3 0.0000 0.8969 0.000 0.000 1.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.0000 0.8969 0.000 0.000 1.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 3 0.6148 0.4247 0.004 0.356 0.640
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 2 0.6079 0.7369 0.388 0.612 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 3 0.5269 0.6999 0.016 0.200 0.784
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 3 0.0000 0.8969 0.000 0.000 1.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.6126 0.7103 0.400 0.600 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 2 0.6154 0.7303 0.408 0.592 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 2 0.6540 0.7247 0.408 0.584 0.008
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.3686 0.4521 0.000 0.860 0.140
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.5882 0.7419 0.348 0.652 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.1860 0.8791 0.948 0.052 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.3752 0.4500 0.000 0.856 0.144
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 3 0.0000 0.8969 0.000 0.000 1.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.6154 0.7303 0.408 0.592 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 3 0.0000 0.8969 0.000 0.000 1.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.5968 0.7417 0.364 0.636 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.2796 0.8508 0.000 0.092 0.908
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 2 0.6154 0.7303 0.408 0.592 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.8969 0.000 0.000 1.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.2625 0.4801 0.000 0.916 0.084
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.1860 0.8791 0.948 0.052 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.6104 0.7422 0.348 0.648 0.004
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.6140 0.7065 0.404 0.596 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.1860 0.8791 0.948 0.052 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.1289 0.8839 0.000 0.032 0.968
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.4110 0.7429 0.152 0.004 0.844
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.5882 0.7419 0.348 0.652 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.6140 0.7065 0.404 0.596 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.3267 0.4682 0.000 0.884 0.116
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.0000 0.8969 0.000 0.000 1.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.3752 0.4500 0.000 0.856 0.144
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.3024 0.8860 0.852 0.000 0.000 0.148
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.5770 0.4714 0.140 0.712 0.000 0.148
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.5386 0.4235 0.000 0.632 0.024 0.344
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 3 0.7542 -0.1578 0.000 0.208 0.472 0.320
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.6805 0.6222 0.592 0.260 0.000 0.148
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.6490 0.6003 0.608 0.008 0.076 0.308
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.7366 -0.0484 0.000 0.344 0.484 0.172
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.2921 0.5101 0.140 0.860 0.000 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.0000 0.8794 1.000 0.000 0.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.8794 1.000 0.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 4 0.3024 0.7608 0.000 0.148 0.000 0.852
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 3 0.6620 0.1901 0.000 0.104 0.576 0.320
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.5770 0.4714 0.140 0.712 0.000 0.148
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 3 0.0000 0.9083 0.000 0.000 1.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.0000 0.8794 1.000 0.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.5386 0.4235 0.000 0.632 0.024 0.344
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.5386 0.4235 0.000 0.632 0.024 0.344
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 4 0.3024 0.7608 0.000 0.148 0.000 0.852
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.2921 0.5101 0.140 0.860 0.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0469 0.6971 0.000 0.012 0.000 0.988
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.5386 0.4235 0.000 0.632 0.024 0.344
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.7206 0.3759 0.140 0.460 0.000 0.400
#> F325847E-F046-4B67-B01C-16919C401020 3 0.0000 0.9083 0.000 0.000 1.000 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 4 0.7042 0.1277 0.132 0.000 0.352 0.516
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 4 0.5691 0.0591 0.028 0.408 0.000 0.564
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.1940 0.7387 0.000 0.076 0.000 0.924
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 2 0.5770 0.4714 0.140 0.712 0.000 0.148
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 3 0.0000 0.9083 0.000 0.000 1.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.3677 0.8818 0.836 0.008 0.008 0.148
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 3 0.0000 0.9083 0.000 0.000 1.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.5770 0.4714 0.140 0.712 0.000 0.148
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 4 0.6449 -0.0710 0.140 0.220 0.000 0.640
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 4 0.7046 -0.0237 0.136 0.340 0.000 0.524
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.8794 1.000 0.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 4 0.3024 0.7608 0.000 0.148 0.000 0.852
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.3498 0.7446 0.832 0.008 0.000 0.160
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0000 0.6861 0.000 0.000 0.000 1.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.3351 0.8845 0.844 0.008 0.000 0.148
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 2 0.5770 0.4714 0.140 0.712 0.000 0.148
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.5386 0.4235 0.000 0.632 0.024 0.344
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.3351 0.8845 0.844 0.008 0.000 0.148
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.2647 0.7554 0.000 0.120 0.000 0.880
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.5770 0.4714 0.140 0.712 0.000 0.148
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.4857 0.1944 0.324 0.008 0.000 0.668
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.4033 0.8753 0.824 0.008 0.020 0.148
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.0000 0.9083 0.000 0.000 1.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.7796 0.4523 0.140 0.492 0.024 0.344
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 4 0.3024 0.7608 0.000 0.148 0.000 0.852
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.3024 0.8860 0.852 0.000 0.000 0.148
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.5770 0.4714 0.140 0.712 0.000 0.148
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 3 0.0000 0.9083 0.000 0.000 1.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 4 0.3024 0.7608 0.000 0.148 0.000 0.852
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 3 0.0000 0.9083 0.000 0.000 1.000 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.4789 0.8461 0.780 0.008 0.040 0.172
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 3 0.0188 0.9051 0.000 0.000 0.996 0.004
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.8794 1.000 0.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 3 0.1940 0.8467 0.000 0.000 0.924 0.076
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.5386 0.4235 0.000 0.632 0.024 0.344
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.3351 0.8845 0.844 0.008 0.000 0.148
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0000 0.8794 1.000 0.000 0.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.3351 0.8845 0.844 0.008 0.000 0.148
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 4 0.6034 0.5968 0.000 0.148 0.164 0.688
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.5386 0.4235 0.000 0.632 0.024 0.344
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 4 0.0000 0.6861 0.000 0.000 0.000 1.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.3351 0.8845 0.844 0.008 0.000 0.148
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.7156 0.4361 0.140 0.492 0.000 0.368
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 3 0.1792 0.8418 0.000 0.000 0.932 0.068
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0000 0.8794 1.000 0.000 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.3024 0.7608 0.000 0.148 0.000 0.852
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.5386 0.4235 0.000 0.632 0.024 0.344
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.7148 0.4406 0.140 0.496 0.000 0.364
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.8794 1.000 0.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 3 0.0000 0.9083 0.000 0.000 1.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.3024 0.8860 0.852 0.000 0.000 0.148
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.3351 0.8845 0.844 0.008 0.000 0.148
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 0.6861 0.000 0.000 0.000 1.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 3 0.0000 0.9083 0.000 0.000 1.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.5386 0.4235 0.000 0.632 0.024 0.344
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.0000 0.8794 1.000 0.000 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.3534 0.8834 0.840 0.008 0.004 0.148
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.8794 1.000 0.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 3 0.0000 0.9083 0.000 0.000 1.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.7096 0.4544 0.140 0.516 0.000 0.344
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.1637 0.8857 0.940 0.000 0.000 0.060
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.5933 0.2444 0.500 0.036 0.000 0.464
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.5386 0.4235 0.000 0.632 0.024 0.344
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.3249 0.5112 0.140 0.852 0.000 0.008
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.7084 0.4578 0.140 0.520 0.000 0.340
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 3 0.0000 0.9083 0.000 0.000 1.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.5386 0.4235 0.000 0.632 0.024 0.344
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.5151 0.5113 0.140 0.760 0.000 0.100
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.0000 0.9083 0.000 0.000 1.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 3 0.0000 0.9083 0.000 0.000 1.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.3024 0.8860 0.852 0.000 0.000 0.148
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.8794 1.000 0.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.0000 0.8794 1.000 0.000 0.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.3813 0.8773 0.828 0.024 0.000 0.148
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 4 0.3024 0.7608 0.000 0.148 0.000 0.852
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.3479 0.8830 0.840 0.012 0.000 0.148
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0469 0.8813 0.988 0.000 0.000 0.012
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.5386 0.4235 0.000 0.632 0.024 0.344
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 4 0.1211 0.7180 0.000 0.040 0.000 0.960
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.0000 0.6861 0.000 0.000 0.000 1.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.4841 0.4927 0.140 0.780 0.000 0.080
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.7156 0.4361 0.140 0.492 0.000 0.368
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 3 0.0000 0.9083 0.000 0.000 1.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.5770 0.4714 0.140 0.712 0.000 0.148
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.8794 1.000 0.000 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 4 0.3024 0.7608 0.000 0.148 0.000 0.852
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.8794 1.000 0.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 4 0.3024 0.7608 0.000 0.148 0.000 0.852
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.4872 0.8402 0.776 0.076 0.000 0.148
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.3024 0.7608 0.000 0.148 0.000 0.852
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.0000 0.8794 1.000 0.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.7045 0.4683 0.140 0.532 0.000 0.328
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 3 0.0000 0.9083 0.000 0.000 1.000 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.7156 0.4361 0.140 0.492 0.000 0.368
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.6979 0.3454 0.000 0.528 0.128 0.344
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.0000 0.9083 0.000 0.000 1.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.3351 0.8845 0.844 0.008 0.000 0.148
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.0592 0.8710 0.984 0.016 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 4 0.3024 0.7608 0.000 0.148 0.000 0.852
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.5386 0.4235 0.000 0.632 0.024 0.344
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.2921 0.5101 0.140 0.860 0.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.3024 0.8860 0.852 0.000 0.000 0.148
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.5770 0.4714 0.140 0.712 0.000 0.148
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.2516 0.884 0.000 0.000 0.860 0.000 0.140
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 5 0.0703 0.949 0.000 0.000 0.024 0.000 0.976
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.1608 0.910 0.928 0.072 0.000 0.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.4182 0.498 0.000 0.000 0.600 0.000 0.400
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.5120 0.547 0.696 0.000 0.164 0.000 0.140
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.0162 0.973 0.996 0.004 0.000 0.000 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 5 0.1393 0.939 0.000 0.012 0.024 0.008 0.956
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0794 0.910 0.000 0.000 0.972 0.000 0.028
#> 5482053D-9F48-4773-B68A-302B3A612503 3 0.0000 0.910 0.000 0.000 1.000 0.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 4 0.1106 0.912 0.000 0.012 0.000 0.964 0.024
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.1270 0.932 0.948 0.052 0.000 0.000 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 5 0.0703 0.949 0.000 0.000 0.024 0.000 0.976
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.910 0.000 0.000 1.000 0.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.0510 0.950 0.016 0.984 0.000 0.000 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 4 0.0404 0.915 0.000 0.012 0.000 0.988 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 5 0.1153 0.944 0.000 0.004 0.024 0.008 0.964
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 0.912 0.000 0.000 0.000 1.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 4 0.2540 0.874 0.000 0.088 0.000 0.888 0.024
#> F325847E-F046-4B67-B01C-16919C401020 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 4 0.3317 0.830 0.112 0.000 0.032 0.848 0.008
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 4 0.2540 0.874 0.000 0.088 0.000 0.888 0.024
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.0000 0.912 0.000 0.000 0.000 1.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 5 0.0703 0.949 0.000 0.000 0.024 0.000 0.976
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.2516 0.884 0.000 0.000 0.860 0.000 0.140
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 5 0.0703 0.949 0.000 0.000 0.024 0.000 0.976
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 4 0.3147 0.816 0.000 0.008 0.024 0.856 0.112
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 4 0.2540 0.874 0.000 0.088 0.000 0.888 0.024
#> 91BA5F90-9174-4533-A050-39A28E34A94D 3 0.0000 0.910 0.000 0.000 1.000 0.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 4 0.0404 0.915 0.000 0.012 0.000 0.988 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 3 0.3388 0.719 0.000 0.000 0.792 0.200 0.008
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.0290 0.910 0.000 0.000 0.000 0.992 0.008
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.2280 0.891 0.000 0.000 0.880 0.000 0.120
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 5 0.0703 0.949 0.000 0.000 0.024 0.000 0.976
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.1544 0.899 0.068 0.932 0.000 0.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.2516 0.884 0.000 0.000 0.860 0.000 0.140
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.0404 0.915 0.000 0.012 0.000 0.988 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 5 0.0703 0.949 0.000 0.000 0.024 0.000 0.976
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.3343 0.737 0.000 0.000 0.172 0.812 0.016
#> CB207A52-09AC-49D3-8240-5840CDFBB154 3 0.2516 0.884 0.000 0.000 0.860 0.000 0.140
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 4 0.4860 0.736 0.000 0.088 0.024 0.756 0.132
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 4 0.0404 0.915 0.000 0.012 0.000 0.988 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.0290 0.911 0.000 0.000 0.992 0.000 0.008
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 5 0.0703 0.949 0.000 0.000 0.024 0.000 0.976
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 4 0.0404 0.915 0.000 0.012 0.000 0.988 0.000
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.4630 0.783 0.116 0.000 0.744 0.000 0.140
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 3 0.0000 0.910 0.000 0.000 1.000 0.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0290 0.970 0.992 0.000 0.000 0.008 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.0290 0.911 0.000 0.000 0.992 0.000 0.008
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.0000 0.910 0.000 0.000 1.000 0.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 3 0.0290 0.911 0.000 0.000 0.992 0.000 0.008
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 4 0.3550 0.756 0.184 0.020 0.000 0.796 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.3913 0.441 0.000 0.676 0.000 0.324 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 4 0.0290 0.910 0.000 0.000 0.000 0.992 0.008
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.2648 0.877 0.000 0.000 0.848 0.000 0.152
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 4 0.2597 0.872 0.000 0.092 0.000 0.884 0.024
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.0000 0.910 0.000 0.000 1.000 0.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0404 0.915 0.000 0.012 0.000 0.988 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 4 0.5215 0.616 0.000 0.088 0.000 0.656 0.256
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 3 0.0000 0.910 0.000 0.000 1.000 0.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.2516 0.884 0.000 0.000 0.860 0.000 0.140
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.2605 0.880 0.000 0.000 0.852 0.000 0.148
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0290 0.910 0.000 0.000 0.000 0.992 0.008
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.910 0.000 0.000 1.000 0.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.2516 0.884 0.000 0.000 0.860 0.000 0.140
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.0000 0.910 0.000 0.000 1.000 0.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 5 0.6161 0.351 0.000 0.088 0.024 0.324 0.564
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.2516 0.884 0.000 0.000 0.860 0.000 0.140
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 3 0.3141 0.869 0.000 0.000 0.832 0.016 0.152
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 5 0.1399 0.934 0.000 0.028 0.020 0.000 0.952
#> 53A96249-66D5-4C26-893B-ADC71481D261 5 0.3033 0.886 0.000 0.064 0.024 0.032 0.880
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 5 0.2707 0.889 0.000 0.080 0.024 0.008 0.888
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.0290 0.911 0.000 0.000 0.992 0.000 0.008
#> C2662596-6E2F-4924-B051-CEA1AC87B197 3 0.0000 0.910 0.000 0.000 1.000 0.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.0880 0.910 0.000 0.000 0.968 0.000 0.032
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 3 0.2648 0.877 0.000 0.000 0.848 0.000 0.152
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 4 0.1106 0.912 0.000 0.012 0.000 0.964 0.024
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 3 0.3215 0.862 0.000 0.000 0.852 0.092 0.056
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 3 0.0290 0.911 0.000 0.000 0.992 0.000 0.008
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 4 0.0000 0.912 0.000 0.000 0.000 1.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.1168 0.898 0.000 0.000 0.032 0.960 0.008
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 5 0.0703 0.949 0.000 0.000 0.024 0.000 0.976
#> 2D962371-EC83-490C-A663-478AF383BC1B 4 0.2597 0.872 0.000 0.092 0.000 0.884 0.024
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 5 0.0703 0.949 0.000 0.000 0.024 0.000 0.976
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 3 0.0000 0.910 0.000 0.000 1.000 0.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 4 0.0404 0.915 0.000 0.012 0.000 0.988 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 3 0.0000 0.910 0.000 0.000 1.000 0.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 4 0.0404 0.915 0.000 0.012 0.000 0.988 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.3109 0.836 0.000 0.000 0.800 0.000 0.200
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0404 0.915 0.000 0.012 0.000 0.988 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.910 0.000 0.000 1.000 0.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 5 0.2824 0.881 0.000 0.088 0.024 0.008 0.880
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 4 0.6256 0.306 0.000 0.088 0.024 0.532 0.356
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0609 0.947 0.020 0.980 0.000 0.000 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.2516 0.884 0.000 0.000 0.860 0.000 0.140
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.2516 0.884 0.000 0.000 0.860 0.000 0.140
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 4 0.1403 0.908 0.000 0.024 0.000 0.952 0.024
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 5 0.0992 0.945 0.000 0.000 0.024 0.008 0.968
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.0290 0.911 0.000 0.000 0.992 0.000 0.008
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 5 0.0703 0.949 0.000 0.000 0.024 0.000 0.976
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.0000 0.961 0.000 0.000 1.000 0.000 0.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 6 0.0000 0.928 0.000 0.000 0.000 0.000 0.000 1.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.2112 0.883 0.896 0.088 0.000 0.000 0.016 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.1501 0.902 0.000 0.000 0.924 0.000 0.000 0.076
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.1957 0.860 0.112 0.000 0.888 0.000 0.000 0.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.0717 0.963 0.976 0.008 0.000 0.000 0.016 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 6 0.0000 0.928 0.000 0.000 0.000 0.000 0.000 1.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.961 0.000 0.000 1.000 0.000 0.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 3 0.0790 0.952 0.000 0.000 0.968 0.000 0.032 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 5 0.2854 0.829 0.000 0.000 0.000 0.208 0.792 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.1838 0.905 0.916 0.068 0.000 0.000 0.016 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 6 0.0000 0.928 0.000 0.000 0.000 0.000 0.000 1.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0632 0.955 0.000 0.000 0.976 0.000 0.024 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.0458 0.973 0.000 0.984 0.000 0.000 0.016 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> F772EA39-E408-4908-BADD-C786D702BF9B 6 0.0000 0.928 0.000 0.000 0.000 0.000 0.000 1.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
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#> B855EF89-1E76-4408-AA65-61A0F0A4F412 6 0.4464 0.647 0.000 0.016 0.000 0.032 0.280 0.672
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#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0458 0.973 0.000 0.984 0.000 0.000 0.016 0.000
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#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 3 0.0790 0.952 0.000 0.000 0.968 0.000 0.032 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.0000 0.961 0.000 0.000 1.000 0.000 0.000 0.000
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#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0790 0.952 0.000 0.000 0.968 0.000 0.032 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0000 0.961 0.000 0.000 1.000 0.000 0.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.0790 0.952 0.000 0.000 0.968 0.000 0.032 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 6 0.3003 0.816 0.000 0.016 0.000 0.000 0.172 0.812
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.961 0.000 0.000 1.000 0.000 0.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 3 0.0000 0.961 0.000 0.000 1.000 0.000 0.000 0.000
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 6 0.0000 0.928 0.000 0.000 0.000 0.000 0.000 1.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 6 0.1265 0.906 0.000 0.008 0.000 0.000 0.044 0.948
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 6 0.0363 0.924 0.000 0.012 0.000 0.000 0.000 0.988
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.0000 0.961 0.000 0.000 1.000 0.000 0.000 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 3 0.0790 0.952 0.000 0.000 0.968 0.000 0.032 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.0000 0.961 0.000 0.000 1.000 0.000 0.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 3 0.0000 0.961 0.000 0.000 1.000 0.000 0.000 0.000
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 5 0.2854 0.829 0.000 0.000 0.000 0.208 0.792 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 3 0.0000 0.961 0.000 0.000 1.000 0.000 0.000 0.000
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 3 0.0146 0.960 0.000 0.000 0.996 0.000 0.004 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 3 0.2969 0.731 0.000 0.000 0.776 0.224 0.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 6 0.0000 0.928 0.000 0.000 0.000 0.000 0.000 1.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 6 0.4464 0.647 0.000 0.016 0.000 0.032 0.280 0.672
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.0458 0.968 0.984 0.000 0.000 0.000 0.016 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 6 0.0000 0.928 0.000 0.000 0.000 0.000 0.000 1.000
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 3 0.0790 0.952 0.000 0.000 0.968 0.000 0.032 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 3 0.0790 0.952 0.000 0.000 0.968 0.000 0.032 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 4 0.2178 0.815 0.000 0.000 0.000 0.868 0.132 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.0547 0.951 0.000 0.000 0.980 0.000 0.000 0.020
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.0363 0.976 0.000 0.000 0.000 0.988 0.012 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0260 0.959 0.000 0.000 0.992 0.000 0.008 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 6 0.0458 0.922 0.000 0.016 0.000 0.000 0.000 0.984
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.0146 0.975 0.996 0.000 0.000 0.000 0.004 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 6 0.3352 0.806 0.000 0.016 0.000 0.012 0.172 0.800
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0458 0.973 0.000 0.984 0.000 0.000 0.016 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0000 0.961 0.000 0.000 1.000 0.000 0.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.961 0.000 0.000 1.000 0.000 0.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 5 0.2793 0.835 0.000 0.000 0.000 0.200 0.800 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0458 0.973 0.000 0.984 0.000 0.000 0.016 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 6 0.0000 0.928 0.000 0.000 0.000 0.000 0.000 1.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.0000 0.961 0.000 0.000 1.000 0.000 0.000 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 6 0.0000 0.928 0.000 0.000 0.000 0.000 0.000 1.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "NMF"]
# you can also extract it by
# res = res_list["ATC:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 18041 rows and 126 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.780 0.872 0.946 0.4500 0.537 0.537
#> 3 3 0.705 0.830 0.927 0.3136 0.713 0.526
#> 4 4 0.555 0.615 0.800 0.2294 0.779 0.486
#> 5 5 0.524 0.496 0.712 0.0576 0.815 0.461
#> 6 6 0.562 0.472 0.696 0.0458 0.854 0.526
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.0000 0.9622 1.000 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.6048 0.8030 0.148 0.852
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.8987 0.000 1.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.0000 0.9622 1.000 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.0000 0.9622 1.000 0.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.0000 0.9622 1.000 0.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.0000 0.9622 1.000 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0000 0.8987 0.000 1.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.0000 0.9622 1.000 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 1 0.0000 0.9622 1.000 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 2 0.0000 0.8987 0.000 1.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.0000 0.9622 1.000 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.6712 0.7793 0.176 0.824
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.9622 1.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.0000 0.9622 1.000 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 1 0.0000 0.9622 1.000 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.8987 0.000 1.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.8909 0.4952 0.692 0.308
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.8987 0.000 1.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.9963 -0.0194 0.536 0.464
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.8987 0.000 1.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 2 0.0000 0.8987 0.000 1.000
#> F325847E-F046-4B67-B01C-16919C401020 1 0.0000 0.9622 1.000 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.0000 0.9622 1.000 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 2 0.0000 0.8987 0.000 1.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.9209 0.4213 0.664 0.336
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 1 0.6148 0.7847 0.848 0.152
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.0000 0.9622 1.000 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.0000 0.9622 1.000 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.0000 0.9622 1.000 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 2 0.9358 0.5404 0.352 0.648
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.9775 0.1875 0.588 0.412
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 2 0.0000 0.8987 0.000 1.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.0000 0.9622 1.000 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 2 0.0000 0.8987 0.000 1.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 1 0.0000 0.9622 1.000 0.000
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.0000 0.9622 1.000 0.000
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 1 0.0000 0.9622 1.000 0.000
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 1 0.0000 0.9622 1.000 0.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 1 0.0000 0.9622 1.000 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.0000 0.9622 1.000 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 1 0.0000 0.9622 1.000 0.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 2 0.9850 0.3670 0.428 0.572
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.0000 0.9622 1.000 0.000
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.0000 0.9622 1.000 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.0000 0.9622 1.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.8713 0.6420 0.292 0.708
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 2 0.2778 0.8733 0.048 0.952
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.0000 0.9622 1.000 0.000
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 2 0.6887 0.7721 0.184 0.816
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0000 0.9622 1.000 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 2 0.1414 0.8892 0.020 0.980
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.0000 0.9622 1.000 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.0000 0.9622 1.000 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.0000 0.9622 1.000 0.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 1 0.0000 0.9622 1.000 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0000 0.9622 1.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.8987 0.000 1.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.0000 0.9622 1.000 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.0000 0.9622 1.000 0.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.0000 0.9622 1.000 0.000
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.0000 0.9622 1.000 0.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.8987 0.000 1.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.0000 0.9622 1.000 0.000
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.0000 0.9622 1.000 0.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.8987 0.000 1.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.0000 0.9622 1.000 0.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 1 0.0000 0.9622 1.000 0.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 2 0.7453 0.7436 0.212 0.788
#> EE16D845-31F2-4178-800B-CA2C358841AD 1 0.6247 0.7890 0.844 0.156
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.0000 0.8987 0.000 1.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 1 0.0000 0.9622 1.000 0.000
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.0000 0.9622 1.000 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 1 0.0000 0.9622 1.000 0.000
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.4562 0.8594 0.904 0.096
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.0000 0.9622 1.000 0.000
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.0000 0.9622 1.000 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.8987 0.000 1.000
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.0000 0.9622 1.000 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.0000 0.9622 1.000 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 1 0.0000 0.9622 1.000 0.000
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.0000 0.9622 1.000 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 2 0.0000 0.8987 0.000 1.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 1 0.0000 0.9622 1.000 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.9522 0.3196 0.628 0.372
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.8987 0.000 1.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0376 0.8971 0.004 0.996
#> 53A96249-66D5-4C26-893B-ADC71481D261 2 0.0000 0.8987 0.000 1.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.0000 0.9622 1.000 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.8987 0.000 1.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.8987 0.000 1.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.0000 0.9622 1.000 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.0000 0.9622 1.000 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 1 0.0000 0.9622 1.000 0.000
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.0000 0.9622 1.000 0.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.0000 0.9622 1.000 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 2 0.9000 0.6045 0.316 0.684
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 2 0.0000 0.8987 0.000 1.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 2 0.9998 0.1645 0.492 0.508
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 1 0.0000 0.9622 1.000 0.000
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.8987 0.000 1.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.0000 0.9622 1.000 0.000
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.0000 0.9622 1.000 0.000
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 2 0.0376 0.8971 0.004 0.996
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.8987 0.000 1.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.0000 0.9622 1.000 0.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 2 0.9427 0.5254 0.360 0.640
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 1 0.0000 0.9622 1.000 0.000
#> A60DC925-7343-496E-900D-0DD81D5C8123 2 0.9795 0.4002 0.416 0.584
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.0000 0.9622 1.000 0.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 2 0.0000 0.8987 0.000 1.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.0672 0.9547 0.992 0.008
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 2 0.8443 0.6707 0.272 0.728
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.0000 0.9622 1.000 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.8987 0.000 1.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.0000 0.9622 1.000 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 2 0.0000 0.8987 0.000 1.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 1 0.3879 0.8869 0.924 0.076
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.0000 0.9622 1.000 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 1 0.0000 0.9622 1.000 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.0000 0.9622 1.000 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 2 0.0000 0.8987 0.000 1.000
#> A608BCEB-2C27-4927-A308-E6975F641722 1 0.8016 0.6479 0.756 0.244
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.8987 0.000 1.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 1 0.0000 0.9622 1.000 0.000
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.9795 0.3982 0.416 0.584
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 3 0.0000 0.9379 0.000 0.000 1.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.4178 0.7297 0.000 0.828 0.172
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0000 0.9168 0.000 1.000 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 3 0.3816 0.7994 0.000 0.148 0.852
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 3 0.0000 0.9379 0.000 0.000 1.000
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 3 0.0000 0.9379 0.000 0.000 1.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 3 0.0000 0.9379 0.000 0.000 1.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0000 0.9168 0.000 1.000 0.000
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.0000 0.9379 0.000 0.000 1.000
#> 5482053D-9F48-4773-B68A-302B3A612503 3 0.0000 0.9379 0.000 0.000 1.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 1 0.0000 0.8239 1.000 0.000 0.000
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 3 0.0000 0.9379 0.000 0.000 1.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.5216 0.6078 0.000 0.740 0.260
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 3 0.0000 0.9379 0.000 0.000 1.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.0000 0.9379 0.000 0.000 1.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.4002 0.7449 0.000 0.840 0.160
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0000 0.9168 0.000 1.000 0.000
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.5968 0.5324 0.636 0.000 0.364
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0000 0.9168 0.000 1.000 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 1 0.0000 0.8239 1.000 0.000 0.000
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0000 0.9168 0.000 1.000 0.000
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 1 0.0000 0.8239 1.000 0.000 0.000
#> F325847E-F046-4B67-B01C-16919C401020 3 0.0000 0.9379 0.000 0.000 1.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 3 0.0000 0.9379 0.000 0.000 1.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 1 0.5678 0.5433 0.684 0.316 0.000
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 1 0.0000 0.8239 1.000 0.000 0.000
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.1163 0.9177 0.028 0.000 0.972
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 3 0.0000 0.9379 0.000 0.000 1.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 3 0.0000 0.9379 0.000 0.000 1.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 3 0.0000 0.9379 0.000 0.000 1.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 3 0.5722 0.5509 0.292 0.004 0.704
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 1 0.5058 0.7319 0.756 0.000 0.244
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 1 0.2261 0.7970 0.932 0.068 0.000
#> 91BA5F90-9174-4533-A050-39A28E34A94D 3 0.0000 0.9379 0.000 0.000 1.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 1 0.0000 0.8239 1.000 0.000 0.000
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 3 0.3752 0.8010 0.144 0.000 0.856
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 1 0.4796 0.7586 0.780 0.000 0.220
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.6140 0.2446 0.404 0.000 0.596
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.0000 0.9379 0.000 0.000 1.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.6307 0.0338 0.000 0.512 0.488
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.0000 0.9379 0.000 0.000 1.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 3 0.0000 0.9379 0.000 0.000 1.000
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.5988 0.3599 0.368 0.000 0.632
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 1 0.4796 0.7580 0.780 0.000 0.220
#> CB207A52-09AC-49D3-8240-5840CDFBB154 3 0.0000 0.9379 0.000 0.000 1.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 3 0.0000 0.9379 0.000 0.000 1.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.5760 0.5079 0.000 0.672 0.328
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 1 0.0000 0.8239 1.000 0.000 0.000
#> 5E343116-414B-41F2-AAEE-A3225450135A 1 0.5706 0.6208 0.680 0.000 0.320
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 1 0.4452 0.7751 0.808 0.000 0.192
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 3 0.0000 0.9379 0.000 0.000 1.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 1 0.1860 0.8224 0.948 0.000 0.052
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 3 0.0000 0.9379 0.000 0.000 1.000
#> AD294665-6F90-459C-90D5-3058F210225D 3 0.0000 0.9379 0.000 0.000 1.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 3 0.0000 0.9379 0.000 0.000 1.000
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 3 0.0000 0.9379 0.000 0.000 1.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 3 0.0000 0.9379 0.000 0.000 1.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0000 0.9168 0.000 1.000 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.5254 0.6160 0.264 0.000 0.736
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.0000 0.9379 0.000 0.000 1.000
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 3 0.1289 0.9147 0.032 0.000 0.968
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 3 0.0000 0.9379 0.000 0.000 1.000
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.0000 0.9168 0.000 1.000 0.000
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 3 0.2165 0.8867 0.064 0.000 0.936
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 3 0.0000 0.9379 0.000 0.000 1.000
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.0000 0.9168 0.000 1.000 0.000
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 3 0.0000 0.9379 0.000 0.000 1.000
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.0000 0.9379 0.000 0.000 1.000
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 1 0.0000 0.8239 1.000 0.000 0.000
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0237 0.9144 0.000 0.996 0.004
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.1860 0.8689 0.052 0.948 0.000
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 3 0.4399 0.7433 0.188 0.000 0.812
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 3 0.0000 0.9379 0.000 0.000 1.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.1411 0.9114 0.036 0.000 0.964
#> 392897E4-6009-422C-B461-649F4DDF260C 1 0.5678 0.6275 0.684 0.000 0.316
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 1 0.4796 0.7580 0.780 0.000 0.220
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 3 0.0000 0.9379 0.000 0.000 1.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0000 0.9168 0.000 1.000 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.0000 0.9379 0.000 0.000 1.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.0000 0.9379 0.000 0.000 1.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.0237 0.9351 0.004 0.000 0.996
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 3 0.0000 0.9379 0.000 0.000 1.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 1 0.0000 0.8239 1.000 0.000 0.000
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.0000 0.9379 0.000 0.000 1.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 1 0.2165 0.8203 0.936 0.000 0.064
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0000 0.9168 0.000 1.000 0.000
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.0237 0.9143 0.000 0.996 0.004
#> 53A96249-66D5-4C26-893B-ADC71481D261 1 0.0000 0.8239 1.000 0.000 0.000
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 3 0.0000 0.9379 0.000 0.000 1.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0000 0.9168 0.000 1.000 0.000
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.0000 0.9168 0.000 1.000 0.000
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 3 0.0000 0.9379 0.000 0.000 1.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 3 0.0000 0.9379 0.000 0.000 1.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.6302 -0.0680 0.480 0.000 0.520
#> C2662596-6E2F-4924-B051-CEA1AC87B197 3 0.0000 0.9379 0.000 0.000 1.000
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 3 0.0000 0.9379 0.000 0.000 1.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 1 0.3267 0.8047 0.884 0.000 0.116
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 1 0.0000 0.8239 1.000 0.000 0.000
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 1 0.0747 0.8250 0.984 0.000 0.016
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 3 0.5254 0.6159 0.264 0.000 0.736
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0000 0.9168 0.000 1.000 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 1 0.5327 0.6956 0.728 0.000 0.272
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 1 0.6095 0.4656 0.608 0.000 0.392
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 1 0.0000 0.8239 1.000 0.000 0.000
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.0000 0.9168 0.000 1.000 0.000
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 3 0.0000 0.9379 0.000 0.000 1.000
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 1 0.6307 0.1613 0.512 0.000 0.488
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 3 0.4452 0.7372 0.192 0.000 0.808
#> A60DC925-7343-496E-900D-0DD81D5C8123 1 0.4504 0.7735 0.804 0.000 0.196
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 3 0.0000 0.9379 0.000 0.000 1.000
#> 6969B6B2-7616-4664-9696-C4DACD10537B 1 0.3412 0.7609 0.876 0.124 0.000
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.4121 0.7706 0.168 0.000 0.832
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 1 0.0000 0.8239 1.000 0.000 0.000
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.0000 0.9379 0.000 0.000 1.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0000 0.9168 0.000 1.000 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 3 0.0000 0.9379 0.000 0.000 1.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 1 0.0000 0.8239 1.000 0.000 0.000
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0237 0.9144 0.000 0.996 0.004
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 3 0.0000 0.9379 0.000 0.000 1.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.0000 0.9379 0.000 0.000 1.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.0000 0.9379 0.000 0.000 1.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 1 0.1529 0.8096 0.960 0.040 0.000
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0000 0.9168 0.000 1.000 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0000 0.9168 0.000 1.000 0.000
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.5216 0.6234 0.260 0.000 0.740
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 3 0.3947 0.8478 0.076 0.040 0.884
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.4994 0.33292 0.520 0.000 0.480 0.000
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.1211 0.86657 0.040 0.960 0.000 0.000
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0336 0.88822 0.000 0.992 0.008 0.000
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.1890 0.74153 0.936 0.056 0.008 0.000
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.3743 0.78673 0.824 0.000 0.160 0.016
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.4888 0.49845 0.588 0.000 0.412 0.000
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.4008 0.73343 0.756 0.000 0.244 0.000
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.0188 0.88877 0.000 0.996 0.000 0.004
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 3 0.2814 0.68664 0.132 0.000 0.868 0.000
#> 5482053D-9F48-4773-B68A-302B3A612503 3 0.2921 0.67753 0.140 0.000 0.860 0.000
#> A31D342D-C67C-428B-BAED-C6E844277A09 4 0.4164 0.64259 0.000 0.000 0.264 0.736
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.2530 0.80386 0.896 0.004 0.100 0.000
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 2 0.1356 0.87120 0.008 0.960 0.032 0.000
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.0000 0.77850 1.000 0.000 0.000 0.000
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 3 0.2281 0.70568 0.096 0.000 0.904 0.000
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.0524 0.88645 0.008 0.988 0.004 0.000
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0188 0.88873 0.000 0.996 0.000 0.004
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 1 0.5714 0.10469 0.552 0.004 0.020 0.424
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0188 0.88874 0.000 0.996 0.004 0.000
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.4564 0.58271 0.000 0.000 0.328 0.672
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.0817 0.88260 0.000 0.976 0.000 0.024
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 4 0.1792 0.69570 0.000 0.000 0.068 0.932
#> F325847E-F046-4B67-B01C-16919C401020 1 0.4877 0.51167 0.592 0.000 0.408 0.000
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.3569 0.77204 0.804 0.000 0.196 0.000
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 4 0.2859 0.62901 0.000 0.112 0.008 0.880
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.4564 0.58520 0.000 0.000 0.328 0.672
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 3 0.1520 0.68457 0.024 0.020 0.956 0.000
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.1474 0.79779 0.948 0.000 0.052 0.000
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.4008 0.73083 0.756 0.000 0.244 0.000
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.3311 0.78467 0.828 0.000 0.172 0.000
#> A8E48877-F8AB-44DD-A18B-194D87C44931 4 0.8832 0.23753 0.268 0.336 0.044 0.352
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 4 0.6273 0.57258 0.100 0.000 0.264 0.636
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 4 0.2708 0.65525 0.040 0.028 0.016 0.916
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.2345 0.80330 0.900 0.000 0.100 0.000
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 4 0.2973 0.69521 0.000 0.000 0.144 0.856
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 3 0.7458 0.00517 0.176 0.000 0.444 0.380
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 3 0.2081 0.61945 0.000 0.000 0.916 0.084
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.6229 -0.03636 0.056 0.000 0.528 0.416
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 3 0.4841 0.61079 0.080 0.140 0.780 0.000
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 2 0.4401 0.59974 0.004 0.724 0.272 0.000
#> 604C06E9-A00E-435E-847A-3992922A5C56 3 0.2469 0.70184 0.108 0.000 0.892 0.000
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 3 0.6214 -0.19799 0.472 0.000 0.476 0.052
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.2019 0.67017 0.004 0.032 0.940 0.024
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 3 0.5290 -0.19118 0.008 0.000 0.516 0.476
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.2921 0.79644 0.860 0.000 0.140 0.000
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.0000 0.77850 1.000 0.000 0.000 0.000
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.4164 0.60506 0.264 0.736 0.000 0.000
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 4 0.3907 0.66348 0.000 0.000 0.232 0.768
#> 5E343116-414B-41F2-AAEE-A3225450135A 4 0.5848 0.56933 0.228 0.000 0.088 0.684
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 4 0.2521 0.68918 0.064 0.000 0.024 0.912
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.0707 0.78698 0.980 0.000 0.020 0.000
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 4 0.4173 0.58764 0.172 0.004 0.020 0.804
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.2216 0.80407 0.908 0.000 0.092 0.000
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.2814 0.79862 0.868 0.000 0.132 0.000
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.0779 0.76753 0.980 0.000 0.016 0.004
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 3 0.2408 0.70419 0.104 0.000 0.896 0.000
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.0000 0.77850 1.000 0.000 0.000 0.000
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0336 0.88822 0.000 0.992 0.008 0.000
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 1 0.5859 -0.01670 0.496 0.000 0.032 0.472
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 3 0.5636 0.36657 0.308 0.000 0.648 0.044
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.5143 0.58757 0.708 0.000 0.036 0.256
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.0927 0.76504 0.976 0.000 0.016 0.008
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.3583 0.79090 0.000 0.816 0.004 0.180
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 1 0.5685 0.05005 0.516 0.000 0.024 0.460
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.4662 0.76485 0.796 0.000 0.112 0.092
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.4624 0.62454 0.000 0.660 0.000 0.340
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.1406 0.75419 0.960 0.000 0.016 0.024
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.2053 0.70409 0.072 0.000 0.924 0.004
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.4543 0.59287 0.000 0.000 0.324 0.676
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0188 0.88880 0.004 0.996 0.000 0.000
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.4605 0.62286 0.000 0.664 0.000 0.336
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 3 0.3441 0.61526 0.024 0.000 0.856 0.120
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.2149 0.80372 0.912 0.000 0.088 0.000
#> 721CDBE6-FC85-4C30-B23E-28407340286F 3 0.1109 0.68663 0.028 0.000 0.968 0.004
#> 392897E4-6009-422C-B461-649F4DDF260C 3 0.1978 0.63977 0.004 0.000 0.928 0.068
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 3 0.5290 0.04363 0.012 0.000 0.584 0.404
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.4477 0.65939 0.688 0.000 0.312 0.000
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0336 0.88822 0.000 0.992 0.008 0.000
#> E5557F52-015D-49DC-9E23-989FC259976F 3 0.2216 0.70516 0.092 0.000 0.908 0.000
#> F3135F5E-2E90-4923-B634-E994563D17B7 3 0.4985 -0.22182 0.468 0.000 0.532 0.000
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.1978 0.70320 0.068 0.000 0.928 0.004
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.2149 0.80372 0.912 0.000 0.088 0.000
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 3 0.4761 0.13256 0.000 0.000 0.628 0.372
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 3 0.2589 0.69790 0.116 0.000 0.884 0.000
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.4633 0.68150 0.048 0.000 0.172 0.780
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0188 0.88873 0.000 0.996 0.000 0.004
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.3356 0.75169 0.000 0.824 0.176 0.000
#> 53A96249-66D5-4C26-893B-ADC71481D261 3 0.7717 -0.27354 0.000 0.224 0.392 0.384
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.1716 0.80065 0.936 0.000 0.064 0.000
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0336 0.88792 0.000 0.992 0.000 0.008
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.4679 0.61054 0.000 0.648 0.000 0.352
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.4193 0.71082 0.732 0.000 0.268 0.000
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.3610 0.76800 0.800 0.000 0.200 0.000
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.5517 0.30867 0.036 0.000 0.648 0.316
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.2867 0.80300 0.884 0.000 0.104 0.012
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.4967 0.40618 0.548 0.000 0.452 0.000
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 4 0.2222 0.67266 0.060 0.000 0.016 0.924
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 4 0.3266 0.68871 0.000 0.000 0.168 0.832
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 4 0.1629 0.69521 0.024 0.000 0.024 0.952
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 4 0.6897 0.37589 0.332 0.000 0.124 0.544
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.0336 0.88822 0.000 0.992 0.008 0.000
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 4 0.4983 0.56552 0.272 0.000 0.024 0.704
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 3 0.5268 0.06504 0.012 0.000 0.592 0.396
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 4 0.5848 0.47519 0.000 0.040 0.376 0.584
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.4222 0.70802 0.000 0.728 0.000 0.272
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.0779 0.76753 0.980 0.000 0.016 0.004
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.8865 -0.05244 0.060 0.212 0.404 0.324
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 3 0.2928 0.68065 0.052 0.000 0.896 0.052
#> A60DC925-7343-496E-900D-0DD81D5C8123 4 0.5558 0.38316 0.036 0.000 0.324 0.640
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.5122 0.74659 0.756 0.000 0.164 0.080
#> 6969B6B2-7616-4664-9696-C4DACD10537B 4 0.1118 0.67346 0.000 0.036 0.000 0.964
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 1 0.7053 0.23780 0.512 0.000 0.132 0.356
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.4382 0.61940 0.000 0.000 0.296 0.704
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 3 0.2281 0.70488 0.096 0.000 0.904 0.000
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0188 0.88874 0.000 0.996 0.004 0.000
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.1637 0.79976 0.940 0.000 0.060 0.000
#> 2629FEE3-A203-4411-8A70-02A796C9505C 4 0.6901 0.35362 0.000 0.108 0.404 0.488
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0188 0.88891 0.004 0.996 0.000 0.000
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.3219 0.78762 0.836 0.000 0.164 0.000
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.2281 0.70544 0.096 0.000 0.904 0.000
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 3 0.2760 0.69145 0.128 0.000 0.872 0.000
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 4 0.1584 0.67741 0.000 0.036 0.012 0.952
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0188 0.88874 0.000 0.996 0.004 0.000
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.0188 0.88873 0.000 0.996 0.000 0.004
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 4 0.6376 0.15490 0.432 0.000 0.064 0.504
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.7157 0.51905 0.184 0.652 0.108 0.056
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 1 0.5688 0.6046 0.660 0.000 0.136 0.012 0.192
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.4344 0.7398 0.096 0.804 0.044 0.000 0.056
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0693 0.8605 0.000 0.980 0.000 0.008 0.012
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 1 0.3528 0.6617 0.848 0.052 0.000 0.016 0.084
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 1 0.2409 0.7374 0.908 0.000 0.008 0.028 0.056
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 1 0.4613 0.6945 0.756 0.000 0.004 0.120 0.120
#> 3067683D-43D4-4F6D-9338-17F6A75E4B25 1 0.3415 0.7282 0.840 0.004 0.004 0.028 0.124
#> 2CE91B81-7CBE-4698-AFEE-6A154313D231 2 0.3563 0.7049 0.000 0.780 0.208 0.000 0.012
#> A639CF4F-4591-4337-A12E-BED71EDDA10B 1 0.7628 0.3662 0.484 0.000 0.224 0.092 0.200
#> 5482053D-9F48-4773-B68A-302B3A612503 4 0.8167 0.0243 0.300 0.000 0.216 0.364 0.120
#> A31D342D-C67C-428B-BAED-C6E844277A09 3 0.2411 0.4866 0.000 0.000 0.884 0.108 0.008
#> 0E9C5985-9AE0-4098-A076-6FFBBDF05110 1 0.2932 0.7189 0.864 0.020 0.000 0.004 0.112
#> 08CCF8A0-15B7-4A13-BC43-6B3B3E2DDE95 3 0.7613 0.2126 0.084 0.368 0.400 0.000 0.148
#> 10577D5B-AD56-403F-A562-73A9ACF2045B 1 0.2293 0.6901 0.900 0.000 0.000 0.016 0.084
#> C940D443-5DDA-4403-868B-7AA6B9A50FC4 1 0.7898 0.1010 0.384 0.000 0.348 0.116 0.152
#> 89D4D945-A717-495F-B253-F5A17CF5B9FA 2 0.1018 0.8543 0.016 0.968 0.000 0.000 0.016
#> DC55EE78-203F-4092-9B83-14B1A529194B 2 0.0290 0.8608 0.000 0.992 0.000 0.000 0.008
#> 8F7368BE-EB41-4192-89AA-9E0428C08851 5 0.5025 0.5125 0.288 0.000 0.020 0.028 0.664
#> F772EA39-E408-4908-BADD-C786D702BF9B 2 0.0451 0.8612 0.000 0.988 0.004 0.000 0.008
#> FD693D10-3ADA-4028-8392-41D2F0296F7E 4 0.5773 0.3212 0.000 0.000 0.216 0.616 0.168
#> 84F16966-7640-49F9-95D1-7648FF74DCC9 2 0.2189 0.8272 0.000 0.904 0.012 0.000 0.084
#> D26DAA2F-AE6A-42E1-9F1F-01943B99785F 3 0.3276 0.3826 0.000 0.000 0.836 0.032 0.132
#> F325847E-F046-4B67-B01C-16919C401020 1 0.5192 0.6499 0.676 0.000 0.004 0.084 0.236
#> 19EB2B10-2529-4A94-8FAE-1CE371A602D9 1 0.3268 0.7373 0.868 0.000 0.032 0.032 0.068
#> B94B9CCF-5FB8-44AE-8D9C-A194C6801A27 3 0.6962 -0.1998 0.000 0.136 0.432 0.036 0.396
#> D2678E70-542A-4AB2-B881-12D66DBA44F5 4 0.5814 0.3040 0.000 0.000 0.180 0.612 0.208
#> 4961CA2A-70CD-42AB-A676-4A98C85F449F 4 0.8558 0.2218 0.044 0.068 0.228 0.392 0.268
#> 5AA74C5C-2AD1-4D59-A030-E964EB199581 1 0.1626 0.7139 0.940 0.000 0.000 0.016 0.044
#> F9E11A1B-BD93-438F-9670-6FB7DFF9E910 1 0.4146 0.7232 0.820 0.000 0.048 0.068 0.064
#> FB78CA5A-C8B9-42AF-9DAE-799CAB280B2E 1 0.2628 0.7299 0.884 0.000 0.000 0.088 0.028
#> A8E48877-F8AB-44DD-A18B-194D87C44931 3 0.6167 0.4016 0.240 0.148 0.600 0.004 0.008
#> CA50C495-F37E-4743-867D-FAF2DCC3376A 3 0.3001 0.5239 0.052 0.000 0.884 0.032 0.032
#> 37342369-EC22-4904-8CCD-A0DC6BD8D183 3 0.5690 -0.1899 0.000 0.024 0.508 0.036 0.432
#> 91BA5F90-9174-4533-A050-39A28E34A94D 1 0.1806 0.7301 0.940 0.000 0.016 0.016 0.028
#> 5BC371AC-1915-44E9-A114-2963E131EC8D 3 0.5013 0.2976 0.000 0.000 0.700 0.108 0.192
#> 74A6C31A-7F21-45AF-A170-18C326D2AE69 4 0.4038 0.5034 0.088 0.000 0.088 0.812 0.012
#> 984F27EF-D4D7-4E68-BD64-776FDFC04D07 4 0.1377 0.4995 0.020 0.000 0.004 0.956 0.020
#> B05701C5-8C44-4FD1-94C9-FC0255A2EA24 3 0.4590 0.4799 0.088 0.000 0.776 0.116 0.020
#> 91E4119C-2CE6-4447-A125-6A4F403A89E6 1 0.9057 0.1561 0.356 0.164 0.176 0.044 0.260
#> 1570FCE7-F1B4-4BDF-A398-355EDF030864 4 0.5619 0.2719 0.032 0.288 0.000 0.632 0.048
#> 604C06E9-A00E-435E-847A-3992922A5C56 1 0.8071 0.2527 0.424 0.000 0.248 0.188 0.140
#> 455C9007-6FF4-4D63-83FA-4915F0331F9A 4 0.4134 0.3603 0.044 0.000 0.000 0.760 0.196
#> FB8BD3CF-D2EC-47B6-B67F-65ADC1C3A6A7 3 0.8561 0.1367 0.044 0.092 0.436 0.200 0.228
#> 9DC7443A-3C8F-4025-8312-3C98BF28D736 4 0.4141 0.4671 0.016 0.000 0.196 0.768 0.020
#> CB207A52-09AC-49D3-8240-5840CDFBB154 1 0.2206 0.7341 0.912 0.000 0.004 0.016 0.068
#> 2A335049-AD9F-4B32-92B7-69B04B0CF2BA 1 0.2408 0.6867 0.892 0.000 0.000 0.016 0.092
#> 6ACA6293-371E-428D-BBAE-ABFD410C886F 2 0.3123 0.6918 0.160 0.828 0.000 0.000 0.012
#> E8929929-73F9-4DB7-ABBA-0852BEFFFF7E 3 0.3012 0.4728 0.000 0.000 0.860 0.104 0.036
#> 5E343116-414B-41F2-AAEE-A3225450135A 3 0.3895 0.4984 0.164 0.000 0.796 0.032 0.008
#> 0A39073C-157C-48A1-B125-A6A04CB738DA 3 0.4201 0.4432 0.096 0.000 0.800 0.012 0.092
#> 300D78E6-1C7E-4114-80EA-9204A6818B9A 1 0.2248 0.6875 0.900 0.000 0.000 0.012 0.088
#> DAAF55AA-ED48-4221-9CD6-D1DEB6376017 5 0.7063 0.5817 0.064 0.032 0.140 0.144 0.620
#> D76FCF4A-4ACF-41EF-A120-64136D6C845E 1 0.1041 0.7291 0.964 0.000 0.000 0.004 0.032
#> AD294665-6F90-459C-90D5-3058F210225D 1 0.2116 0.7349 0.912 0.000 0.004 0.008 0.076
#> 92E8AD7A-1084-44C8-BDC0-FE4E47B6143F 1 0.3421 0.5889 0.788 0.000 0.000 0.008 0.204
#> 5644A861-3C59-486D-8FBE-4DF6A3B19558 4 0.3905 0.5011 0.088 0.000 0.060 0.828 0.024
#> 1BF8AAE7-B771-4CF2-8B1C-D2BEB5E6579E 1 0.3562 0.5702 0.788 0.000 0.000 0.016 0.196
#> A54731AE-FC40-407F-8D10-67DDC122237D 2 0.0510 0.8576 0.000 0.984 0.000 0.000 0.016
#> 179DC906-5654-4CBA-9C27-C9560B5F12DE 3 0.4213 0.4267 0.308 0.000 0.680 0.012 0.000
#> 979B9A2B-2D81-47C3-A553-9B9441CAAE47 1 0.6803 0.3703 0.540 0.000 0.300 0.064 0.096
#> D69BD86A-08FB-49DA-9084-2725F6C9195F 1 0.4460 0.6304 0.768 0.000 0.160 0.012 0.060
#> 84611033-BCF7-49D7-9B91-DA29B62AC8D3 1 0.3757 0.5701 0.772 0.000 0.000 0.020 0.208
#> 8AA1DA3E-8C00-4653-AA33-EA70531C1E50 2 0.4201 0.7149 0.000 0.752 0.044 0.000 0.204
#> CEBE9594-0F19-46B4-AF7D-F8DF33E00AFB 4 0.6406 -0.1032 0.136 0.000 0.008 0.476 0.380
#> C68E82D2-2BD3-41E9-92D7-D4C06E1953B2 1 0.3299 0.6967 0.828 0.000 0.152 0.004 0.016
#> B855EF89-1E76-4408-AA65-61A0F0A4F412 2 0.6575 0.4621 0.000 0.572 0.148 0.032 0.248
#> 4488EFB3-5B01-41E3-B57E-8E4F607CF448 1 0.4227 0.4271 0.692 0.000 0.000 0.016 0.292
#> C2BD8440-CAC6-4FE5-8EBB-5E6AE308D52F 3 0.8108 0.0774 0.208 0.000 0.400 0.268 0.124
#> E0E50F10-1FED-41C1-84DB-81A46F25D7E9 4 0.5870 0.2187 0.000 0.000 0.136 0.580 0.284
#> EE16D845-31F2-4178-800B-CA2C358841AD 2 0.0609 0.8604 0.000 0.980 0.000 0.000 0.020
#> 169828CF-5693-4A46-B5D7-E45CBA9DF317 2 0.6672 0.3135 0.000 0.500 0.320 0.016 0.164
#> 51077BA3-AEE0-4BD4-A1B1-1B0A811642A1 4 0.5222 0.3301 0.028 0.000 0.356 0.600 0.016
#> D2F4E240-C44C-4CF7-8016-6CACD370D093 1 0.1357 0.7163 0.948 0.000 0.000 0.004 0.048
#> 721CDBE6-FC85-4C30-B23E-28407340286F 4 0.7119 0.1535 0.048 0.000 0.352 0.460 0.140
#> 392897E4-6009-422C-B461-649F4DDF260C 4 0.6017 0.3232 0.004 0.000 0.292 0.572 0.132
#> 617E13D2-6924-45F8-A8DE-BE21B718F822 4 0.3458 0.4381 0.016 0.000 0.024 0.840 0.120
#> 5746C00F-9CBB-46B7-83FD-90B2AB3F507B 1 0.3533 0.7268 0.836 0.000 0.004 0.056 0.104
#> 982B4344-A223-4D1F-9485-2E56F9FD45C0 2 0.0510 0.8576 0.000 0.984 0.000 0.000 0.016
#> E5557F52-015D-49DC-9E23-989FC259976F 1 0.8032 0.2475 0.424 0.000 0.268 0.160 0.148
#> F3135F5E-2E90-4923-B634-E994563D17B7 1 0.5155 0.6651 0.700 0.000 0.008 0.092 0.200
#> D1ED15A5-9802-4314-B556-E89EB772D1F0 3 0.7790 0.1164 0.148 0.000 0.456 0.272 0.124
#> 222B06E3-FCFB-4104-92C3-D73BC31854D4 1 0.2278 0.7147 0.908 0.000 0.000 0.032 0.060
#> 4C810FFA-ED07-4F4C-9F81-B8F1CF4956F7 4 0.3438 0.4921 0.000 0.000 0.172 0.808 0.020
#> 9A608964-ED12-4E6E-9D3A-430F59FFF65B 4 0.7843 0.2599 0.192 0.000 0.140 0.476 0.192
#> 4087357F-C17A-4992-A8AB-41ACA2F72001 4 0.6477 0.1145 0.000 0.000 0.340 0.464 0.196
#> B3F013A5-BCB8-4CE0-86B2-634EE180AA6E 2 0.0290 0.8608 0.000 0.992 0.000 0.000 0.008
#> 322AF320-1379-4F51-AFDC-5292A060CD52 2 0.2707 0.7886 0.000 0.876 0.000 0.024 0.100
#> 53A96249-66D5-4C26-893B-ADC71481D261 3 0.6708 0.1283 0.000 0.384 0.408 0.204 0.004
#> 1AB7A6F2-14BD-447C-B2E3-DEB0CE56B209 1 0.1892 0.7031 0.916 0.000 0.000 0.004 0.080
#> 8B4BCDA0-6787-4A55-99F7-AAF22AF85BA6 2 0.0609 0.8599 0.000 0.980 0.000 0.000 0.020
#> 5D9D9E08-2C2C-414E-9547-62799F90D543 2 0.5558 0.5422 0.000 0.620 0.112 0.000 0.268
#> C345CD17-E4F4-41D5-9891-FEFB19342C52 1 0.3876 0.7196 0.812 0.000 0.004 0.068 0.116
#> 1AF8FDE1-1A74-41F6-A1C5-4952CDFB7D3F 1 0.3248 0.7314 0.856 0.000 0.004 0.052 0.088
#> BC3405FF-0660-4B2B-8DC1-5F34D3133078 3 0.4629 0.4714 0.076 0.000 0.780 0.112 0.032
#> C2662596-6E2F-4924-B051-CEA1AC87B197 1 0.1885 0.7298 0.936 0.000 0.032 0.020 0.012
#> 39AE85F7-49FB-4438-BD41-6AC812FA1C72 1 0.4933 0.6941 0.740 0.000 0.036 0.048 0.176
#> 6FF266DB-3F08-43F2-8F6F-679F805B80B8 5 0.6296 0.4367 0.108 0.000 0.324 0.020 0.548
#> B03B7B81-BBD6-4194-BC5E-6EDF0D3F015A 3 0.2300 0.4854 0.000 0.000 0.908 0.052 0.040
#> C7617D56-F13C-4C43-906C-BD458C5DC4CD 3 0.3366 0.4453 0.032 0.000 0.844 0.008 0.116
#> 09420F8B-7A71-4B32-8388-4767670F1FEB 3 0.4587 0.4479 0.276 0.000 0.692 0.024 0.008
#> 6AF47534-74FF-4128-865B-4E8EE1FFB469 2 0.1410 0.8389 0.000 0.940 0.000 0.000 0.060
#> 8FF9E94A-2ED2-4727-947F-D524D7ECE815 5 0.7029 0.3048 0.096 0.000 0.076 0.324 0.504
#> A390E20D-03F9-40E4-A132-0FA5C2BEDB63 4 0.2139 0.5173 0.012 0.000 0.056 0.920 0.012
#> A489CCCA-1374-4071-80CE-05B83C9A0D5E 3 0.2131 0.5126 0.000 0.008 0.920 0.056 0.016
#> 2D962371-EC83-490C-A663-478AF383BC1B 2 0.5565 0.5913 0.000 0.652 0.092 0.012 0.244
#> D91B31A1-EE71-4726-B94C-0CC2815E9D4E 1 0.2873 0.6636 0.856 0.000 0.000 0.016 0.128
#> E0123C5C-E1D1-4162-9895-CC8B01949D84 3 0.7390 0.2207 0.064 0.396 0.428 0.100 0.012
#> EC73959A-2728-49FE-B72A-790BB14F4CBF 4 0.4727 0.5170 0.064 0.000 0.144 0.764 0.028
#> A60DC925-7343-496E-900D-0DD81D5C8123 4 0.4737 0.0959 0.004 0.000 0.016 0.600 0.380
#> 659B64DB-F4A5-43BD-811B-05004CB49D99 1 0.4350 0.7022 0.792 0.000 0.104 0.088 0.016
#> 6969B6B2-7616-4664-9696-C4DACD10537B 3 0.5918 -0.1342 0.000 0.040 0.528 0.036 0.396
#> 2F6392DE-0D54-4768-B062-907C81E5B0CC 3 0.5512 0.2593 0.384 0.000 0.560 0.016 0.040
#> C74BE8C5-BA6D-4596-9D67-3C731799F999 4 0.6547 0.1268 0.000 0.000 0.232 0.472 0.296
#> 79A7647F-BDBA-45A2-B207-ABF788F6CC95 1 0.8352 0.0925 0.348 0.000 0.264 0.240 0.148
#> D47D0433-2313-4A2F-B268-5AD293D7534E 2 0.0451 0.8612 0.000 0.988 0.004 0.000 0.008
#> C5058B93-C1DA-43B9-9951-B23A9810AA6E 1 0.1364 0.7141 0.952 0.000 0.000 0.012 0.036
#> 2629FEE3-A203-4411-8A70-02A796C9505C 3 0.4191 0.4881 0.000 0.084 0.804 0.096 0.016
#> 1AF329E4-11D4-4CFC-801F-C24A1EA33102 2 0.0451 0.8596 0.008 0.988 0.000 0.000 0.004
#> D453BEF8-3F18-4B89-BA42-CE74EB105032 1 0.2929 0.7341 0.876 0.000 0.004 0.044 0.076
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.7773 -0.0699 0.344 0.004 0.348 0.048 0.256
#> BCAB1918-5FA9-4CBD-85CB-008743FEA2CC 1 0.7813 0.3262 0.468 0.000 0.240 0.128 0.164
#> 9A5432D3-19EE-47B4-BD88-698DEC75A5E9 3 0.6575 -0.1332 0.000 0.088 0.496 0.040 0.376
#> A608BCEB-2C27-4927-A308-E6975F641722 2 0.0794 0.8535 0.000 0.972 0.000 0.000 0.028
#> E4752275-7BF6-4C1E-8A45-C7D571ED85AD 2 0.1012 0.8572 0.000 0.968 0.020 0.000 0.012
#> FDEC1714-C02D-4AB7-AE82-789E9D709EDE 3 0.4260 0.4226 0.308 0.000 0.680 0.008 0.004
#> 33737781-8638-4FA2-AD4C-E888BB9343D8 1 0.8120 -0.1374 0.344 0.276 0.296 0.004 0.080
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DC1F29A2-39DE-44AD-936A-54B2B32D6370 5 0.4792 0.407526 0.016 0.000 0.284 0.000 0.648 0.052
#> FA3C3413-FA8C-4A29-B9CA-5BC3A56274A8 2 0.5806 0.508814 0.136 0.644 0.008 0.000 0.160 0.052
#> B9B72975-8134-475C-BC0B-0CFDB24F636A 2 0.0748 0.768823 0.004 0.976 0.000 0.016 0.000 0.004
#> F44DB757-4D00-4BFC-99C5-AA82F99C5BDA 5 0.4638 0.495862 0.000 0.096 0.000 0.000 0.672 0.232
#> 3BD942BB-A3A7-4993-B3F1-B8641626B773 5 0.2332 0.691923 0.020 0.000 0.036 0.004 0.908 0.032
#> 431F73A2-2AB3-4101-B610-7FC02A7097EC 5 0.5024 0.545062 0.000 0.000 0.164 0.016 0.680 0.140
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#> D453BEF8-3F18-4B89-BA42-CE74EB105032 5 0.3638 0.660623 0.008 0.000 0.036 0.000 0.784 0.172
#> B12A4446-2310-4139-897F-CA030478CBD5 3 0.5423 0.334312 0.016 0.000 0.540 0.004 0.372 0.068
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#> 33737781-8638-4FA2-AD4C-E888BB9343D8 2 0.7563 0.000802 0.084 0.392 0.124 0.000 0.348 0.052
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
sessionInfo()
#> R version 3.6.0 (2019-04-26)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#>
#> Matrix products: default
#> BLAS: /usr/lib64/libblas.so.3.4.2
#> LAPACK: /usr/lib64/liblapack.so.3.4.2
#>
#> locale:
#> [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8
#> [4] LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
#> [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] grid stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] genefilter_1.66.0 ComplexHeatmap_2.3.1 markdown_1.1 knitr_1.26
#> [5] GetoptLong_0.1.7 cola_1.3.2
#>
#> loaded via a namespace (and not attached):
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