Date: 2019-12-26 01:40:07 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 17548 rows and 122 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] 17548 122
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 | ||
|---|---|---|---|---|---|---|
| SD:skmeans | 2 | 1.000 | 0.992 | 0.996 | ** | |
| SD:mclust | 2 | 1.000 | 0.996 | 0.994 | ** | |
| SD:NMF | 2 | 1.000 | 0.973 | 0.988 | ** | |
| MAD:skmeans | 2 | 1.000 | 0.979 | 0.991 | ** | |
| MAD:mclust | 2 | 1.000 | 0.983 | 0.979 | ** | |
| MAD:NMF | 2 | 1.000 | 0.976 | 0.990 | ** | |
| ATC:pam | 2 | 1.000 | 0.978 | 0.990 | ** | |
| ATC:NMF | 3 | 1.000 | 0.962 | 0.985 | ** | 2 |
| ATC:kmeans | 3 | 1.000 | 0.933 | 0.975 | ** | 2 |
| MAD:kmeans | 2 | 0.949 | 0.963 | 0.983 | * | |
| ATC:skmeans | 4 | 0.949 | 0.915 | 0.960 | * | 2,3 |
| CV:skmeans | 6 | 0.915 | 0.877 | 0.926 | * | 5 |
| SD:kmeans | 2 | 0.898 | 0.906 | 0.963 | ||
| CV:mclust | 6 | 0.885 | 0.850 | 0.922 | ||
| CV:pam | 4 | 0.878 | 0.897 | 0.953 | ||
| MAD:pam | 2 | 0.848 | 0.945 | 0.970 | ||
| CV:NMF | 2 | 0.753 | 0.882 | 0.948 | ||
| SD:hclust | 4 | 0.749 | 0.766 | 0.851 | ||
| CV:hclust | 2 | 0.722 | 0.854 | 0.926 | ||
| ATC:hclust | 4 | 0.672 | 0.812 | 0.892 | ||
| MAD:hclust | 2 | 0.592 | 0.818 | 0.899 | ||
| ATC:mclust | 2 | 0.538 | 0.919 | 0.946 | ||
| CV:kmeans | 2 | 0.510 | 0.739 | 0.861 | ||
| SD:pam | 2 | 0.387 | 0.843 | 0.901 |
**: 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 1.000 0.973 0.988 0.497 0.505 0.505
#> CV:NMF 2 0.753 0.882 0.948 0.504 0.496 0.496
#> MAD:NMF 2 1.000 0.976 0.990 0.501 0.499 0.499
#> ATC:NMF 2 1.000 0.965 0.986 0.432 0.568 0.568
#> SD:skmeans 2 1.000 0.992 0.996 0.501 0.499 0.499
#> CV:skmeans 2 0.520 0.700 0.873 0.502 0.497 0.497
#> MAD:skmeans 2 1.000 0.979 0.991 0.502 0.499 0.499
#> ATC:skmeans 2 1.000 0.978 0.992 0.501 0.499 0.499
#> SD:mclust 2 1.000 0.996 0.994 0.500 0.497 0.497
#> CV:mclust 2 0.505 0.796 0.898 0.492 0.512 0.512
#> MAD:mclust 2 1.000 0.983 0.979 0.491 0.497 0.497
#> ATC:mclust 2 0.538 0.919 0.946 0.501 0.497 0.497
#> SD:kmeans 2 0.898 0.906 0.963 0.487 0.507 0.507
#> CV:kmeans 2 0.510 0.739 0.861 0.452 0.545 0.545
#> MAD:kmeans 2 0.949 0.963 0.983 0.498 0.501 0.501
#> ATC:kmeans 2 1.000 0.997 0.998 0.496 0.505 0.505
#> SD:pam 2 0.387 0.843 0.901 0.468 0.512 0.512
#> CV:pam 2 0.548 0.845 0.915 0.489 0.496 0.496
#> MAD:pam 2 0.848 0.945 0.970 0.473 0.519 0.519
#> ATC:pam 2 1.000 0.978 0.990 0.471 0.531 0.531
#> SD:hclust 2 0.606 0.763 0.895 0.431 0.561 0.561
#> CV:hclust 2 0.722 0.854 0.926 0.404 0.618 0.618
#> MAD:hclust 2 0.592 0.818 0.899 0.457 0.498 0.498
#> ATC:hclust 2 0.503 0.677 0.798 0.431 0.522 0.522
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.813 0.921 0.960 0.343 0.727 0.507
#> CV:NMF 3 0.637 0.701 0.842 0.281 0.761 0.554
#> MAD:NMF 3 0.682 0.751 0.878 0.324 0.765 0.562
#> ATC:NMF 3 1.000 0.962 0.985 0.497 0.702 0.510
#> SD:skmeans 3 0.673 0.816 0.867 0.309 0.795 0.610
#> CV:skmeans 3 0.622 0.769 0.872 0.311 0.733 0.518
#> MAD:skmeans 3 0.557 0.773 0.823 0.303 0.807 0.632
#> ATC:skmeans 3 0.935 0.928 0.969 0.289 0.838 0.684
#> SD:mclust 3 0.539 0.732 0.786 0.246 0.875 0.748
#> CV:mclust 3 0.661 0.769 0.804 0.237 0.908 0.823
#> MAD:mclust 3 0.797 0.816 0.889 0.284 0.863 0.725
#> ATC:mclust 3 0.667 0.848 0.890 0.308 0.684 0.450
#> SD:kmeans 3 0.539 0.718 0.824 0.320 0.832 0.680
#> CV:kmeans 3 0.501 0.543 0.745 0.377 0.834 0.705
#> MAD:kmeans 3 0.541 0.722 0.828 0.298 0.846 0.700
#> ATC:kmeans 3 1.000 0.933 0.975 0.305 0.670 0.443
#> SD:pam 3 0.587 0.650 0.798 0.390 0.751 0.551
#> CV:pam 3 0.808 0.891 0.933 0.321 0.835 0.674
#> MAD:pam 3 0.508 0.650 0.788 0.367 0.690 0.470
#> ATC:pam 3 0.816 0.891 0.954 0.357 0.777 0.601
#> SD:hclust 3 0.538 0.719 0.839 0.420 0.761 0.590
#> CV:hclust 3 0.600 0.765 0.890 0.319 0.863 0.778
#> MAD:hclust 3 0.641 0.716 0.852 0.385 0.741 0.525
#> ATC:hclust 3 0.600 0.571 0.778 0.430 0.671 0.462
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.761 0.740 0.864 0.0977 0.888 0.680
#> CV:NMF 4 0.726 0.775 0.857 0.1138 0.855 0.615
#> MAD:NMF 4 0.824 0.855 0.913 0.1072 0.849 0.592
#> ATC:NMF 4 0.816 0.868 0.927 0.1528 0.858 0.618
#> SD:skmeans 4 0.822 0.888 0.922 0.1339 0.853 0.611
#> CV:skmeans 4 0.871 0.870 0.944 0.1183 0.879 0.667
#> MAD:skmeans 4 0.801 0.781 0.871 0.1374 0.857 0.622
#> ATC:skmeans 4 0.949 0.915 0.960 0.0921 0.925 0.797
#> SD:mclust 4 0.785 0.838 0.898 0.1677 0.822 0.556
#> CV:mclust 4 0.746 0.820 0.887 0.1261 0.870 0.703
#> MAD:mclust 4 0.840 0.908 0.936 0.1737 0.837 0.579
#> ATC:mclust 4 0.498 0.452 0.654 0.1105 0.841 0.571
#> SD:kmeans 4 0.584 0.502 0.751 0.1248 0.878 0.686
#> CV:kmeans 4 0.659 0.757 0.820 0.1246 0.758 0.491
#> MAD:kmeans 4 0.613 0.366 0.615 0.1321 0.833 0.599
#> ATC:kmeans 4 0.703 0.732 0.871 0.1411 0.788 0.488
#> SD:pam 4 0.606 0.513 0.739 0.1197 0.774 0.461
#> CV:pam 4 0.878 0.897 0.953 0.0874 0.910 0.757
#> MAD:pam 4 0.651 0.643 0.817 0.1323 0.821 0.536
#> ATC:pam 4 0.810 0.856 0.928 0.1567 0.798 0.510
#> SD:hclust 4 0.749 0.766 0.851 0.1329 0.911 0.759
#> CV:hclust 4 0.629 0.702 0.853 0.1176 0.995 0.989
#> MAD:hclust 4 0.744 0.796 0.872 0.1186 0.911 0.747
#> ATC:hclust 4 0.672 0.812 0.892 0.1868 0.804 0.524
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.830 0.848 0.916 0.0602 0.910 0.683
#> CV:NMF 5 0.735 0.678 0.817 0.0954 0.893 0.637
#> MAD:NMF 5 0.854 0.824 0.920 0.0545 0.919 0.709
#> ATC:NMF 5 0.776 0.640 0.821 0.0518 0.943 0.784
#> SD:skmeans 5 0.896 0.820 0.871 0.0464 0.944 0.792
#> CV:skmeans 5 0.967 0.927 0.961 0.0785 0.908 0.671
#> MAD:skmeans 5 0.751 0.545 0.721 0.0435 0.878 0.636
#> ATC:skmeans 5 0.874 0.839 0.913 0.0677 0.909 0.713
#> SD:mclust 5 0.741 0.697 0.870 0.0557 0.955 0.835
#> CV:mclust 5 0.831 0.842 0.915 0.1347 0.843 0.540
#> MAD:mclust 5 0.777 0.802 0.892 0.0379 0.986 0.944
#> ATC:mclust 5 0.801 0.744 0.873 0.0841 0.859 0.521
#> SD:kmeans 5 0.673 0.639 0.735 0.0721 0.845 0.527
#> CV:kmeans 5 0.724 0.856 0.822 0.0833 0.919 0.727
#> MAD:kmeans 5 0.651 0.470 0.656 0.0654 0.770 0.397
#> ATC:kmeans 5 0.704 0.670 0.809 0.0749 0.869 0.559
#> SD:pam 5 0.750 0.758 0.871 0.0612 0.862 0.559
#> CV:pam 5 0.778 0.829 0.894 0.0502 0.936 0.799
#> MAD:pam 5 0.692 0.572 0.746 0.0673 0.873 0.583
#> ATC:pam 5 0.779 0.680 0.819 0.0641 0.971 0.886
#> SD:hclust 5 0.697 0.752 0.858 0.0471 0.976 0.914
#> CV:hclust 5 0.751 0.805 0.910 0.1134 0.866 0.717
#> MAD:hclust 5 0.757 0.780 0.865 0.0386 0.976 0.913
#> ATC:hclust 5 0.696 0.724 0.862 0.0377 0.991 0.964
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.788 0.787 0.863 0.0566 0.909 0.621
#> CV:NMF 6 0.860 0.771 0.890 0.0515 0.910 0.616
#> MAD:NMF 6 0.754 0.558 0.779 0.0454 0.956 0.816
#> ATC:NMF 6 0.844 0.793 0.892 0.0302 0.920 0.671
#> SD:skmeans 6 0.831 0.775 0.843 0.0476 0.953 0.790
#> CV:skmeans 6 0.915 0.877 0.926 0.0423 0.952 0.774
#> MAD:skmeans 6 0.823 0.694 0.800 0.0466 0.839 0.491
#> ATC:skmeans 6 0.842 0.748 0.875 0.0298 0.975 0.900
#> SD:mclust 6 0.842 0.706 0.834 0.0596 0.936 0.746
#> CV:mclust 6 0.885 0.850 0.922 0.0419 0.976 0.887
#> MAD:mclust 6 0.802 0.730 0.840 0.0557 0.945 0.781
#> ATC:mclust 6 0.849 0.775 0.869 0.0432 0.924 0.659
#> SD:kmeans 6 0.712 0.561 0.693 0.0459 0.878 0.537
#> CV:kmeans 6 0.826 0.845 0.855 0.0556 0.959 0.815
#> MAD:kmeans 6 0.718 0.664 0.732 0.0477 0.846 0.464
#> ATC:kmeans 6 0.763 0.681 0.802 0.0421 0.932 0.683
#> SD:pam 6 0.841 0.785 0.889 0.0550 0.949 0.777
#> CV:pam 6 0.866 0.830 0.932 0.0818 0.926 0.730
#> MAD:pam 6 0.846 0.719 0.831 0.0442 0.883 0.557
#> ATC:pam 6 0.782 0.679 0.756 0.0453 0.894 0.593
#> SD:hclust 6 0.719 0.741 0.845 0.0386 0.971 0.892
#> CV:hclust 6 0.716 0.720 0.838 0.0802 0.989 0.967
#> MAD:hclust 6 0.740 0.740 0.850 0.0355 0.989 0.958
#> ATC:hclust 6 0.735 0.691 0.823 0.0283 0.972 0.888
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 17548 rows and 122 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 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 0.606 0.763 0.895 0.4312 0.561 0.561
#> 3 3 0.538 0.719 0.839 0.4204 0.761 0.590
#> 4 4 0.749 0.766 0.851 0.1329 0.911 0.759
#> 5 5 0.697 0.752 0.858 0.0471 0.976 0.914
#> 6 6 0.719 0.741 0.845 0.0386 0.971 0.892
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.2603 0.870 0.956 0.044
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.9815 0.272 0.420 0.580
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.8081 0.680 0.752 0.248
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.2423 0.871 0.960 0.040
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 1 0.9522 0.472 0.628 0.372
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0938 0.862 0.012 0.988
#> 9264567D-4524-46AF-A851-C091C3CD76CF 1 0.9661 0.424 0.608 0.392
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.2603 0.870 0.956 0.044
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.3431 0.859 0.936 0.064
#> 806616FE-1855-4284-9265-42842104CB21 1 0.9522 0.472 0.628 0.372
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.862 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.1184 0.861 0.016 0.984
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.2603 0.870 0.956 0.044
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.9850 0.246 0.428 0.572
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 1 0.9209 0.541 0.664 0.336
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 1 0.9522 0.472 0.628 0.372
#> 853120F0-857B-4108-9EC8-727189630C5F 1 0.9209 0.541 0.664 0.336
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.873 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.862 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.862 0.000 1.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.873 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.862 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 1 0.9209 0.541 0.664 0.336
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.873 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0938 0.862 0.012 0.988
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.873 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.873 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.873 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.9209 0.541 0.664 0.336
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.9850 0.246 0.428 0.572
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.862 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.2603 0.870 0.956 0.044
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.873 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.0000 0.873 1.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.873 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 1 0.9209 0.541 0.664 0.336
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0000 0.873 1.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.9661 0.424 0.608 0.392
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.862 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.2423 0.871 0.960 0.040
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0938 0.862 0.012 0.988
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.873 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.873 1.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.1184 0.870 0.984 0.016
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 1 0.9661 0.424 0.608 0.392
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 1 0.9209 0.541 0.664 0.336
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.9775 0.292 0.412 0.588
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.8555 0.587 0.280 0.720
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.2603 0.870 0.956 0.044
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.1184 0.870 0.984 0.016
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.6148 0.751 0.152 0.848
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.1184 0.870 0.984 0.016
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.9580 0.454 0.620 0.380
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.2423 0.871 0.960 0.040
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.1184 0.870 0.984 0.016
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.2423 0.871 0.960 0.040
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0938 0.862 0.012 0.988
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.873 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.873 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.8555 0.587 0.280 0.720
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.1184 0.870 0.984 0.016
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.862 0.000 1.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.1184 0.861 0.016 0.984
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.8081 0.680 0.752 0.248
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 1 0.9661 0.424 0.608 0.392
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.1184 0.870 0.984 0.016
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.873 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.873 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.1184 0.870 0.984 0.016
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.2423 0.871 0.960 0.040
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.1184 0.861 0.016 0.984
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0938 0.862 0.012 0.988
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.6148 0.751 0.152 0.848
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.862 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.2778 0.868 0.952 0.048
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.862 0.000 1.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.2423 0.871 0.960 0.040
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.873 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.873 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.9795 0.280 0.416 0.584
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.873 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.873 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.2603 0.870 0.956 0.044
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.862 0.000 1.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.1184 0.861 0.016 0.984
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.9775 0.292 0.412 0.588
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.2423 0.871 0.960 0.040
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.873 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.1184 0.861 0.016 0.984
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0938 0.860 0.012 0.988
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.0000 0.873 1.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.1414 0.874 0.980 0.020
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.862 0.000 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.1414 0.874 0.980 0.020
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.9209 0.541 0.664 0.336
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.9754 0.354 0.592 0.408
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.873 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.2423 0.871 0.960 0.040
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.862 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.862 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.8081 0.680 0.752 0.248
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.2778 0.868 0.952 0.048
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.2603 0.870 0.956 0.044
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.862 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.873 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.8327 0.614 0.264 0.736
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.1414 0.874 0.980 0.020
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.1414 0.874 0.980 0.020
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.873 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 1 0.9661 0.424 0.608 0.392
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.862 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.2603 0.870 0.956 0.044
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.873 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.873 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.9795 0.280 0.416 0.584
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.6712 0.757 0.824 0.176
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.2423 0.871 0.960 0.040
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.2778 0.868 0.952 0.048
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.873 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.862 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.0376 0.872 0.996 0.004
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 1 0.9209 0.541 0.664 0.336
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.2229 0.8298 0.944 0.012 0.044
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 3 0.9371 0.3906 0.172 0.376 0.452
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.4784 0.5387 0.200 0.004 0.796
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.6664 0.3341 0.528 0.008 0.464
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.5536 0.7548 0.236 0.012 0.752
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.1832 0.8914 0.008 0.956 0.036
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.5020 0.7635 0.192 0.012 0.796
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.1999 0.8286 0.952 0.012 0.036
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.2689 0.8132 0.932 0.032 0.036
#> 806616FE-1855-4284-9265-42842104CB21 3 0.5536 0.7548 0.236 0.012 0.752
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.2448 0.8588 0.000 0.924 0.076
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.1999 0.8898 0.012 0.952 0.036
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.2229 0.8298 0.944 0.012 0.044
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 3 0.9490 0.4151 0.188 0.368 0.444
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.5285 0.7562 0.244 0.004 0.752
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.5406 0.7604 0.224 0.012 0.764
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.5285 0.7562 0.244 0.004 0.752
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.8366 1.000 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0424 0.9020 0.000 0.992 0.008
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0424 0.9020 0.000 0.992 0.008
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.8366 1.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0424 0.9020 0.000 0.992 0.008
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.5325 0.7536 0.248 0.004 0.748
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.6260 0.3579 0.552 0.000 0.448
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.1832 0.8914 0.008 0.956 0.036
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.8366 1.000 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.8366 1.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.8366 1.000 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.5285 0.7562 0.244 0.004 0.752
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 3 0.9490 0.4151 0.188 0.368 0.444
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0424 0.9020 0.000 0.992 0.008
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.2229 0.8298 0.944 0.012 0.044
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.5650 0.5518 0.688 0.000 0.312
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.0424 0.8360 0.992 0.000 0.008
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.8366 1.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.5285 0.7562 0.244 0.004 0.752
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0892 0.8339 0.980 0.000 0.020
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.5020 0.7635 0.192 0.012 0.796
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0424 0.9020 0.000 0.992 0.008
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.2063 0.8315 0.948 0.008 0.044
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.1832 0.8914 0.008 0.956 0.036
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.6260 0.3579 0.552 0.000 0.448
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.6225 -0.0748 0.432 0.000 0.568
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.1964 0.8116 0.944 0.000 0.056
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.5020 0.7635 0.192 0.012 0.796
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.5285 0.7562 0.244 0.004 0.752
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 3 0.9485 0.3793 0.184 0.388 0.428
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.8868 0.1879 0.172 0.568 0.260
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.1999 0.8286 0.952 0.012 0.036
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.1964 0.8116 0.944 0.000 0.056
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.7039 0.5659 0.144 0.728 0.128
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.1964 0.8131 0.944 0.000 0.056
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.5406 0.7589 0.224 0.012 0.764
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.2063 0.8315 0.948 0.008 0.044
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.1860 0.8145 0.948 0.000 0.052
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.6672 0.3175 0.520 0.008 0.472
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.1832 0.8914 0.008 0.956 0.036
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.6260 0.3579 0.552 0.000 0.448
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0237 0.8360 0.996 0.000 0.004
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.8868 0.1879 0.172 0.568 0.260
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.1964 0.8116 0.944 0.000 0.056
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0424 0.9020 0.000 0.992 0.008
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.1999 0.8898 0.012 0.952 0.036
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.4784 0.5387 0.200 0.004 0.796
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.5020 0.7635 0.192 0.012 0.796
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.1753 0.8175 0.952 0.000 0.048
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.8366 1.000 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.8366 1.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.2448 0.8037 0.924 0.000 0.076
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.2063 0.8315 0.948 0.008 0.044
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.1999 0.8898 0.012 0.952 0.036
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.1832 0.8914 0.008 0.956 0.036
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.7039 0.5659 0.144 0.728 0.128
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0424 0.9020 0.000 0.992 0.008
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.2152 0.8266 0.948 0.016 0.036
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0592 0.9004 0.000 0.988 0.012
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.2063 0.8315 0.948 0.008 0.044
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0237 0.8360 0.996 0.000 0.004
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.6260 0.3579 0.552 0.000 0.448
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 3 0.9481 0.3887 0.184 0.384 0.432
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.6260 0.3579 0.552 0.000 0.448
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.2625 0.7801 0.916 0.000 0.084
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.2229 0.8298 0.944 0.012 0.044
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0424 0.9020 0.000 0.992 0.008
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.1999 0.8898 0.012 0.952 0.036
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 3 0.9485 0.3793 0.184 0.388 0.428
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.2063 0.8315 0.948 0.008 0.044
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.8366 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.1999 0.8898 0.012 0.952 0.036
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0983 0.8982 0.004 0.980 0.016
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.0892 0.8339 0.980 0.000 0.020
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.6307 0.2982 0.512 0.000 0.488
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0424 0.9020 0.000 0.992 0.008
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.6307 0.2982 0.512 0.000 0.488
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.5285 0.7562 0.244 0.004 0.752
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 3 0.9491 0.5847 0.292 0.220 0.488
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0237 0.8366 0.996 0.000 0.004
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.2063 0.8315 0.948 0.008 0.044
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0424 0.9020 0.000 0.992 0.008
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0424 0.9020 0.000 0.992 0.008
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.4784 0.5387 0.200 0.004 0.796
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.2152 0.8266 0.948 0.016 0.036
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.2229 0.8298 0.944 0.012 0.044
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0424 0.9020 0.000 0.992 0.008
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.6260 0.3579 0.552 0.000 0.448
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.8670 0.2663 0.168 0.592 0.240
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.6307 0.2982 0.512 0.000 0.488
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.6307 0.2982 0.512 0.000 0.488
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.8366 1.000 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.5020 0.7635 0.192 0.012 0.796
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0424 0.9020 0.000 0.992 0.008
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.1999 0.8286 0.952 0.012 0.036
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0237 0.8372 0.996 0.000 0.004
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0237 0.8372 0.996 0.000 0.004
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 3 0.9481 0.3887 0.184 0.384 0.432
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.4605 0.4851 0.204 0.000 0.796
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.2063 0.8315 0.948 0.008 0.044
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.2152 0.8266 0.948 0.016 0.036
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.8366 1.000 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0424 0.9020 0.000 0.992 0.008
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.1163 0.8320 0.972 0.000 0.028
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.5285 0.7562 0.244 0.004 0.752
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.1716 0.906 0.064 0.000 0.000 0.936
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 3 0.6079 0.337 0.052 0.380 0.568 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.6326 0.142 0.376 0.000 0.556 0.068
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.4761 0.837 0.628 0.000 0.000 0.372
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.3074 0.585 0.000 0.000 0.848 0.152
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.1557 0.888 0.056 0.944 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0000 0.687 0.000 0.000 1.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.1557 0.908 0.056 0.000 0.000 0.944
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.2256 0.886 0.056 0.020 0.000 0.924
#> 806616FE-1855-4284-9265-42842104CB21 3 0.3074 0.585 0.000 0.000 0.848 0.152
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.1792 0.852 0.000 0.932 0.068 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.1661 0.888 0.052 0.944 0.000 0.004
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.1716 0.906 0.064 0.000 0.000 0.936
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 3 0.6216 0.354 0.044 0.372 0.576 0.008
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.4836 0.669 0.320 0.000 0.672 0.008
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.1022 0.677 0.000 0.000 0.968 0.032
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.4836 0.669 0.320 0.000 0.672 0.008
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.5271 0.663 0.320 0.000 0.656 0.024
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.4877 0.875 0.592 0.000 0.000 0.408
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.1557 0.888 0.056 0.944 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.4836 0.669 0.320 0.000 0.672 0.008
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 3 0.6216 0.354 0.044 0.372 0.576 0.008
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.1716 0.906 0.064 0.000 0.000 0.936
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.5673 -0.620 0.448 0.000 0.024 0.528
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.0336 0.914 0.000 0.000 0.008 0.992
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.4836 0.669 0.320 0.000 0.672 0.008
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.0707 0.909 0.000 0.000 0.020 0.980
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0000 0.687 0.000 0.000 1.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.1637 0.908 0.060 0.000 0.000 0.940
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.1474 0.889 0.052 0.948 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.4877 0.875 0.592 0.000 0.000 0.408
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.4353 0.717 0.756 0.000 0.012 0.232
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.1557 0.878 0.000 0.000 0.056 0.944
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0000 0.687 0.000 0.000 1.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.4836 0.669 0.320 0.000 0.672 0.008
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 3 0.6440 0.321 0.048 0.384 0.556 0.012
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.6754 0.246 0.064 0.556 0.364 0.016
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 4 0.1557 0.908 0.056 0.000 0.000 0.944
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.1557 0.878 0.000 0.000 0.056 0.944
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.4164 0.574 0.000 0.736 0.264 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.2198 0.851 0.008 0.000 0.072 0.920
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.2921 0.597 0.000 0.000 0.860 0.140
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 4 0.1637 0.908 0.060 0.000 0.000 0.940
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.1474 0.881 0.000 0.000 0.052 0.948
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.4713 0.845 0.640 0.000 0.000 0.360
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.1557 0.888 0.056 0.944 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.4877 0.875 0.592 0.000 0.000 0.408
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0188 0.915 0.004 0.000 0.000 0.996
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.6754 0.246 0.064 0.556 0.364 0.016
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.1557 0.878 0.000 0.000 0.056 0.944
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.1743 0.885 0.056 0.940 0.000 0.004
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.6326 0.142 0.376 0.000 0.556 0.068
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0000 0.687 0.000 0.000 1.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.1792 0.864 0.000 0.000 0.068 0.932
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.3471 0.776 0.072 0.000 0.060 0.868
#> 352471DC-A881-4EA8-B646-EB1200291893 4 0.1637 0.908 0.060 0.000 0.000 0.940
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.1661 0.888 0.052 0.944 0.000 0.004
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.1557 0.888 0.056 0.944 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.4164 0.574 0.000 0.736 0.264 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 4 0.1743 0.905 0.056 0.004 0.000 0.940
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0524 0.897 0.004 0.988 0.008 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.1637 0.908 0.060 0.000 0.000 0.940
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0188 0.915 0.004 0.000 0.000 0.996
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.4877 0.875 0.592 0.000 0.000 0.408
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 3 0.6429 0.330 0.048 0.380 0.560 0.012
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.4877 0.875 0.592 0.000 0.000 0.408
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.3356 0.599 0.176 0.000 0.000 0.824
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.1716 0.906 0.064 0.000 0.000 0.936
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.1743 0.885 0.056 0.940 0.000 0.004
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 3 0.6440 0.321 0.048 0.384 0.556 0.012
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.1637 0.908 0.060 0.000 0.000 0.940
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.1743 0.885 0.056 0.940 0.000 0.004
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0469 0.896 0.000 0.988 0.012 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.0707 0.909 0.000 0.000 0.020 0.980
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.4661 0.879 0.652 0.000 0.000 0.348
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.4661 0.879 0.652 0.000 0.000 0.348
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.4836 0.669 0.320 0.000 0.672 0.008
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 3 0.8487 0.475 0.152 0.216 0.536 0.096
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0188 0.915 0.000 0.000 0.004 0.996
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 4 0.1637 0.908 0.060 0.000 0.000 0.940
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.6326 0.142 0.376 0.000 0.556 0.068
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 4 0.1743 0.905 0.056 0.004 0.000 0.940
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.1716 0.906 0.064 0.000 0.000 0.936
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.4877 0.875 0.592 0.000 0.000 0.408
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.6809 0.319 0.056 0.580 0.336 0.028
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.4661 0.879 0.652 0.000 0.000 0.348
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.4661 0.879 0.652 0.000 0.000 0.348
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0000 0.687 0.000 0.000 1.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.1557 0.908 0.056 0.000 0.000 0.944
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0188 0.916 0.004 0.000 0.000 0.996
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0188 0.916 0.004 0.000 0.000 0.996
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 3 0.6429 0.330 0.048 0.380 0.560 0.012
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.1661 0.247 0.944 0.000 0.052 0.004
#> 12F54761-4F68-4181-8421-88EA858902FC 4 0.1637 0.908 0.060 0.000 0.000 0.940
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 4 0.1743 0.905 0.056 0.004 0.000 0.940
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.916 0.000 0.000 0.000 1.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.0921 0.905 0.000 0.000 0.028 0.972
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.4836 0.669 0.320 0.000 0.672 0.008
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.2536 0.8603 0.128 0.000 0.004 0.868 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 3 0.5671 0.3737 0.096 0.336 0.568 0.000 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.5553 0.1969 0.380 0.000 0.552 0.064 0.004
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.3861 0.7598 0.712 0.000 0.004 0.284 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.2921 0.5691 0.004 0.000 0.844 0.148 0.004
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.2020 0.8644 0.100 0.900 0.000 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0162 0.6226 0.000 0.000 0.996 0.000 0.004
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.2439 0.8632 0.120 0.000 0.004 0.876 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.3031 0.8452 0.120 0.020 0.004 0.856 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.2921 0.5691 0.004 0.000 0.844 0.148 0.004
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.1544 0.8428 0.000 0.932 0.068 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.2124 0.8647 0.096 0.900 0.004 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.2536 0.8603 0.128 0.000 0.004 0.868 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 5 0.7968 0.0365 0.088 0.328 0.224 0.000 0.360
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.0404 0.8283 0.000 0.000 0.012 0.000 0.988
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.1116 0.6231 0.004 0.000 0.964 0.028 0.004
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0404 0.8283 0.000 0.000 0.012 0.000 0.988
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.8864 0.000 0.000 0.000 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.8873 0.000 1.000 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.8873 0.000 1.000 0.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.8864 0.000 0.000 0.000 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.8873 0.000 1.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.0613 0.8155 0.004 0.000 0.004 0.008 0.984
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.3999 0.8389 0.656 0.000 0.000 0.344 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.2020 0.8644 0.100 0.900 0.000 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.8864 0.000 0.000 0.000 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.8864 0.000 0.000 0.000 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.8864 0.000 0.000 0.000 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.0404 0.8283 0.000 0.000 0.012 0.000 0.988
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 5 0.7968 0.0365 0.088 0.328 0.224 0.000 0.360
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.8873 0.000 1.000 0.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.2536 0.8603 0.128 0.000 0.004 0.868 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.4886 -0.4775 0.448 0.000 0.024 0.528 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.0290 0.8841 0.000 0.000 0.008 0.992 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.8864 0.000 0.000 0.000 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0404 0.8283 0.000 0.000 0.012 0.000 0.988
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.0609 0.8803 0.000 0.000 0.020 0.980 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0162 0.6226 0.000 0.000 0.996 0.000 0.004
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.8873 0.000 1.000 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.2488 0.8624 0.124 0.000 0.004 0.872 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.1965 0.8659 0.096 0.904 0.000 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.3999 0.8389 0.656 0.000 0.000 0.344 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.3241 0.6633 0.832 0.000 0.000 0.144 0.024
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.1502 0.8583 0.004 0.000 0.056 0.940 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0162 0.6226 0.000 0.000 0.996 0.000 0.004
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0404 0.8283 0.000 0.000 0.012 0.000 0.988
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 3 0.5900 0.3630 0.092 0.340 0.560 0.008 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.6223 0.2818 0.100 0.512 0.004 0.008 0.376
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 4 0.2439 0.8632 0.120 0.000 0.004 0.876 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.1502 0.8583 0.004 0.000 0.056 0.940 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.3586 0.5794 0.000 0.736 0.264 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.1894 0.8339 0.008 0.000 0.072 0.920 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.2787 0.5754 0.004 0.000 0.856 0.136 0.004
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 4 0.2488 0.8624 0.124 0.000 0.004 0.872 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.1270 0.8578 0.000 0.000 0.052 0.948 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.3636 0.7716 0.728 0.000 0.000 0.272 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.2020 0.8644 0.100 0.900 0.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.3999 0.8389 0.656 0.000 0.000 0.344 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0162 0.8853 0.004 0.000 0.000 0.996 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.6223 0.2818 0.100 0.512 0.004 0.008 0.376
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.1502 0.8583 0.004 0.000 0.056 0.940 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0290 0.8869 0.008 0.992 0.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.2179 0.8618 0.100 0.896 0.004 0.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.5553 0.1969 0.380 0.000 0.552 0.064 0.004
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0162 0.6226 0.000 0.000 0.996 0.000 0.004
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.1544 0.8446 0.000 0.000 0.068 0.932 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0000 0.8864 0.000 0.000 0.000 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.8864 0.000 0.000 0.000 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.2989 0.7706 0.072 0.000 0.060 0.868 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 4 0.2488 0.8624 0.124 0.000 0.004 0.872 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.2124 0.8647 0.096 0.900 0.004 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.2020 0.8644 0.100 0.900 0.000 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.3586 0.5794 0.000 0.736 0.264 0.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.8873 0.000 1.000 0.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 4 0.2488 0.8611 0.124 0.000 0.004 0.872 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0486 0.8833 0.004 0.988 0.004 0.000 0.004
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.2488 0.8624 0.124 0.000 0.004 0.872 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0162 0.8853 0.004 0.000 0.000 0.996 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.3999 0.8389 0.656 0.000 0.000 0.344 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 3 0.5888 0.3712 0.092 0.336 0.564 0.008 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.3999 0.8389 0.656 0.000 0.000 0.344 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.2891 0.6267 0.176 0.000 0.000 0.824 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.2536 0.8603 0.128 0.000 0.004 0.868 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0290 0.8869 0.008 0.992 0.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.2179 0.8618 0.100 0.896 0.004 0.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 3 0.5900 0.3630 0.092 0.340 0.560 0.008 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.2488 0.8624 0.124 0.000 0.004 0.872 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.8864 0.000 0.000 0.000 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.2179 0.8618 0.100 0.896 0.004 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0404 0.8829 0.000 0.988 0.012 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.0609 0.8803 0.000 0.000 0.020 0.980 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.3109 0.8182 0.800 0.000 0.000 0.200 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.8873 0.000 1.000 0.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.3109 0.8182 0.800 0.000 0.000 0.200 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.0404 0.8283 0.000 0.000 0.012 0.000 0.988
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 3 0.6765 0.4489 0.264 0.172 0.536 0.028 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0162 0.8855 0.000 0.000 0.004 0.996 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 4 0.2488 0.8624 0.124 0.000 0.004 0.872 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.8873 0.000 1.000 0.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.8873 0.000 1.000 0.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.5553 0.1969 0.380 0.000 0.552 0.064 0.004
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 4 0.2488 0.8611 0.124 0.000 0.004 0.872 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.2536 0.8603 0.128 0.000 0.004 0.868 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.8873 0.000 1.000 0.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.3999 0.8389 0.656 0.000 0.000 0.344 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.6287 0.3501 0.104 0.536 0.004 0.012 0.344
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.3109 0.8182 0.800 0.000 0.000 0.200 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.3109 0.8182 0.800 0.000 0.000 0.200 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.8864 0.000 0.000 0.000 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0162 0.6226 0.000 0.000 0.996 0.000 0.004
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.8873 0.000 1.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.2439 0.8632 0.120 0.000 0.004 0.876 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0162 0.8856 0.004 0.000 0.000 0.996 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0162 0.8856 0.004 0.000 0.000 0.996 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 3 0.5888 0.3712 0.092 0.336 0.564 0.008 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.3508 0.3477 0.748 0.000 0.000 0.000 0.252
#> 12F54761-4F68-4181-8421-88EA858902FC 4 0.2488 0.8624 0.124 0.000 0.004 0.872 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 4 0.2488 0.8611 0.124 0.000 0.004 0.872 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.8864 0.000 0.000 0.000 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.8873 0.000 1.000 0.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.0794 0.8766 0.000 0.000 0.028 0.972 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0404 0.8283 0.000 0.000 0.012 0.000 0.988
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.2887 0.847 0.036 0.000 0.000 0.844 0.000 0.120
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.4943 0.543 0.552 0.044 0.392 0.000 0.000 0.012
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.4845 0.378 0.008 0.000 0.560 0.044 0.000 0.388
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 6 0.3911 0.693 0.032 0.000 0.000 0.256 0.000 0.712
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.2615 0.664 0.004 0.000 0.852 0.136 0.000 0.008
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.3342 0.702 0.228 0.760 0.000 0.000 0.000 0.012
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0260 0.716 0.008 0.000 0.992 0.000 0.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.2798 0.850 0.036 0.000 0.000 0.852 0.000 0.112
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.3204 0.836 0.052 0.004 0.000 0.832 0.000 0.112
#> 806616FE-1855-4284-9265-42842104CB21 3 0.2615 0.664 0.004 0.000 0.852 0.136 0.000 0.008
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.3468 0.648 0.128 0.804 0.068 0.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.3201 0.731 0.208 0.780 0.000 0.000 0.000 0.012
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.2887 0.847 0.036 0.000 0.000 0.844 0.000 0.120
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 1 0.2662 0.536 0.888 0.044 0.048 0.000 0.016 0.004
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.0000 0.953 0.000 0.000 0.000 0.000 1.000 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0806 0.715 0.000 0.000 0.972 0.020 0.000 0.008
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0000 0.953 0.000 0.000 0.000 0.000 1.000 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.876 0.000 0.000 0.000 1.000 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.876 0.000 0.000 0.000 1.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.3672 0.595 0.368 0.000 0.000 0.000 0.632 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 6 0.3707 0.803 0.008 0.000 0.000 0.312 0.000 0.680
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.3342 0.702 0.228 0.760 0.000 0.000 0.000 0.012
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.876 0.000 0.000 0.000 1.000 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.876 0.000 0.000 0.000 1.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.876 0.000 0.000 0.000 1.000 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.0000 0.953 0.000 0.000 0.000 0.000 1.000 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.2662 0.536 0.888 0.044 0.048 0.000 0.016 0.004
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.2887 0.847 0.036 0.000 0.000 0.844 0.000 0.120
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.4524 -0.422 0.004 0.000 0.024 0.520 0.000 0.452
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.0665 0.870 0.008 0.000 0.008 0.980 0.000 0.004
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.876 0.000 0.000 0.000 1.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0000 0.953 0.000 0.000 0.000 0.000 1.000 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.1036 0.865 0.008 0.000 0.024 0.964 0.000 0.004
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0260 0.716 0.008 0.000 0.992 0.000 0.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.2815 0.849 0.032 0.000 0.000 0.848 0.000 0.120
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.3171 0.734 0.204 0.784 0.000 0.000 0.000 0.012
#> 692C65BB-BF32-4846-806B-01A285BED1B9 6 0.3707 0.803 0.008 0.000 0.000 0.312 0.000 0.680
#> CB925BF0-1249-4350-A175-9A4129C43B8D 6 0.2762 0.605 0.048 0.000 0.000 0.092 0.000 0.860
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.1882 0.844 0.008 0.000 0.060 0.920 0.000 0.012
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0260 0.716 0.008 0.000 0.992 0.000 0.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0000 0.953 0.000 0.000 0.000 0.000 1.000 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.4703 0.568 0.568 0.052 0.380 0.000 0.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 1 0.4570 0.183 0.596 0.368 0.000 0.000 0.024 0.012
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 4 0.2798 0.850 0.036 0.000 0.000 0.852 0.000 0.112
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.1820 0.847 0.008 0.000 0.056 0.924 0.000 0.012
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.5336 0.177 0.168 0.588 0.244 0.000 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.2114 0.819 0.008 0.000 0.076 0.904 0.000 0.012
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.2573 0.666 0.004 0.000 0.856 0.132 0.000 0.008
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 4 0.2815 0.849 0.032 0.000 0.000 0.848 0.000 0.120
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.1542 0.845 0.008 0.000 0.052 0.936 0.000 0.004
#> B5474EEB-D585-4668-959C-38F240F55BC2 6 0.3770 0.704 0.028 0.000 0.000 0.244 0.000 0.728
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.3342 0.702 0.228 0.760 0.000 0.000 0.000 0.012
#> A533C39D-CE42-42AD-92AD-549157A43139 6 0.3707 0.803 0.008 0.000 0.000 0.312 0.000 0.680
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0146 0.875 0.000 0.000 0.000 0.996 0.000 0.004
#> 84E18629-1B13-4696-8E54-121ABE469CD2 1 0.4570 0.183 0.596 0.368 0.000 0.000 0.024 0.012
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.1882 0.844 0.008 0.000 0.060 0.920 0.000 0.012
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.1957 0.761 0.112 0.888 0.000 0.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.3368 0.698 0.232 0.756 0.000 0.000 0.000 0.012
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.4845 0.378 0.008 0.000 0.560 0.044 0.000 0.388
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0260 0.716 0.008 0.000 0.992 0.000 0.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.1787 0.832 0.008 0.000 0.068 0.920 0.000 0.004
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0000 0.876 0.000 0.000 0.000 1.000 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.876 0.000 0.000 0.000 1.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.2882 0.765 0.004 0.000 0.060 0.860 0.000 0.076
#> 352471DC-A881-4EA8-B646-EB1200291893 4 0.2696 0.852 0.028 0.000 0.000 0.856 0.000 0.116
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.3201 0.731 0.208 0.780 0.000 0.000 0.000 0.012
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.3342 0.702 0.228 0.760 0.000 0.000 0.000 0.012
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.5336 0.177 0.168 0.588 0.244 0.000 0.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0547 0.823 0.020 0.980 0.000 0.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 4 0.2867 0.848 0.040 0.000 0.000 0.848 0.000 0.112
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0405 0.820 0.004 0.988 0.000 0.000 0.008 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.2815 0.849 0.032 0.000 0.000 0.848 0.000 0.120
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0146 0.875 0.000 0.000 0.000 0.996 0.000 0.004
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 6 0.3707 0.803 0.008 0.000 0.000 0.312 0.000 0.680
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.4658 0.564 0.568 0.048 0.384 0.000 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 6 0.3707 0.803 0.008 0.000 0.000 0.312 0.000 0.680
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.2597 0.640 0.000 0.000 0.000 0.824 0.000 0.176
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.2887 0.847 0.036 0.000 0.000 0.844 0.000 0.120
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.1957 0.761 0.112 0.888 0.000 0.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.3368 0.698 0.232 0.756 0.000 0.000 0.000 0.012
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.4703 0.568 0.568 0.052 0.380 0.000 0.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.2815 0.849 0.032 0.000 0.000 0.848 0.000 0.120
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.876 0.000 0.000 0.000 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.3368 0.698 0.232 0.756 0.000 0.000 0.000 0.012
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0405 0.820 0.004 0.988 0.008 0.000 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.1149 0.866 0.008 0.000 0.024 0.960 0.000 0.008
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 6 0.2489 0.769 0.012 0.000 0.000 0.128 0.000 0.860
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 6 0.2489 0.769 0.012 0.000 0.000 0.128 0.000 0.860
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.0000 0.953 0.000 0.000 0.000 0.000 1.000 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.6224 0.317 0.412 0.012 0.364 0.000 0.000 0.212
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0436 0.873 0.004 0.000 0.004 0.988 0.000 0.004
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 4 0.2815 0.849 0.032 0.000 0.000 0.848 0.000 0.120
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.4845 0.378 0.008 0.000 0.560 0.044 0.000 0.388
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 4 0.2867 0.848 0.040 0.000 0.000 0.848 0.000 0.112
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.2887 0.847 0.036 0.000 0.000 0.844 0.000 0.120
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0547 0.823 0.020 0.980 0.000 0.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 6 0.3707 0.803 0.008 0.000 0.000 0.312 0.000 0.680
#> B3561356-5A80-4C79-B23A-D518425565FE 1 0.4694 0.124 0.572 0.392 0.000 0.004 0.016 0.016
#> F900E9BE-2400-4451-9434-EE8BC513BA94 6 0.2489 0.769 0.012 0.000 0.000 0.128 0.000 0.860
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 6 0.2489 0.769 0.012 0.000 0.000 0.128 0.000 0.860
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.876 0.000 0.000 0.000 1.000 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0260 0.716 0.008 0.000 0.992 0.000 0.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.2798 0.850 0.036 0.000 0.000 0.852 0.000 0.112
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0146 0.876 0.000 0.000 0.000 0.996 0.000 0.004
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0146 0.876 0.000 0.000 0.000 0.996 0.000 0.004
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.4658 0.564 0.568 0.048 0.384 0.000 0.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 6 0.4000 0.353 0.048 0.000 0.000 0.000 0.228 0.724
#> 12F54761-4F68-4181-8421-88EA858902FC 4 0.2815 0.849 0.032 0.000 0.000 0.848 0.000 0.120
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 4 0.2867 0.848 0.040 0.000 0.000 0.848 0.000 0.112
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.876 0.000 0.000 0.000 1.000 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.825 0.000 1.000 0.000 0.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.1307 0.863 0.008 0.000 0.032 0.952 0.000 0.008
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0000 0.953 0.000 0.000 0.000 0.000 1.000 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["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 17548 rows and 122 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 0.898 0.906 0.963 0.4871 0.507 0.507
#> 3 3 0.539 0.718 0.824 0.3198 0.832 0.680
#> 4 4 0.584 0.502 0.751 0.1248 0.878 0.686
#> 5 5 0.673 0.639 0.735 0.0721 0.845 0.527
#> 6 6 0.712 0.561 0.693 0.0459 0.878 0.537
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.976 1.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.0000 0.939 0.000 1.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.976 1.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.976 1.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 1 0.8207 0.624 0.744 0.256
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.939 0.000 1.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 2 0.0000 0.939 0.000 1.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.976 1.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0000 0.976 1.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 1 0.0000 0.976 1.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.939 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.939 0.000 1.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.976 1.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.939 0.000 1.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 2 0.9922 0.243 0.448 0.552
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 1 0.9833 0.198 0.576 0.424
#> 853120F0-857B-4108-9EC8-727189630C5F 2 0.9922 0.243 0.448 0.552
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.976 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.939 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.939 0.000 1.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.976 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.939 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 2 0.9922 0.243 0.448 0.552
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.976 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.939 0.000 1.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.976 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.976 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.976 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 2 0.8713 0.600 0.292 0.708
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.9460 0.458 0.364 0.636
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.939 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.976 1.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.976 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.0000 0.976 1.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.976 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 2 0.9922 0.243 0.448 0.552
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0000 0.976 1.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 2 0.5408 0.828 0.124 0.876
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.939 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.976 1.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.939 0.000 1.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.976 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.976 1.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.976 1.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.0000 0.939 0.000 1.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.0000 0.939 0.000 1.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.939 0.000 1.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0000 0.939 0.000 1.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.976 1.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.0000 0.976 1.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.939 0.000 1.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.0000 0.976 1.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.9635 0.311 0.612 0.388
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.976 1.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.0000 0.976 1.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.976 1.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.939 0.000 1.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.976 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.976 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.0000 0.939 0.000 1.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.0000 0.976 1.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.939 0.000 1.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.939 0.000 1.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.0000 0.976 1.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.0000 0.939 0.000 1.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.0000 0.976 1.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.976 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.976 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0000 0.976 1.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.976 1.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.939 0.000 1.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.939 0.000 1.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.939 0.000 1.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.939 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.976 1.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.939 0.000 1.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.976 1.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.976 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.976 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.4298 0.864 0.088 0.912
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.976 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.976 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.976 1.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.939 0.000 1.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0938 0.930 0.012 0.988
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0000 0.939 0.000 1.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.976 1.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.976 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.939 0.000 1.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.939 0.000 1.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.0000 0.976 1.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.976 1.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.939 0.000 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.976 1.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.9209 0.451 0.664 0.336
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.8661 0.612 0.288 0.712
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.976 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.976 1.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.939 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.939 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0000 0.976 1.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0000 0.976 1.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.976 1.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.939 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.976 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.939 0.000 1.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.976 1.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.976 1.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.976 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.0000 0.939 0.000 1.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.939 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.976 1.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.976 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.976 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0000 0.939 0.000 1.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.4815 0.860 0.896 0.104
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.976 1.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.0000 0.976 1.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.976 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.939 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.0000 0.976 1.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.0000 0.939 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.6880 0.7469 0.736 0.156 0.108
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 3 0.6260 -0.0925 0.000 0.448 0.552
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.5733 0.5770 0.324 0.000 0.676
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.7558 0.7348 0.692 0.144 0.164
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.7276 0.5468 0.404 0.032 0.564
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0237 0.8082 0.000 0.996 0.004
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.5493 0.6151 0.012 0.232 0.756
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.6880 0.7469 0.736 0.156 0.108
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.6880 0.7469 0.736 0.156 0.108
#> 806616FE-1855-4284-9265-42842104CB21 3 0.6235 0.4657 0.436 0.000 0.564
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.3941 0.8472 0.000 0.844 0.156
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0237 0.8082 0.000 0.996 0.004
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.6880 0.7469 0.736 0.156 0.108
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.4399 0.8444 0.000 0.812 0.188
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.5467 0.7463 0.112 0.072 0.816
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.7706 0.7070 0.264 0.088 0.648
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.5467 0.7463 0.112 0.072 0.816
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.7894 1.000 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.4399 0.8444 0.000 0.812 0.188
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.4399 0.8444 0.000 0.812 0.188
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.7894 1.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.4399 0.8444 0.000 0.812 0.188
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.5710 0.7454 0.116 0.080 0.804
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.3619 0.7262 0.864 0.000 0.136
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0237 0.8082 0.000 0.996 0.004
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.7894 1.000 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.7894 1.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.7894 1.000 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.4660 0.7303 0.072 0.072 0.856
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 3 0.8701 0.3610 0.108 0.400 0.492
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.4399 0.8444 0.000 0.812 0.188
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.6880 0.7469 0.736 0.156 0.108
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.3619 0.7262 0.864 0.000 0.136
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.2200 0.7507 0.940 0.004 0.056
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.7894 1.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.5467 0.7463 0.112 0.072 0.816
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0000 0.7894 1.000 0.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.4790 0.7148 0.056 0.096 0.848
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.4399 0.8444 0.000 0.812 0.188
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.6705 0.7517 0.748 0.144 0.108
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.2066 0.8312 0.000 0.940 0.060
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.3619 0.7262 0.864 0.000 0.136
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.4002 0.7174 0.840 0.000 0.160
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.7894 1.000 0.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.5493 0.6151 0.012 0.232 0.756
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.5772 0.6287 0.024 0.220 0.756
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0424 0.8091 0.000 0.992 0.008
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.4521 0.7630 0.004 0.816 0.180
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.6880 0.7469 0.736 0.156 0.108
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.5591 0.3257 0.696 0.000 0.304
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.3941 0.8472 0.000 0.844 0.156
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.5591 0.3257 0.696 0.000 0.304
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.7065 0.6378 0.316 0.040 0.644
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.7091 0.7456 0.724 0.152 0.124
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.5815 0.3221 0.692 0.004 0.304
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.8408 0.6827 0.612 0.144 0.244
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0237 0.8082 0.000 0.996 0.004
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.3619 0.7262 0.864 0.000 0.136
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.7894 1.000 0.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.5285 0.5943 0.004 0.244 0.752
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.5591 0.3257 0.696 0.000 0.304
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.4399 0.8444 0.000 0.812 0.188
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.1964 0.7617 0.000 0.944 0.056
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.5733 0.5770 0.324 0.000 0.676
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.5493 0.6151 0.012 0.232 0.756
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.1964 0.7506 0.944 0.000 0.056
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.7894 1.000 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.7894 1.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0237 0.7883 0.996 0.000 0.004
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.5722 0.7689 0.804 0.084 0.112
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0892 0.8179 0.000 0.980 0.020
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0237 0.8082 0.000 0.996 0.004
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.3941 0.8472 0.000 0.844 0.156
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.3941 0.8472 0.000 0.844 0.156
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.7248 0.7256 0.708 0.184 0.108
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.4605 0.8310 0.000 0.796 0.204
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.6644 0.7533 0.752 0.140 0.108
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.7894 1.000 0.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.3619 0.7262 0.864 0.000 0.136
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.5334 0.6100 0.060 0.820 0.120
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.3619 0.7262 0.864 0.000 0.136
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0237 0.7887 0.996 0.000 0.004
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.6880 0.7469 0.736 0.156 0.108
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.4399 0.8444 0.000 0.812 0.188
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.2625 0.7313 0.000 0.916 0.084
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.3192 0.8378 0.000 0.888 0.112
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.6705 0.7517 0.748 0.144 0.108
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.7894 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0237 0.8082 0.000 0.996 0.004
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.4399 0.8444 0.000 0.812 0.188
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.5623 0.3834 0.716 0.004 0.280
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.8007 0.6996 0.640 0.116 0.244
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.4399 0.8444 0.000 0.812 0.188
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.7458 0.7133 0.672 0.084 0.244
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.4446 0.7355 0.112 0.032 0.856
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.9106 0.1902 0.208 0.548 0.244
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.7894 1.000 0.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.7091 0.7456 0.724 0.152 0.124
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.4399 0.8444 0.000 0.812 0.188
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.4399 0.8444 0.000 0.812 0.188
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.5733 0.5770 0.324 0.000 0.676
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.7297 0.7224 0.704 0.188 0.108
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.6823 0.7484 0.740 0.152 0.108
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.3941 0.8472 0.000 0.844 0.156
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.3619 0.7262 0.864 0.000 0.136
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.1411 0.7816 0.000 0.964 0.036
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.8129 0.6952 0.632 0.124 0.244
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.7533 0.7117 0.668 0.088 0.244
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.7894 1.000 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.5493 0.6151 0.012 0.232 0.756
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.4399 0.8444 0.000 0.812 0.188
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.6880 0.7469 0.736 0.156 0.108
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.7894 1.000 0.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.7894 1.000 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.3116 0.7038 0.000 0.892 0.108
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.4555 0.6926 0.200 0.000 0.800
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.6975 0.7495 0.732 0.144 0.124
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.7297 0.7224 0.704 0.188 0.108
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.7894 1.000 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.4399 0.8444 0.000 0.812 0.188
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.5591 0.3257 0.696 0.000 0.304
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.5493 0.6151 0.012 0.232 0.756
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.4994 -0.366 0.480 0.000 0.000 0.520
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.6868 0.283 0.120 0.544 0.336 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.7146 0.500 0.228 0.000 0.560 0.212
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.4889 0.491 0.636 0.000 0.004 0.360
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.3873 0.672 0.000 0.000 0.772 0.228
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.4406 0.670 0.300 0.700 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.2011 0.787 0.000 0.080 0.920 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.4992 -0.360 0.476 0.000 0.000 0.524
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.4933 0.460 0.568 0.000 0.000 0.432
#> 806616FE-1855-4284-9265-42842104CB21 3 0.4008 0.650 0.000 0.000 0.756 0.244
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.1004 0.833 0.004 0.972 0.024 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.4406 0.670 0.300 0.700 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.4994 -0.366 0.480 0.000 0.000 0.520
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.3399 0.824 0.092 0.868 0.040 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.4846 0.788 0.180 0.028 0.776 0.016
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.2805 0.772 0.000 0.012 0.888 0.100
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.4846 0.788 0.180 0.028 0.776 0.016
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.616 0.000 0.000 0.000 1.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.1474 0.835 0.000 0.948 0.052 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.1474 0.835 0.000 0.948 0.052 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.616 0.000 0.000 0.000 1.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.1474 0.835 0.000 0.948 0.052 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.4928 0.787 0.188 0.028 0.768 0.016
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.4468 0.475 0.232 0.000 0.016 0.752
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.4406 0.670 0.300 0.700 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.616 0.000 0.000 0.000 1.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.616 0.000 0.000 0.000 1.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.616 0.000 0.000 0.000 1.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.4281 0.782 0.180 0.028 0.792 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.7357 0.199 0.524 0.164 0.308 0.004
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.1474 0.835 0.000 0.948 0.052 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.4994 -0.366 0.480 0.000 0.000 0.520
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.4399 0.481 0.224 0.000 0.016 0.760
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.2714 0.564 0.004 0.000 0.112 0.884
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.616 0.000 0.000 0.000 1.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.4846 0.788 0.180 0.028 0.776 0.016
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.0000 0.616 0.000 0.000 0.000 1.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0469 0.801 0.000 0.012 0.988 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.1474 0.835 0.000 0.948 0.052 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.4985 -0.349 0.468 0.000 0.000 0.532
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.2760 0.800 0.128 0.872 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.4468 0.475 0.232 0.000 0.016 0.752
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.4711 0.465 0.236 0.000 0.024 0.740
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.2408 0.571 0.000 0.000 0.104 0.896
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.2011 0.787 0.000 0.080 0.920 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.4511 0.779 0.176 0.040 0.784 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.5349 0.625 0.336 0.640 0.024 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.6308 0.655 0.232 0.648 0.120 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 4 0.4992 -0.360 0.476 0.000 0.000 0.524
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.4907 0.118 0.000 0.000 0.420 0.580
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0817 0.829 0.024 0.976 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.4907 0.118 0.000 0.000 0.420 0.580
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.3908 0.685 0.000 0.004 0.784 0.212
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.4977 0.426 0.540 0.000 0.000 0.460
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.4907 0.118 0.000 0.000 0.420 0.580
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.4857 0.502 0.700 0.000 0.016 0.284
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.4585 0.626 0.332 0.668 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.4468 0.475 0.232 0.000 0.016 0.752
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0000 0.616 0.000 0.000 0.000 1.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.2473 0.783 0.012 0.080 0.908 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.4907 0.118 0.000 0.000 0.420 0.580
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.1474 0.835 0.000 0.948 0.052 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.4948 0.410 0.440 0.560 0.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.5716 0.632 0.088 0.000 0.700 0.212
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.2011 0.787 0.000 0.080 0.920 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.3942 0.476 0.000 0.000 0.236 0.764
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0000 0.616 0.000 0.000 0.000 1.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.616 0.000 0.000 0.000 1.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.2882 0.579 0.024 0.000 0.084 0.892
#> 352471DC-A881-4EA8-B646-EB1200291893 4 0.5137 -0.312 0.452 0.000 0.004 0.544
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.2868 0.796 0.136 0.864 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.4406 0.670 0.300 0.700 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.2342 0.821 0.080 0.912 0.008 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0817 0.829 0.024 0.976 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.4925 0.464 0.572 0.000 0.000 0.428
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.1716 0.830 0.000 0.936 0.064 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.4941 -0.304 0.436 0.000 0.000 0.564
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0000 0.616 0.000 0.000 0.000 1.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.4468 0.475 0.232 0.000 0.016 0.752
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.5286 0.020 0.604 0.384 0.008 0.004
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.4468 0.475 0.232 0.000 0.016 0.752
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0469 0.611 0.012 0.000 0.000 0.988
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.4994 -0.366 0.480 0.000 0.000 0.520
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.1474 0.835 0.000 0.948 0.052 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 1 0.4985 -0.197 0.532 0.468 0.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.3606 0.807 0.132 0.844 0.024 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.4985 -0.349 0.468 0.000 0.000 0.532
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.616 0.000 0.000 0.000 1.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.4585 0.626 0.332 0.668 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.1474 0.835 0.000 0.948 0.052 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.4877 0.147 0.000 0.000 0.408 0.592
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.5186 0.441 0.640 0.000 0.016 0.344
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.1474 0.835 0.000 0.948 0.052 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.5253 0.410 0.624 0.000 0.016 0.360
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.4911 0.782 0.196 0.024 0.764 0.016
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.5179 0.415 0.760 0.184 0.020 0.036
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0000 0.616 0.000 0.000 0.000 1.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.4977 0.426 0.540 0.000 0.000 0.460
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.1474 0.835 0.000 0.948 0.052 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.1474 0.835 0.000 0.948 0.052 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.6248 0.603 0.128 0.000 0.660 0.212
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.4907 0.469 0.580 0.000 0.000 0.420
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.4992 -0.362 0.476 0.000 0.000 0.524
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0817 0.829 0.024 0.976 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.4468 0.475 0.232 0.000 0.016 0.752
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.4941 0.418 0.436 0.564 0.000 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.5110 0.462 0.656 0.000 0.016 0.328
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.5220 0.427 0.632 0.000 0.016 0.352
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.616 0.000 0.000 0.000 1.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.1940 0.787 0.000 0.076 0.924 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.1474 0.835 0.000 0.948 0.052 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.4994 -0.366 0.480 0.000 0.000 0.520
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0000 0.616 0.000 0.000 0.000 1.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0000 0.616 0.000 0.000 0.000 1.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.4955 -0.121 0.556 0.444 0.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.6136 0.618 0.412 0.016 0.548 0.024
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.5151 0.418 0.532 0.000 0.004 0.464
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.5193 0.475 0.580 0.008 0.000 0.412
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.616 0.000 0.000 0.000 1.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.1474 0.835 0.000 0.948 0.052 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.4907 0.118 0.000 0.000 0.420 0.580
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.4511 0.779 0.176 0.040 0.784 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.3774 0.7201 0.704 0.000 0.000 0.296 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.7146 0.5117 0.164 0.572 0.124 0.000 0.140
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.6749 0.3524 0.164 0.000 0.608 0.084 0.144
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.3928 0.6749 0.788 0.000 0.008 0.176 0.028
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.2074 0.5693 0.000 0.000 0.896 0.104 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.6744 0.5125 0.248 0.456 0.004 0.000 0.292
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.1412 0.4615 0.004 0.036 0.952 0.000 0.008
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.3837 0.7132 0.692 0.000 0.000 0.308 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.3579 0.7165 0.756 0.000 0.000 0.240 0.004
#> 806616FE-1855-4284-9265-42842104CB21 3 0.2127 0.5705 0.000 0.000 0.892 0.108 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0609 0.7724 0.000 0.980 0.000 0.000 0.020
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.6744 0.5125 0.248 0.456 0.004 0.000 0.292
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.3774 0.7201 0.704 0.000 0.000 0.296 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.4620 0.7304 0.060 0.732 0.004 0.000 0.204
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.4881 0.9374 0.000 0.016 0.460 0.004 0.520
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.1699 0.4988 0.004 0.008 0.944 0.036 0.008
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.4881 0.9374 0.000 0.016 0.460 0.004 0.520
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.8277 0.000 0.000 0.000 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.7725 0.000 1.000 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.7725 0.000 1.000 0.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0162 0.8285 0.004 0.000 0.000 0.996 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.7725 0.000 1.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.4881 0.9374 0.000 0.016 0.460 0.004 0.520
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.6189 0.5831 0.216 0.000 0.020 0.612 0.152
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.6744 0.5125 0.248 0.456 0.004 0.000 0.292
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0162 0.8285 0.004 0.000 0.000 0.996 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0162 0.8285 0.004 0.000 0.000 0.996 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0162 0.8285 0.004 0.000 0.000 0.996 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.4818 0.9355 0.000 0.020 0.460 0.000 0.520
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.6724 0.2972 0.560 0.124 0.048 0.000 0.268
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.7725 0.000 1.000 0.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.3774 0.7201 0.704 0.000 0.000 0.296 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.5035 0.6392 0.212 0.000 0.008 0.704 0.076
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.1851 0.7371 0.000 0.000 0.088 0.912 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0162 0.8285 0.004 0.000 0.000 0.996 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.4881 0.9374 0.000 0.016 0.460 0.004 0.520
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.0290 0.8255 0.000 0.000 0.008 0.992 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.1377 0.4424 0.004 0.020 0.956 0.000 0.020
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.7725 0.000 1.000 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.3837 0.7133 0.692 0.000 0.000 0.308 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.5238 0.6911 0.064 0.640 0.004 0.000 0.292
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.6189 0.5831 0.216 0.000 0.020 0.612 0.152
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.6407 0.5531 0.220 0.000 0.020 0.584 0.176
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.1792 0.7533 0.000 0.000 0.084 0.916 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.1412 0.4615 0.004 0.036 0.952 0.000 0.008
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.4818 0.9355 0.000 0.020 0.460 0.000 0.520
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.6890 0.3356 0.352 0.380 0.004 0.000 0.264
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.7688 0.4878 0.228 0.416 0.064 0.000 0.292
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.3857 0.7112 0.688 0.000 0.000 0.312 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.4302 0.3703 0.000 0.000 0.520 0.480 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.2825 0.7639 0.016 0.860 0.000 0.000 0.124
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.4302 0.3703 0.000 0.000 0.520 0.480 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.2249 0.5668 0.000 0.008 0.896 0.096 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.3676 0.7199 0.760 0.000 0.004 0.232 0.004
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.4302 0.3703 0.000 0.000 0.520 0.480 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.3873 0.5971 0.820 0.000 0.008 0.084 0.088
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.6865 0.4415 0.288 0.416 0.004 0.000 0.292
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.6189 0.5831 0.216 0.000 0.020 0.612 0.152
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0162 0.8268 0.000 0.000 0.004 0.996 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.2568 0.4479 0.004 0.016 0.888 0.000 0.092
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.4302 0.3703 0.000 0.000 0.520 0.480 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0162 0.7723 0.000 0.996 0.000 0.000 0.004
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 1 0.6744 -0.0372 0.456 0.248 0.004 0.000 0.292
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.4237 0.5412 0.040 0.000 0.812 0.084 0.064
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.1412 0.4615 0.004 0.036 0.952 0.000 0.008
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.4030 0.1724 0.000 0.000 0.352 0.648 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0162 0.8285 0.004 0.000 0.000 0.996 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0162 0.8285 0.004 0.000 0.000 0.996 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.3060 0.6952 0.024 0.000 0.128 0.848 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.5156 0.6146 0.620 0.000 0.008 0.332 0.040
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.5238 0.6911 0.064 0.640 0.004 0.000 0.292
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.6744 0.5125 0.248 0.456 0.004 0.000 0.292
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.4488 0.7373 0.060 0.748 0.004 0.000 0.188
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.2513 0.7658 0.008 0.876 0.000 0.000 0.116
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.3789 0.7153 0.768 0.000 0.000 0.212 0.020
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.7725 0.000 1.000 0.000 0.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.4430 0.6546 0.628 0.000 0.000 0.360 0.012
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0162 0.8268 0.000 0.000 0.004 0.996 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.6352 0.5640 0.216 0.000 0.020 0.592 0.172
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.5810 0.3677 0.632 0.136 0.008 0.000 0.224
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.6189 0.5831 0.216 0.000 0.020 0.612 0.152
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.1571 0.7993 0.060 0.000 0.004 0.936 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.3774 0.7201 0.704 0.000 0.000 0.296 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0162 0.7723 0.000 0.996 0.000 0.000 0.004
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 1 0.6444 0.1536 0.516 0.188 0.004 0.000 0.292
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.5830 0.6680 0.140 0.616 0.004 0.000 0.240
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.3837 0.7133 0.692 0.000 0.000 0.308 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0162 0.8285 0.004 0.000 0.000 0.996 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.6865 0.4415 0.288 0.416 0.004 0.000 0.292
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.7725 0.000 1.000 0.000 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.4302 0.3703 0.000 0.000 0.520 0.480 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.5772 0.4402 0.660 0.000 0.016 0.148 0.176
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.7725 0.000 1.000 0.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.5844 0.4291 0.652 0.000 0.016 0.156 0.176
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.5045 0.9255 0.008 0.012 0.456 0.004 0.520
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.4425 0.4826 0.740 0.036 0.008 0.000 0.216
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0162 0.8285 0.004 0.000 0.000 0.996 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.3676 0.7199 0.760 0.000 0.004 0.232 0.004
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.7725 0.000 1.000 0.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.7725 0.000 1.000 0.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.4450 0.5329 0.052 0.000 0.800 0.084 0.064
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.4333 0.7050 0.752 0.000 0.000 0.188 0.060
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.3774 0.7201 0.704 0.000 0.000 0.296 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.2513 0.7658 0.008 0.876 0.000 0.000 0.116
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.6189 0.5831 0.216 0.000 0.020 0.612 0.152
#> B3561356-5A80-4C79-B23A-D518425565FE 1 0.6904 -0.2140 0.396 0.308 0.004 0.000 0.292
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.5734 0.4461 0.664 0.000 0.016 0.144 0.176
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.5808 0.4335 0.656 0.000 0.016 0.152 0.176
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0162 0.8285 0.004 0.000 0.000 0.996 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.1412 0.4615 0.004 0.036 0.952 0.000 0.008
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.7725 0.000 1.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.3796 0.7180 0.700 0.000 0.000 0.300 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0162 0.8285 0.004 0.000 0.000 0.996 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0162 0.8268 0.000 0.000 0.004 0.996 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.6139 0.2577 0.560 0.148 0.004 0.000 0.288
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.6285 0.5623 0.140 0.012 0.256 0.004 0.588
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.4125 0.7126 0.740 0.000 0.004 0.236 0.020
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.4409 0.7020 0.752 0.000 0.000 0.176 0.072
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0162 0.8285 0.004 0.000 0.000 0.996 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.7725 0.000 1.000 0.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.4302 0.3703 0.000 0.000 0.520 0.480 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.4818 0.9355 0.000 0.020 0.460 0.000 0.520
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.6847 0.309 0.400 0.000 0.004 0.224 0.044 0.328
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.7371 0.486 0.204 0.492 0.036 0.000 0.096 0.172
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.4788 0.417 0.004 0.000 0.608 0.028 0.016 0.344
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 6 0.6136 -0.183 0.348 0.000 0.000 0.120 0.040 0.492
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.1152 0.715 0.004 0.000 0.952 0.044 0.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 1 0.4428 0.205 0.708 0.228 0.016 0.000 0.048 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.1514 0.686 0.016 0.016 0.948 0.000 0.004 0.016
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.6890 0.300 0.388 0.000 0.004 0.240 0.044 0.324
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.6775 0.320 0.416 0.000 0.004 0.188 0.048 0.344
#> 806616FE-1855-4284-9265-42842104CB21 3 0.1429 0.715 0.000 0.000 0.940 0.052 0.004 0.004
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0858 0.879 0.004 0.968 0.000 0.000 0.028 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 1 0.4454 0.196 0.704 0.232 0.016 0.000 0.048 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.6847 0.309 0.400 0.000 0.004 0.224 0.044 0.328
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.6021 0.556 0.264 0.572 0.008 0.000 0.124 0.032
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.3215 0.944 0.000 0.004 0.240 0.000 0.756 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.1312 0.700 0.012 0.000 0.956 0.020 0.004 0.008
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.3215 0.944 0.000 0.004 0.240 0.000 0.756 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0405 0.907 0.008 0.000 0.000 0.988 0.000 0.004
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0146 0.886 0.000 0.996 0.000 0.000 0.004 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0146 0.886 0.000 0.996 0.000 0.000 0.000 0.004
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.910 0.000 0.000 0.000 1.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0146 0.886 0.000 0.996 0.000 0.000 0.004 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.3163 0.938 0.000 0.004 0.232 0.000 0.764 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 6 0.4432 0.318 0.000 0.000 0.004 0.432 0.020 0.544
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 1 0.4428 0.205 0.708 0.228 0.016 0.000 0.048 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.910 0.000 0.000 0.000 1.000 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.910 0.000 0.000 0.000 1.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.910 0.000 0.000 0.000 1.000 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.3215 0.944 0.000 0.004 0.240 0.000 0.756 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.3884 0.396 0.812 0.012 0.020 0.000 0.092 0.064
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0146 0.886 0.000 0.996 0.000 0.000 0.000 0.004
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.6847 0.309 0.400 0.000 0.004 0.224 0.044 0.328
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.4291 0.214 0.000 0.000 0.008 0.620 0.016 0.356
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.2302 0.840 0.008 0.000 0.060 0.900 0.000 0.032
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.910 0.000 0.000 0.000 1.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.3215 0.944 0.000 0.004 0.240 0.000 0.756 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.1194 0.894 0.008 0.000 0.004 0.956 0.000 0.032
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.1458 0.678 0.016 0.000 0.948 0.000 0.020 0.016
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0146 0.886 0.000 0.996 0.000 0.000 0.004 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.6883 0.300 0.388 0.000 0.004 0.236 0.044 0.328
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 1 0.5029 -0.161 0.568 0.368 0.016 0.000 0.048 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 6 0.4432 0.318 0.000 0.000 0.004 0.432 0.020 0.544
#> CB925BF0-1249-4350-A175-9A4129C43B8D 6 0.4700 0.382 0.004 0.000 0.004 0.364 0.036 0.592
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.2420 0.832 0.008 0.000 0.068 0.892 0.000 0.032
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.1514 0.686 0.016 0.016 0.948 0.000 0.004 0.016
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.3215 0.944 0.000 0.004 0.240 0.000 0.756 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.5305 0.237 0.664 0.212 0.004 0.000 0.084 0.036
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 1 0.4467 0.221 0.712 0.216 0.016 0.000 0.056 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.6900 0.296 0.384 0.000 0.004 0.244 0.044 0.324
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.4605 0.523 0.008 0.000 0.596 0.364 0.000 0.032
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.4253 0.718 0.196 0.728 0.000 0.000 0.072 0.004
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.4626 0.508 0.008 0.000 0.588 0.372 0.000 0.032
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.1007 0.715 0.000 0.000 0.956 0.044 0.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.6540 0.257 0.392 0.000 0.000 0.180 0.040 0.388
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.4605 0.523 0.008 0.000 0.596 0.364 0.000 0.032
#> B5474EEB-D585-4668-959C-38F240F55BC2 6 0.4749 0.117 0.252 0.000 0.000 0.032 0.040 0.676
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 1 0.4377 0.218 0.716 0.220 0.016 0.000 0.048 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 6 0.4432 0.318 0.000 0.000 0.004 0.432 0.020 0.544
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0622 0.904 0.008 0.000 0.000 0.980 0.000 0.012
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.1553 0.688 0.032 0.004 0.944 0.000 0.012 0.008
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.4616 0.517 0.008 0.000 0.592 0.368 0.000 0.032
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.1155 0.876 0.004 0.956 0.000 0.000 0.036 0.004
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 1 0.2945 0.385 0.864 0.072 0.016 0.000 0.048 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.3050 0.682 0.004 0.000 0.856 0.028 0.016 0.096
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.1514 0.686 0.016 0.016 0.948 0.000 0.004 0.016
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.4605 0.201 0.008 0.000 0.364 0.596 0.000 0.032
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0000 0.910 0.000 0.000 0.000 1.000 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.910 0.000 0.000 0.000 1.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.4066 0.678 0.008 0.000 0.152 0.776 0.012 0.052
#> 352471DC-A881-4EA8-B646-EB1200291893 6 0.6603 -0.118 0.268 0.000 0.000 0.240 0.040 0.452
#> F779417A-9E29-4B27-BEA3-B23273A66021 1 0.5009 -0.139 0.576 0.360 0.016 0.000 0.048 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 1 0.4428 0.205 0.708 0.228 0.016 0.000 0.048 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.5727 0.578 0.260 0.596 0.004 0.000 0.112 0.028
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.4488 0.686 0.224 0.704 0.012 0.000 0.060 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.6572 0.327 0.464 0.000 0.004 0.164 0.044 0.324
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0146 0.886 0.000 0.996 0.000 0.000 0.004 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.6941 0.261 0.360 0.000 0.004 0.260 0.044 0.332
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0806 0.900 0.008 0.000 0.000 0.972 0.000 0.020
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 6 0.4658 0.374 0.000 0.000 0.004 0.376 0.040 0.580
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.4653 0.385 0.740 0.024 0.004 0.000 0.108 0.124
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 6 0.4528 0.316 0.000 0.000 0.008 0.428 0.020 0.544
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.1462 0.849 0.000 0.000 0.000 0.936 0.008 0.056
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.6847 0.309 0.400 0.000 0.004 0.224 0.044 0.328
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.1155 0.876 0.004 0.956 0.000 0.000 0.036 0.004
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 1 0.2430 0.416 0.900 0.036 0.012 0.000 0.048 0.004
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.6199 -0.308 0.436 0.420 0.004 0.000 0.096 0.044
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.6883 0.300 0.388 0.000 0.004 0.236 0.044 0.328
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.910 0.000 0.000 0.000 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 1 0.4350 0.224 0.720 0.216 0.016 0.000 0.048 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0146 0.886 0.000 0.996 0.000 0.000 0.004 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.4616 0.517 0.008 0.000 0.592 0.368 0.000 0.032
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 6 0.2699 0.460 0.040 0.000 0.000 0.048 0.028 0.884
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0146 0.886 0.000 0.996 0.000 0.000 0.000 0.004
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 6 0.2985 0.464 0.044 0.000 0.004 0.048 0.032 0.872
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.3215 0.944 0.000 0.004 0.240 0.000 0.756 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.5380 0.258 0.600 0.008 0.004 0.000 0.108 0.280
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.1049 0.894 0.000 0.000 0.008 0.960 0.000 0.032
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.6540 0.257 0.392 0.000 0.000 0.180 0.040 0.388
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0146 0.886 0.000 0.996 0.000 0.000 0.000 0.004
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0146 0.886 0.000 0.996 0.000 0.000 0.000 0.004
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.3050 0.682 0.004 0.000 0.856 0.028 0.016 0.096
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.6326 0.335 0.516 0.000 0.004 0.136 0.044 0.300
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.6872 0.304 0.392 0.000 0.004 0.232 0.044 0.328
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.4488 0.686 0.224 0.704 0.012 0.000 0.060 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 6 0.4432 0.318 0.000 0.000 0.004 0.432 0.020 0.544
#> B3561356-5A80-4C79-B23A-D518425565FE 1 0.3715 0.328 0.800 0.132 0.016 0.000 0.052 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 6 0.2701 0.455 0.044 0.000 0.000 0.044 0.028 0.884
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 6 0.2842 0.465 0.040 0.000 0.004 0.048 0.028 0.880
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.910 0.000 0.000 0.000 1.000 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.1514 0.686 0.016 0.016 0.948 0.000 0.004 0.016
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0146 0.886 0.000 0.996 0.000 0.000 0.004 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.6872 0.304 0.392 0.000 0.004 0.232 0.044 0.328
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0000 0.910 0.000 0.000 0.000 1.000 0.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0363 0.906 0.000 0.000 0.000 0.988 0.000 0.012
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.3606 0.405 0.828 0.028 0.004 0.000 0.088 0.052
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.4929 0.511 0.004 0.000 0.072 0.000 0.600 0.324
#> 12F54761-4F68-4181-8421-88EA858902FC 6 0.6549 -0.289 0.364 0.000 0.000 0.184 0.040 0.412
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.6153 0.337 0.540 0.000 0.004 0.116 0.044 0.296
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.910 0.000 0.000 0.000 1.000 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0146 0.886 0.000 0.996 0.000 0.000 0.000 0.004
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.4605 0.523 0.008 0.000 0.596 0.364 0.000 0.032
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.3215 0.944 0.000 0.004 0.240 0.000 0.756 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["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 17548 rows and 122 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 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 0.992 0.996 0.5012 0.499 0.499
#> 3 3 0.673 0.816 0.867 0.3095 0.795 0.610
#> 4 4 0.822 0.888 0.922 0.1339 0.853 0.611
#> 5 5 0.896 0.820 0.871 0.0464 0.944 0.792
#> 6 6 0.831 0.775 0.843 0.0476 0.953 0.790
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.997 1.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.0000 0.996 0.000 1.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.997 1.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.997 1.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 2 0.7453 0.731 0.212 0.788
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.996 0.000 1.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 2 0.0000 0.996 0.000 1.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.997 1.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0000 0.997 1.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 1 0.0000 0.997 1.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.996 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.996 0.000 1.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.997 1.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.996 0.000 1.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 2 0.0000 0.996 0.000 1.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 2 0.0376 0.992 0.004 0.996
#> 853120F0-857B-4108-9EC8-727189630C5F 2 0.0000 0.996 0.000 1.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.997 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.996 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.996 0.000 1.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.997 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.996 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 2 0.0000 0.996 0.000 1.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.997 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.996 0.000 1.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.997 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.997 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.997 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 2 0.0000 0.996 0.000 1.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.0000 0.996 0.000 1.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.996 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.997 1.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.997 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.0000 0.997 1.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.997 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 2 0.0000 0.996 0.000 1.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0000 0.997 1.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 2 0.0000 0.996 0.000 1.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.996 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.997 1.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.996 0.000 1.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.997 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.997 1.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.997 1.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.0000 0.996 0.000 1.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.0000 0.996 0.000 1.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.996 0.000 1.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0000 0.996 0.000 1.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.997 1.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.0000 0.997 1.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.996 0.000 1.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.0000 0.997 1.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 2 0.1184 0.980 0.016 0.984
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.997 1.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.0000 0.997 1.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.997 1.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.996 0.000 1.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.997 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.997 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.0000 0.996 0.000 1.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.0000 0.997 1.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.996 0.000 1.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.996 0.000 1.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.0000 0.997 1.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.0000 0.996 0.000 1.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.0000 0.997 1.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.997 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.997 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0000 0.997 1.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.997 1.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.996 0.000 1.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.996 0.000 1.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.996 0.000 1.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.996 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.997 1.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.996 0.000 1.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.997 1.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.997 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.997 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.0000 0.996 0.000 1.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.997 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.997 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.997 1.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.996 0.000 1.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0000 0.996 0.000 1.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0000 0.996 0.000 1.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.997 1.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.997 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.996 0.000 1.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.996 0.000 1.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.0000 0.997 1.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.997 1.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.996 0.000 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.997 1.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 2 0.0000 0.996 0.000 1.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.0000 0.996 0.000 1.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.997 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.997 1.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.996 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.996 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0376 0.993 0.996 0.004
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0000 0.997 1.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.997 1.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.996 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.997 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.996 0.000 1.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.997 1.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.997 1.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.997 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.0000 0.996 0.000 1.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.996 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.997 1.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.997 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.997 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0000 0.996 0.000 1.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.7376 0.737 0.792 0.208
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.997 1.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.0000 0.997 1.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.997 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.996 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.0000 0.997 1.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.0000 0.996 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.5397 0.813 0.720 0.000 0.280
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.0892 0.889 0.000 0.980 0.020
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.5397 0.780 0.280 0.000 0.720
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.5497 0.810 0.708 0.000 0.292
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.5953 0.784 0.280 0.012 0.708
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.4555 0.806 0.000 0.800 0.200
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.5529 0.759 0.000 0.296 0.704
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.5397 0.813 0.720 0.000 0.280
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.5397 0.813 0.720 0.000 0.280
#> 806616FE-1855-4284-9265-42842104CB21 3 0.5397 0.780 0.280 0.000 0.720
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.903 0.000 1.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.4555 0.806 0.000 0.800 0.200
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.5397 0.813 0.720 0.000 0.280
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.903 0.000 1.000 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.5431 0.765 0.000 0.284 0.716
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.5953 0.784 0.280 0.012 0.708
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.5397 0.766 0.000 0.280 0.720
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.828 1.000 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.903 0.000 1.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.903 0.000 1.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.828 1.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.903 0.000 1.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.5560 0.756 0.000 0.300 0.700
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0892 0.823 0.980 0.000 0.020
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.4555 0.806 0.000 0.800 0.200
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.828 1.000 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.828 1.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.828 1.000 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.5397 0.766 0.000 0.280 0.720
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.0000 0.903 0.000 1.000 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.903 0.000 1.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.5397 0.813 0.720 0.000 0.280
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0892 0.823 0.980 0.000 0.020
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.5465 0.304 0.712 0.000 0.288
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.828 1.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.5431 0.765 0.000 0.284 0.716
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0892 0.823 0.980 0.000 0.020
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.5588 0.768 0.004 0.276 0.720
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.903 0.000 1.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.5397 0.813 0.720 0.000 0.280
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0592 0.899 0.000 0.988 0.012
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0892 0.823 0.980 0.000 0.020
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0892 0.823 0.980 0.000 0.020
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.828 1.000 0.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.5560 0.756 0.000 0.300 0.700
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.5560 0.756 0.000 0.300 0.700
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.1529 0.888 0.000 0.960 0.040
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0000 0.903 0.000 1.000 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.5397 0.813 0.720 0.000 0.280
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.5560 0.775 0.300 0.000 0.700
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.903 0.000 1.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.5497 0.778 0.292 0.000 0.708
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.5397 0.780 0.280 0.000 0.720
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.5497 0.810 0.708 0.000 0.292
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.5560 0.775 0.300 0.000 0.700
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.5560 0.807 0.700 0.000 0.300
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.4555 0.806 0.000 0.800 0.200
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0892 0.823 0.980 0.000 0.020
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.828 1.000 0.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.5560 0.756 0.000 0.300 0.700
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.5560 0.775 0.300 0.000 0.700
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.903 0.000 1.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.4796 0.788 0.000 0.780 0.220
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.5397 0.780 0.280 0.000 0.720
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.5560 0.756 0.000 0.300 0.700
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.6180 0.603 0.416 0.000 0.584
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.828 1.000 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.828 1.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.1860 0.794 0.948 0.000 0.052
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.4931 0.819 0.768 0.000 0.232
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.1529 0.888 0.000 0.960 0.040
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.4555 0.806 0.000 0.800 0.200
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.903 0.000 1.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.903 0.000 1.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.5397 0.813 0.720 0.000 0.280
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.903 0.000 1.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.5397 0.813 0.720 0.000 0.280
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.828 1.000 0.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0892 0.823 0.980 0.000 0.020
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.5431 0.725 0.000 0.716 0.284
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0892 0.823 0.980 0.000 0.020
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.828 1.000 0.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.5397 0.813 0.720 0.000 0.280
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.903 0.000 1.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.5327 0.735 0.000 0.728 0.272
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0000 0.903 0.000 1.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.5397 0.813 0.720 0.000 0.280
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.828 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.4555 0.806 0.000 0.800 0.200
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.903 0.000 1.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.5560 0.775 0.300 0.000 0.700
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.5560 0.807 0.700 0.000 0.300
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.903 0.000 1.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.4796 0.820 0.780 0.000 0.220
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.5588 0.768 0.004 0.276 0.720
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.5529 0.715 0.000 0.704 0.296
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.828 1.000 0.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.5497 0.810 0.708 0.000 0.292
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.903 0.000 1.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.903 0.000 1.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.5397 0.780 0.280 0.000 0.720
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.5397 0.813 0.720 0.000 0.280
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.5397 0.813 0.720 0.000 0.280
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.903 0.000 1.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0892 0.823 0.980 0.000 0.020
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.4555 0.806 0.000 0.800 0.200
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.5560 0.807 0.700 0.000 0.300
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.5560 0.807 0.700 0.000 0.300
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.828 1.000 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.5560 0.756 0.000 0.300 0.700
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.903 0.000 1.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.5397 0.813 0.720 0.000 0.280
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.828 1.000 0.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.828 1.000 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.4796 0.788 0.000 0.780 0.220
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.6922 0.784 0.080 0.200 0.720
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.5497 0.810 0.708 0.000 0.292
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.5397 0.813 0.720 0.000 0.280
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.828 1.000 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.903 0.000 1.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.5497 0.778 0.292 0.000 0.708
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.5560 0.756 0.000 0.300 0.700
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.3873 0.889 0.772 0.000 0.000 0.228
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.4638 0.739 0.180 0.776 0.044 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.3945 0.775 0.216 0.000 0.780 0.004
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0592 0.831 0.984 0.000 0.000 0.016
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0188 0.941 0.000 0.000 0.996 0.004
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.3873 0.889 0.772 0.000 0.000 0.228
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.3873 0.889 0.772 0.000 0.000 0.228
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0188 0.941 0.000 0.000 0.996 0.004
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.3873 0.889 0.772 0.000 0.000 0.228
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0188 0.976 0.000 0.996 0.004 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.0592 0.943 0.016 0.000 0.984 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.0592 0.943 0.016 0.000 0.984 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0188 0.976 0.000 0.996 0.004 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0188 0.976 0.000 0.996 0.004 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0188 0.976 0.000 0.996 0.004 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.0592 0.943 0.016 0.000 0.984 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.3764 0.808 0.216 0.000 0.000 0.784
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.0592 0.943 0.016 0.000 0.984 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0188 0.976 0.000 0.996 0.004 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.3873 0.889 0.772 0.000 0.000 0.228
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.3764 0.808 0.216 0.000 0.000 0.784
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.0592 0.856 0.000 0.000 0.016 0.984
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.0592 0.943 0.016 0.000 0.984 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.3764 0.808 0.216 0.000 0.000 0.784
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0188 0.976 0.000 0.996 0.004 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.3873 0.889 0.772 0.000 0.000 0.228
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.3764 0.808 0.216 0.000 0.000 0.784
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.4609 0.793 0.224 0.000 0.024 0.752
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.0592 0.856 0.000 0.000 0.016 0.984
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.0657 0.942 0.012 0.004 0.984 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.3873 0.889 0.772 0.000 0.000 0.228
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.3873 0.766 0.000 0.000 0.228 0.772
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.3873 0.766 0.000 0.000 0.228 0.772
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0592 0.831 0.984 0.000 0.000 0.016
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.3873 0.766 0.000 0.000 0.228 0.772
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0469 0.828 0.988 0.000 0.000 0.012
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.3764 0.808 0.216 0.000 0.000 0.784
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.1637 0.895 0.000 0.060 0.940 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.3873 0.766 0.000 0.000 0.228 0.772
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0188 0.976 0.000 0.996 0.004 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.2999 0.856 0.132 0.000 0.864 0.004
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.3873 0.766 0.000 0.000 0.228 0.772
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.4290 0.806 0.212 0.000 0.016 0.772
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0592 0.831 0.984 0.000 0.000 0.016
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.3873 0.889 0.772 0.000 0.000 0.228
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0188 0.976 0.000 0.996 0.004 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.3873 0.889 0.772 0.000 0.000 0.228
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.3764 0.808 0.216 0.000 0.000 0.784
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.0817 0.956 0.024 0.976 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.3764 0.808 0.216 0.000 0.000 0.784
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.3569 0.816 0.196 0.000 0.000 0.804
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.3873 0.889 0.772 0.000 0.000 0.228
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0188 0.976 0.000 0.996 0.004 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.3610 0.739 0.200 0.800 0.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.3873 0.889 0.772 0.000 0.000 0.228
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0188 0.976 0.000 0.996 0.004 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.3873 0.766 0.000 0.000 0.228 0.772
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0469 0.828 0.988 0.000 0.000 0.012
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0188 0.976 0.000 0.996 0.004 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0469 0.828 0.988 0.000 0.000 0.012
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.3444 0.822 0.184 0.000 0.816 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.4830 0.444 0.392 0.608 0.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0592 0.831 0.984 0.000 0.000 0.016
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0188 0.976 0.000 0.996 0.004 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0188 0.976 0.000 0.996 0.004 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.3494 0.821 0.172 0.000 0.824 0.004
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.3873 0.889 0.772 0.000 0.000 0.228
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.3873 0.889 0.772 0.000 0.000 0.228
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.3764 0.808 0.216 0.000 0.000 0.784
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0469 0.828 0.988 0.000 0.000 0.012
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0469 0.828 0.988 0.000 0.000 0.012
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0188 0.976 0.000 0.996 0.004 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.3873 0.889 0.772 0.000 0.000 0.228
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.3873 0.774 0.228 0.000 0.772 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0592 0.831 0.984 0.000 0.000 0.016
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.3873 0.889 0.772 0.000 0.000 0.228
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.859 0.000 0.000 0.000 1.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0188 0.976 0.000 0.996 0.004 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.3873 0.766 0.000 0.000 0.228 0.772
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.0657 0.942 0.012 0.004 0.984 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.1478 0.907 0.936 0.000 0.000 0.064 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.4003 0.625 0.000 0.704 0.008 0.000 0.288
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.5133 0.471 0.056 0.000 0.664 0.008 0.272
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0162 0.880 0.996 0.000 0.000 0.000 0.004
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0290 0.660 0.000 0.000 0.992 0.008 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.1043 0.948 0.000 0.960 0.000 0.000 0.040
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0000 0.658 0.000 0.000 1.000 0.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.1478 0.907 0.936 0.000 0.000 0.064 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.1478 0.907 0.936 0.000 0.000 0.064 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0290 0.660 0.000 0.000 0.992 0.008 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0290 0.955 0.000 0.992 0.008 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.1043 0.948 0.000 0.960 0.000 0.000 0.040
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.1478 0.907 0.936 0.000 0.000 0.064 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0290 0.955 0.000 0.992 0.008 0.000 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.3857 0.880 0.000 0.000 0.312 0.000 0.688
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0290 0.654 0.000 0.000 0.992 0.000 0.008
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.3857 0.880 0.000 0.000 0.312 0.000 0.688
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0290 0.955 0.000 0.992 0.008 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0290 0.955 0.000 0.992 0.008 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0290 0.955 0.000 0.992 0.008 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.3857 0.880 0.000 0.000 0.312 0.000 0.688
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.4983 0.656 0.064 0.000 0.000 0.664 0.272
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.1043 0.948 0.000 0.960 0.000 0.000 0.040
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.3857 0.880 0.000 0.000 0.312 0.000 0.688
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.1121 0.929 0.000 0.956 0.000 0.000 0.044
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0290 0.955 0.000 0.992 0.008 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.1478 0.907 0.936 0.000 0.000 0.064 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.4960 0.659 0.064 0.000 0.000 0.668 0.268
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.3857 0.880 0.000 0.000 0.312 0.000 0.688
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.1478 0.849 0.064 0.000 0.000 0.936 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0290 0.654 0.000 0.000 0.992 0.000 0.008
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0290 0.955 0.000 0.992 0.008 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.1478 0.907 0.936 0.000 0.000 0.064 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.1043 0.948 0.000 0.960 0.000 0.000 0.040
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.4983 0.656 0.064 0.000 0.000 0.664 0.272
#> CB925BF0-1249-4350-A175-9A4129C43B8D 5 0.4332 0.384 0.064 0.000 0.004 0.164 0.768
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.0510 0.873 0.000 0.000 0.016 0.984 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0000 0.658 0.000 0.000 1.000 0.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.3857 0.880 0.000 0.000 0.312 0.000 0.688
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0290 0.955 0.000 0.992 0.008 0.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.1671 0.926 0.000 0.924 0.000 0.000 0.076
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.1478 0.907 0.936 0.000 0.000 0.064 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.4227 0.487 0.000 0.000 0.580 0.420 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.954 0.000 1.000 0.000 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.4235 0.480 0.000 0.000 0.576 0.424 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0000 0.658 0.000 0.000 1.000 0.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.881 1.000 0.000 0.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.4210 0.498 0.000 0.000 0.588 0.412 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.3561 0.681 0.740 0.000 0.000 0.000 0.260
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.1043 0.948 0.000 0.960 0.000 0.000 0.040
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.4983 0.656 0.064 0.000 0.000 0.664 0.272
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0162 0.881 0.004 0.000 0.000 0.996 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.0404 0.645 0.000 0.012 0.988 0.000 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.4227 0.487 0.000 0.000 0.580 0.420 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0290 0.955 0.000 0.992 0.008 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.1043 0.948 0.000 0.960 0.000 0.000 0.040
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.3861 0.535 0.000 0.000 0.728 0.008 0.264
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0000 0.658 0.000 0.000 1.000 0.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.1965 0.795 0.000 0.000 0.096 0.904 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.1628 0.849 0.056 0.000 0.008 0.936 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0404 0.876 0.988 0.000 0.000 0.000 0.012
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.1043 0.948 0.000 0.960 0.000 0.000 0.040
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.1043 0.948 0.000 0.960 0.000 0.000 0.040
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0290 0.955 0.000 0.992 0.008 0.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0794 0.951 0.000 0.972 0.000 0.000 0.028
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.1478 0.907 0.936 0.000 0.000 0.064 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.1628 0.920 0.000 0.936 0.008 0.000 0.056
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.1478 0.907 0.936 0.000 0.000 0.064 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0404 0.878 0.012 0.000 0.000 0.988 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.4983 0.656 0.064 0.000 0.000 0.664 0.272
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.0566 0.953 0.004 0.984 0.000 0.000 0.012
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.4983 0.656 0.064 0.000 0.000 0.664 0.272
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.1478 0.849 0.064 0.000 0.000 0.936 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.1478 0.907 0.936 0.000 0.000 0.064 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0290 0.955 0.000 0.992 0.008 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.4028 0.726 0.192 0.768 0.000 0.000 0.040
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0290 0.955 0.000 0.992 0.008 0.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.1478 0.907 0.936 0.000 0.000 0.064 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.1043 0.948 0.000 0.960 0.000 0.000 0.040
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0290 0.955 0.000 0.992 0.008 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.4294 0.379 0.000 0.000 0.532 0.468 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.3636 0.669 0.728 0.000 0.000 0.000 0.272
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0290 0.955 0.000 0.992 0.008 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.3636 0.669 0.728 0.000 0.000 0.000 0.272
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.3857 0.880 0.000 0.000 0.312 0.000 0.688
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.5901 0.405 0.148 0.584 0.000 0.000 0.268
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.881 1.000 0.000 0.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0290 0.955 0.000 0.992 0.008 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0290 0.955 0.000 0.992 0.008 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.4039 0.529 0.004 0.000 0.720 0.008 0.268
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.1478 0.907 0.936 0.000 0.000 0.064 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.1478 0.907 0.936 0.000 0.000 0.064 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0794 0.951 0.000 0.972 0.000 0.000 0.028
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.4983 0.656 0.064 0.000 0.000 0.664 0.272
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.1043 0.948 0.000 0.960 0.000 0.000 0.040
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.3636 0.669 0.728 0.000 0.000 0.000 0.272
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.3636 0.669 0.728 0.000 0.000 0.000 0.272
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0000 0.658 0.000 0.000 1.000 0.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0290 0.955 0.000 0.992 0.008 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.1478 0.907 0.936 0.000 0.000 0.064 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.1043 0.948 0.000 0.960 0.000 0.000 0.040
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.1043 0.602 0.000 0.000 0.040 0.000 0.960
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0162 0.880 0.996 0.000 0.000 0.000 0.004
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.1764 0.902 0.928 0.000 0.000 0.064 0.008
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0290 0.955 0.000 0.992 0.008 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.4242 0.472 0.000 0.000 0.572 0.428 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.3857 0.880 0.000 0.000 0.312 0.000 0.688
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0935 0.8402 0.964 0.000 0.000 0.032 0.000 0.004
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.3901 0.5342 0.000 0.768 0.000 0.000 0.096 0.136
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.5188 0.4699 0.032 0.000 0.616 0.008 0.036 0.308
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.2964 0.7416 0.792 0.000 0.000 0.004 0.000 0.204
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.8473 0.000 0.000 1.000 0.000 0.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 6 0.3804 0.8784 0.000 0.424 0.000 0.000 0.000 0.576
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0146 0.8464 0.000 0.000 0.996 0.000 0.004 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0935 0.8402 0.964 0.000 0.000 0.032 0.000 0.004
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0935 0.8402 0.964 0.000 0.000 0.032 0.000 0.004
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0000 0.8473 0.000 0.000 1.000 0.000 0.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.8954 0.000 1.000 0.000 0.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 6 0.3810 0.8750 0.000 0.428 0.000 0.000 0.000 0.572
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0935 0.8402 0.964 0.000 0.000 0.032 0.000 0.004
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0622 0.8779 0.000 0.980 0.000 0.000 0.008 0.012
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.1007 0.9088 0.000 0.000 0.044 0.000 0.956 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0000 0.8473 0.000 0.000 1.000 0.000 0.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.1007 0.9088 0.000 0.000 0.044 0.000 0.956 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0260 0.8578 0.008 0.000 0.000 0.992 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.8954 0.000 1.000 0.000 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.8954 0.000 1.000 0.000 0.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0260 0.8578 0.008 0.000 0.000 0.992 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.8954 0.000 1.000 0.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.1007 0.9088 0.000 0.000 0.044 0.000 0.956 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.5129 0.5715 0.032 0.000 0.000 0.568 0.036 0.364
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 6 0.3804 0.8784 0.000 0.424 0.000 0.000 0.000 0.576
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0260 0.8578 0.008 0.000 0.000 0.992 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0260 0.8578 0.008 0.000 0.000 0.992 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0260 0.8578 0.008 0.000 0.000 0.992 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.1007 0.9088 0.000 0.000 0.044 0.000 0.956 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.2618 0.7469 0.000 0.872 0.000 0.000 0.076 0.052
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.8954 0.000 1.000 0.000 0.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0935 0.8402 0.964 0.000 0.000 0.032 0.000 0.004
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.4995 0.6066 0.032 0.000 0.000 0.612 0.036 0.320
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.0551 0.8532 0.004 0.000 0.004 0.984 0.000 0.008
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0260 0.8578 0.008 0.000 0.000 0.992 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.1007 0.9088 0.000 0.000 0.044 0.000 0.956 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.0713 0.8424 0.028 0.000 0.000 0.972 0.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0146 0.8464 0.000 0.000 0.996 0.000 0.004 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.8954 0.000 1.000 0.000 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0790 0.8399 0.968 0.000 0.000 0.032 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 6 0.3810 0.8750 0.000 0.428 0.000 0.000 0.000 0.572
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.5129 0.5715 0.032 0.000 0.000 0.568 0.036 0.364
#> CB925BF0-1249-4350-A175-9A4129C43B8D 5 0.6194 0.3288 0.032 0.000 0.000 0.136 0.440 0.392
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.0520 0.8516 0.000 0.000 0.008 0.984 0.000 0.008
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0146 0.8464 0.000 0.000 0.996 0.000 0.004 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.1007 0.9088 0.000 0.000 0.044 0.000 0.956 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0146 0.8926 0.000 0.996 0.000 0.000 0.004 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 6 0.4123 0.8708 0.000 0.420 0.000 0.000 0.012 0.568
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0935 0.8402 0.964 0.000 0.000 0.032 0.000 0.004
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.3103 0.7613 0.000 0.000 0.784 0.208 0.000 0.008
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.8954 0.000 1.000 0.000 0.000 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.3103 0.7611 0.000 0.000 0.784 0.208 0.000 0.008
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0000 0.8473 0.000 0.000 1.000 0.000 0.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.1152 0.8180 0.952 0.000 0.000 0.004 0.000 0.044
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.2980 0.7714 0.000 0.000 0.800 0.192 0.000 0.008
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.4467 0.5881 0.592 0.000 0.000 0.004 0.028 0.376
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 6 0.3930 0.8782 0.004 0.420 0.000 0.000 0.000 0.576
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.5129 0.5715 0.032 0.000 0.000 0.568 0.036 0.364
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0000 0.8559 0.000 0.000 0.000 1.000 0.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.0000 0.8473 0.000 0.000 1.000 0.000 0.000 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.3073 0.7643 0.000 0.000 0.788 0.204 0.000 0.008
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.8954 0.000 1.000 0.000 0.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 6 0.3930 0.8782 0.004 0.420 0.000 0.000 0.000 0.576
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.2848 0.7631 0.000 0.000 0.856 0.004 0.036 0.104
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0146 0.8464 0.000 0.000 0.996 0.000 0.004 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.3373 0.5912 0.000 0.000 0.248 0.744 0.000 0.008
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0260 0.8578 0.008 0.000 0.000 0.992 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0260 0.8578 0.008 0.000 0.000 0.992 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.3686 0.7134 0.032 0.000 0.000 0.748 0.000 0.220
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.3925 0.6464 0.656 0.000 0.000 0.008 0.004 0.332
#> F779417A-9E29-4B27-BEA3-B23273A66021 6 0.3847 0.8345 0.000 0.456 0.000 0.000 0.000 0.544
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 6 0.3804 0.8784 0.000 0.424 0.000 0.000 0.000 0.576
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0508 0.8807 0.000 0.984 0.000 0.000 0.004 0.012
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.3706 -0.3993 0.000 0.620 0.000 0.000 0.000 0.380
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0935 0.8402 0.964 0.000 0.000 0.032 0.000 0.004
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0405 0.8843 0.000 0.988 0.000 0.000 0.008 0.004
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0790 0.8399 0.968 0.000 0.000 0.032 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0000 0.8559 0.000 0.000 0.000 1.000 0.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.5129 0.5715 0.032 0.000 0.000 0.568 0.036 0.364
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.3133 0.4976 0.000 0.780 0.000 0.000 0.008 0.212
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.5129 0.5715 0.032 0.000 0.000 0.568 0.036 0.364
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0790 0.8401 0.032 0.000 0.000 0.968 0.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0935 0.8402 0.964 0.000 0.000 0.032 0.000 0.004
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.8954 0.000 1.000 0.000 0.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 6 0.4982 0.7510 0.084 0.340 0.000 0.000 0.000 0.576
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0622 0.8779 0.000 0.980 0.000 0.000 0.008 0.012
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0790 0.8399 0.968 0.000 0.000 0.032 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0260 0.8578 0.008 0.000 0.000 0.992 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 6 0.3930 0.8782 0.004 0.420 0.000 0.000 0.000 0.576
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.8954 0.000 1.000 0.000 0.000 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.3615 0.6446 0.000 0.000 0.700 0.292 0.000 0.008
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.4649 0.5592 0.560 0.000 0.000 0.004 0.036 0.400
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.8954 0.000 1.000 0.000 0.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.5001 0.5300 0.532 0.000 0.000 0.016 0.040 0.412
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.1007 0.9088 0.000 0.000 0.044 0.000 0.956 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 6 0.5406 -0.0358 0.048 0.384 0.000 0.000 0.036 0.532
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0520 0.8542 0.008 0.000 0.000 0.984 0.000 0.008
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.1152 0.8180 0.952 0.000 0.000 0.004 0.000 0.044
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.8954 0.000 1.000 0.000 0.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.8954 0.000 1.000 0.000 0.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.3564 0.7047 0.004 0.000 0.796 0.004 0.036 0.160
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.1863 0.7770 0.896 0.000 0.000 0.000 0.000 0.104
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0790 0.8399 0.968 0.000 0.000 0.032 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.3717 -0.4139 0.000 0.616 0.000 0.000 0.000 0.384
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.5129 0.5715 0.032 0.000 0.000 0.568 0.036 0.364
#> B3561356-5A80-4C79-B23A-D518425565FE 6 0.3930 0.8782 0.004 0.420 0.000 0.000 0.000 0.576
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.4649 0.5592 0.560 0.000 0.000 0.004 0.036 0.400
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.4758 0.5509 0.552 0.000 0.000 0.008 0.036 0.404
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0260 0.8578 0.008 0.000 0.000 0.992 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0000 0.8473 0.000 0.000 1.000 0.000 0.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.8954 0.000 1.000 0.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0935 0.8402 0.964 0.000 0.000 0.032 0.000 0.004
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0260 0.8578 0.008 0.000 0.000 0.992 0.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0260 0.8578 0.008 0.000 0.000 0.992 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 6 0.4093 0.7550 0.000 0.476 0.000 0.000 0.008 0.516
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.2884 0.7398 0.008 0.000 0.004 0.000 0.824 0.164
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.2933 0.7440 0.796 0.000 0.000 0.004 0.000 0.200
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.2135 0.7559 0.872 0.000 0.000 0.000 0.000 0.128
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0260 0.8578 0.008 0.000 0.000 0.992 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.8954 0.000 1.000 0.000 0.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.3133 0.7574 0.000 0.000 0.780 0.212 0.000 0.008
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.1007 0.9088 0.000 0.000 0.044 0.000 0.956 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["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 17548 rows and 122 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 0.387 0.843 0.901 0.4682 0.512 0.512
#> 3 3 0.587 0.650 0.798 0.3902 0.751 0.551
#> 4 4 0.606 0.513 0.739 0.1197 0.774 0.461
#> 5 5 0.750 0.758 0.871 0.0612 0.862 0.559
#> 6 6 0.841 0.785 0.889 0.0550 0.949 0.777
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.9209 0.698 0.664 0.336
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.5408 0.811 0.124 0.876
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.851 1.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.1184 0.851 0.984 0.016
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 2 0.9909 0.144 0.444 0.556
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.930 0.000 1.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 2 0.4690 0.848 0.100 0.900
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.8443 0.773 0.728 0.272
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.9209 0.698 0.664 0.336
#> 806616FE-1855-4284-9265-42842104CB21 1 0.5946 0.870 0.856 0.144
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.930 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.930 0.000 1.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.9209 0.698 0.664 0.336
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.930 0.000 1.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 1 0.6148 0.866 0.848 0.152
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 2 0.7219 0.734 0.200 0.800
#> 853120F0-857B-4108-9EC8-727189630C5F 1 0.5946 0.870 0.856 0.144
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.5946 0.870 0.856 0.144
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.930 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.930 0.000 1.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.5946 0.870 0.856 0.144
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.930 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 1 0.5946 0.870 0.856 0.144
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.851 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.930 0.000 1.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.4298 0.866 0.912 0.088
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.851 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.851 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.6343 0.863 0.840 0.160
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.8144 0.588 0.252 0.748
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.930 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.9209 0.698 0.664 0.336
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.851 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.5946 0.870 0.856 0.144
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.5946 0.870 0.856 0.144
#> AA403EC3-FD44-4247-B06D-AEF415391E46 1 0.5946 0.870 0.856 0.144
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.5946 0.870 0.856 0.144
#> 50D620F3-5C52-42FB-89A1-6840A7444647 2 0.8608 0.613 0.284 0.716
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.930 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.6801 0.851 0.820 0.180
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.930 0.000 1.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.851 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.851 1.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.5946 0.870 0.856 0.144
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.7056 0.743 0.192 0.808
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.8267 0.648 0.260 0.740
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.930 0.000 1.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0672 0.927 0.008 0.992
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.7883 0.807 0.764 0.236
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.5946 0.870 0.856 0.144
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.930 0.000 1.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.5946 0.870 0.856 0.144
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 2 0.8763 0.588 0.296 0.704
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.9209 0.698 0.664 0.336
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.5946 0.870 0.856 0.144
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.7056 0.733 0.808 0.192
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0672 0.927 0.008 0.992
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.851 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.851 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.0000 0.930 0.000 1.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.5946 0.870 0.856 0.144
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.930 0.000 1.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0672 0.927 0.008 0.992
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.0000 0.851 1.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.6801 0.758 0.180 0.820
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.5946 0.870 0.856 0.144
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.5946 0.870 0.856 0.144
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.5946 0.870 0.856 0.144
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0000 0.851 1.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.851 1.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.930 0.000 1.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.930 0.000 1.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.930 0.000 1.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.930 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.9209 0.698 0.664 0.336
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.930 0.000 1.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.5946 0.870 0.856 0.144
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.851 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.851 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.4690 0.841 0.100 0.900
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.851 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.851 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.9209 0.698 0.664 0.336
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.930 0.000 1.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0938 0.924 0.012 0.988
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0000 0.930 0.000 1.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.6801 0.851 0.820 0.180
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.5946 0.870 0.856 0.144
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0672 0.927 0.008 0.992
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.930 0.000 1.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.5946 0.870 0.856 0.144
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.851 1.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.930 0.000 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.1184 0.851 0.984 0.016
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.0672 0.849 0.992 0.008
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.5737 0.795 0.136 0.864
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.5946 0.870 0.856 0.144
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.9209 0.698 0.664 0.336
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.930 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.930 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0000 0.851 1.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.9209 0.698 0.664 0.336
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.9209 0.698 0.664 0.336
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.930 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.851 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0672 0.927 0.008 0.992
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.1184 0.851 0.984 0.016
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.1184 0.851 0.984 0.016
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.5946 0.870 0.856 0.144
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.7056 0.743 0.192 0.808
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.930 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.9209 0.698 0.664 0.336
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.851 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.851 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0672 0.927 0.008 0.992
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0000 0.851 1.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.8661 0.756 0.712 0.288
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.9209 0.698 0.664 0.336
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.5946 0.870 0.856 0.144
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.930 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.5946 0.870 0.856 0.144
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.0000 0.930 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.6168 0.8721 0.588 0.000 0.412
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.4235 0.5979 0.000 0.824 0.176
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.6168 0.6773 0.412 0.000 0.588
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.6168 0.8721 0.588 0.000 0.412
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.7158 0.6700 0.372 0.032 0.596
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.6154 0.5459 0.408 0.592 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 2 0.6008 0.4279 0.372 0.628 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.6168 0.8721 0.588 0.000 0.412
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.6168 0.8721 0.588 0.000 0.412
#> 806616FE-1855-4284-9265-42842104CB21 3 0.6168 0.6773 0.412 0.000 0.588
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.7607 0.000 1.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.6154 0.5459 0.408 0.592 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.6168 0.8721 0.588 0.000 0.412
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.7607 0.000 1.000 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.6008 0.6843 0.372 0.000 0.628
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.7363 0.6648 0.372 0.040 0.588
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.5465 0.6958 0.288 0.000 0.712
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 3 0.0000 0.6580 0.000 0.000 1.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.7607 0.000 1.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.7607 0.000 1.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 3 0.0000 0.6580 0.000 0.000 1.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.7607 0.000 1.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.0000 0.6580 0.000 0.000 1.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 3 0.1529 0.6646 0.040 0.000 0.960
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.6154 0.5459 0.408 0.592 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 3 0.0000 0.6580 0.000 0.000 1.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 3 0.3816 0.4177 0.148 0.000 0.852
#> 4496EE84-2C36-413B-A328-A5B598A6C387 3 0.0000 0.6580 0.000 0.000 1.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.9494 0.5011 0.404 0.184 0.412
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.9277 0.2020 0.496 0.328 0.176
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.7607 0.000 1.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.6168 0.8721 0.588 0.000 0.412
#> F5940915-4123-49B3-95EE-4A0412BE8C30 3 0.5760 0.6955 0.328 0.000 0.672
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.5397 0.6962 0.280 0.000 0.720
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 3 0.3752 0.4267 0.144 0.000 0.856
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.5465 0.6958 0.288 0.000 0.712
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 3 0.0892 0.6367 0.020 0.000 0.980
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.9687 -0.3349 0.412 0.372 0.216
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.7607 0.000 1.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.6168 0.8721 0.588 0.000 0.412
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.6140 0.5492 0.404 0.596 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 3 0.1529 0.6646 0.040 0.000 0.960
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.2356 0.6353 0.072 0.000 0.928
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.6008 0.6843 0.372 0.000 0.628
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.6548 0.4070 0.372 0.616 0.012
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.9638 0.4975 0.372 0.208 0.420
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.6154 0.5459 0.408 0.592 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.6154 0.5459 0.408 0.592 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.6168 0.8721 0.588 0.000 0.412
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.6008 0.6843 0.372 0.000 0.628
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.7607 0.000 1.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.6008 0.6843 0.372 0.000 0.628
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.6168 0.6773 0.412 0.000 0.588
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.6168 0.8721 0.588 0.000 0.412
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.6008 0.6843 0.372 0.000 0.628
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.6168 0.8721 0.588 0.000 0.412
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.6154 0.5459 0.408 0.592 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 3 0.1529 0.6646 0.040 0.000 0.960
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 3 0.0892 0.6628 0.020 0.000 0.980
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.7945 0.2121 0.064 0.548 0.388
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.6008 0.6843 0.372 0.000 0.628
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.7607 0.000 1.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.6154 0.5459 0.408 0.592 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.6168 0.6773 0.412 0.000 0.588
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.7263 0.3750 0.372 0.592 0.036
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.6008 0.6843 0.372 0.000 0.628
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 3 0.0000 0.6580 0.000 0.000 1.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 3 0.0000 0.6580 0.000 0.000 1.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.6168 0.6773 0.412 0.000 0.588
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.6225 0.8461 0.568 0.000 0.432
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.6140 0.5492 0.404 0.596 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.6154 0.5459 0.408 0.592 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.7607 0.000 1.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.7607 0.000 1.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.6168 0.8721 0.588 0.000 0.412
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.7607 0.000 1.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.6168 0.8721 0.588 0.000 0.412
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 3 0.1529 0.6646 0.040 0.000 0.960
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 3 0.1753 0.6590 0.048 0.000 0.952
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.9434 0.0607 0.408 0.416 0.176
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 3 0.3038 0.6787 0.104 0.000 0.896
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 3 0.1529 0.6646 0.040 0.000 0.960
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.6168 0.8721 0.588 0.000 0.412
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.7607 0.000 1.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.6154 0.5459 0.408 0.592 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.4750 0.6712 0.216 0.784 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.6168 0.8721 0.588 0.000 0.412
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 3 0.3816 0.4177 0.148 0.000 0.852
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.6154 0.5459 0.408 0.592 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.7607 0.000 1.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.6302 -0.6039 0.520 0.000 0.480
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.6154 0.8622 0.592 0.000 0.408
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.7607 0.000 1.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 3 0.5254 0.2294 0.264 0.000 0.736
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.3752 0.6801 0.144 0.000 0.856
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.9633 0.1797 0.444 0.340 0.216
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 3 0.6235 0.5923 0.436 0.000 0.564
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.6168 0.8721 0.588 0.000 0.412
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.7607 0.000 1.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.7607 0.000 1.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.6168 0.6773 0.412 0.000 0.588
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.6168 0.8721 0.588 0.000 0.412
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.6168 0.8721 0.588 0.000 0.412
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.7607 0.000 1.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 3 0.1529 0.6646 0.040 0.000 0.960
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.6154 0.5459 0.408 0.592 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.6026 0.8265 0.624 0.000 0.376
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.6305 0.6574 0.516 0.000 0.484
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 3 0.3619 0.4438 0.136 0.000 0.864
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.7551 0.6600 0.372 0.048 0.580
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.7607 0.000 1.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.6168 0.8721 0.588 0.000 0.412
#> 472B75A2-A8C0-4503-B212-CADB781802EB 3 0.0000 0.6580 0.000 0.000 1.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 3 0.0000 0.6580 0.000 0.000 1.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.6154 0.5459 0.408 0.592 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.2356 0.6353 0.072 0.000 0.928
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.6168 0.8721 0.588 0.000 0.412
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.6168 0.8721 0.588 0.000 0.412
#> FA716037-886B-4DD0-8016-686C2D24550A 3 0.0000 0.6580 0.000 0.000 1.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.7607 0.000 1.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.6008 0.6843 0.372 0.000 0.628
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.4062 0.6510 0.164 0.836 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.6910 1.000 0.000 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.3837 0.5441 0.224 0.776 0.000 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.4776 -0.0887 0.000 0.000 0.624 0.376
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.6910 1.000 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.6485 0.000 0.000 1.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.5560 0.6127 0.392 0.584 0.000 0.024
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.4543 0.4673 0.000 0.324 0.676 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.6910 1.000 0.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0000 0.6910 1.000 0.000 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0000 0.6485 0.000 0.000 1.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.7758 0.000 1.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.5548 0.6155 0.388 0.588 0.000 0.024
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.6910 1.000 0.000 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0817 0.7744 0.000 0.976 0.000 0.024
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.4722 0.5137 0.008 0.000 0.692 0.300
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0000 0.6485 0.000 0.000 1.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.7911 0.2235 0.304 0.000 0.348 0.348
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 3 0.7730 -0.3313 0.264 0.000 0.444 0.292
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.7758 0.000 1.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.7758 0.000 1.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.7878 -0.2023 0.384 0.000 0.324 0.292
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.7758 0.000 1.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 1 0.5649 0.3885 0.620 0.000 0.036 0.344
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.4543 0.7619 0.000 0.000 0.324 0.676
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.5560 0.6127 0.392 0.584 0.000 0.024
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.7878 -0.2023 0.384 0.000 0.324 0.292
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.7828 -0.1545 0.412 0.000 0.296 0.292
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.6867 0.6546 0.124 0.000 0.324 0.552
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.6578 0.4643 0.108 0.000 0.592 0.300
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.2944 0.5446 0.868 0.128 0.004 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.7758 0.000 1.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.6910 1.000 0.000 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.4543 0.7619 0.000 0.000 0.324 0.676
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.6400 0.1175 0.180 0.000 0.652 0.168
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.7845 -0.1672 0.404 0.000 0.304 0.292
#> AA403EC3-FD44-4247-B06D-AEF415391E46 1 0.7893 -0.1471 0.376 0.000 0.324 0.300
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.7045 0.0418 0.532 0.000 0.328 0.140
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.5867 0.5212 0.000 0.096 0.688 0.216
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.7758 0.000 1.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.6910 1.000 0.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.5420 0.6391 0.352 0.624 0.000 0.024
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.4543 0.7619 0.000 0.000 0.324 0.676
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.5668 0.7375 0.048 0.000 0.300 0.652
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.4193 0.1750 0.000 0.000 0.732 0.268
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.4454 0.4790 0.000 0.308 0.692 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.4957 0.5099 0.000 0.016 0.684 0.300
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.5560 0.6127 0.392 0.584 0.000 0.024
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.5560 0.6127 0.392 0.584 0.000 0.024
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0469 0.6857 0.988 0.000 0.000 0.012
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.0000 0.6485 0.000 0.000 1.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0817 0.7744 0.000 0.976 0.000 0.024
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0000 0.6485 0.000 0.000 1.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0000 0.6485 0.000 0.000 1.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.6910 1.000 0.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.0000 0.6485 0.000 0.000 1.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.6910 1.000 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.5560 0.6127 0.392 0.584 0.000 0.024
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.4543 0.7619 0.000 0.000 0.324 0.676
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.7142 0.6172 0.152 0.000 0.324 0.524
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.5452 0.2680 0.000 0.360 0.616 0.024
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0000 0.6485 0.000 0.000 1.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.7758 0.000 1.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.5560 0.6127 0.392 0.584 0.000 0.024
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.2704 0.5383 0.000 0.000 0.876 0.124
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.6080 0.5154 0.000 0.156 0.684 0.160
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.1302 0.6130 0.000 0.000 0.956 0.044
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.7830 -0.1707 0.404 0.000 0.324 0.272
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.7878 -0.2023 0.384 0.000 0.324 0.292
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.4790 -0.1376 0.000 0.000 0.620 0.380
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.3224 0.5831 0.864 0.000 0.016 0.120
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.5420 0.6391 0.352 0.624 0.000 0.024
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.5560 0.6127 0.392 0.584 0.000 0.024
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0817 0.7744 0.000 0.976 0.000 0.024
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0817 0.7744 0.000 0.976 0.000 0.024
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.6910 1.000 0.000 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.7758 0.000 1.000 0.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.6910 1.000 0.000 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.4543 0.7619 0.000 0.000 0.324 0.676
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.5271 0.7517 0.024 0.000 0.320 0.656
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.4713 -0.0361 0.640 0.360 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.4543 0.7619 0.000 0.000 0.324 0.676
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.4543 0.7619 0.000 0.000 0.324 0.676
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.6910 1.000 0.000 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.7758 0.000 1.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.5668 0.5327 0.444 0.532 0.000 0.024
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.3400 0.7283 0.180 0.820 0.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.6910 1.000 0.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.7805 -0.1390 0.420 0.000 0.300 0.280
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.5560 0.6127 0.392 0.584 0.000 0.024
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.7758 0.000 1.000 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.2450 0.6022 0.072 0.000 0.912 0.016
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.4907 0.2036 0.580 0.000 0.000 0.420
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.7758 0.000 1.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 4 0.7301 0.5481 0.236 0.000 0.228 0.536
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 4 0.5560 0.1507 0.156 0.000 0.116 0.728
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.2342 0.6122 0.912 0.080 0.000 0.008
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 3 0.7203 -0.0762 0.312 0.000 0.524 0.164
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.6910 1.000 0.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.7758 0.000 1.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.7758 0.000 1.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.2868 0.5220 0.000 0.000 0.864 0.136
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0188 0.6894 0.996 0.000 0.000 0.004
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.6910 1.000 0.000 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0817 0.7744 0.000 0.976 0.000 0.024
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.4543 0.7619 0.000 0.000 0.324 0.676
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.5560 0.6127 0.392 0.584 0.000 0.024
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.4955 0.1436 0.556 0.000 0.000 0.444
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 4 0.6155 0.1494 0.412 0.000 0.052 0.536
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.7867 -0.1878 0.392 0.000 0.316 0.292
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0000 0.6485 0.000 0.000 1.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.7758 0.000 1.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.6910 1.000 0.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.7878 -0.2023 0.384 0.000 0.324 0.292
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.7529 0.5527 0.204 0.000 0.324 0.472
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.5560 0.6127 0.392 0.584 0.000 0.024
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 4 0.1722 0.4073 0.048 0.000 0.008 0.944
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.6910 1.000 0.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.0592 0.6835 0.984 0.000 0.000 0.016
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.7878 -0.2023 0.384 0.000 0.324 0.292
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.7758 0.000 1.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.0000 0.6485 0.000 0.000 1.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.7870 -0.0587 0.000 0.392 0.308 0.300
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.85557 1.000 0.000 0.000 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.2548 0.74457 0.036 0.912 0.008 0.024 0.020
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.4343 0.71323 0.000 0.000 0.768 0.136 0.096
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.85557 1.000 0.000 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0963 0.89563 0.000 0.000 0.964 0.036 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.5667 0.61401 0.332 0.596 0.028 0.000 0.044
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0794 0.87048 0.000 0.028 0.972 0.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.85557 1.000 0.000 0.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0000 0.85557 1.000 0.000 0.000 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0963 0.89563 0.000 0.000 0.964 0.036 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0290 0.79451 0.000 0.992 0.008 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.5667 0.61401 0.332 0.596 0.028 0.000 0.044
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.85557 1.000 0.000 0.000 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.1907 0.78844 0.000 0.928 0.028 0.000 0.044
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.2516 0.88666 0.000 0.000 0.140 0.000 0.860
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0963 0.89563 0.000 0.000 0.964 0.036 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.2761 0.89447 0.024 0.000 0.104 0.000 0.872
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.2514 0.84587 0.060 0.000 0.044 0.896 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0290 0.79451 0.000 0.992 0.008 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0290 0.79451 0.000 0.992 0.008 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.2074 0.85466 0.104 0.000 0.000 0.896 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0290 0.79451 0.000 0.992 0.008 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.2653 0.84105 0.024 0.000 0.000 0.096 0.880
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.2124 0.83354 0.004 0.000 0.000 0.900 0.096
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.5667 0.61401 0.332 0.596 0.028 0.000 0.044
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.2074 0.85466 0.104 0.000 0.000 0.896 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.2230 0.84962 0.116 0.000 0.000 0.884 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.1121 0.85689 0.044 0.000 0.000 0.956 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.2516 0.88666 0.000 0.000 0.140 0.000 0.860
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.4464 0.00619 0.584 0.408 0.008 0.000 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0290 0.79451 0.000 0.992 0.008 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.85557 1.000 0.000 0.000 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.1965 0.83346 0.000 0.000 0.000 0.904 0.096
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.4735 0.61800 0.272 0.000 0.048 0.680 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.2179 0.85148 0.112 0.000 0.000 0.888 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.3012 0.89038 0.036 0.000 0.104 0.000 0.860
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.4307 -0.25357 0.500 0.000 0.000 0.500 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.1012 0.87129 0.000 0.012 0.968 0.000 0.020
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0290 0.79451 0.000 0.992 0.008 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.85557 1.000 0.000 0.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.5416 0.66271 0.276 0.652 0.028 0.000 0.044
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.2124 0.83354 0.004 0.000 0.000 0.900 0.096
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.2720 0.82527 0.020 0.000 0.004 0.880 0.096
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.3074 0.73034 0.000 0.000 0.196 0.804 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0794 0.87048 0.000 0.028 0.972 0.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.2516 0.88666 0.000 0.000 0.140 0.000 0.860
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.4998 0.56600 0.372 0.596 0.024 0.000 0.008
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.5667 0.61401 0.332 0.596 0.028 0.000 0.044
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0162 0.85274 0.996 0.000 0.000 0.004 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.1043 0.89487 0.000 0.000 0.960 0.040 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.1907 0.78844 0.000 0.928 0.028 0.000 0.044
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.1043 0.89496 0.000 0.000 0.960 0.040 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0963 0.89563 0.000 0.000 0.964 0.036 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.85557 1.000 0.000 0.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.1043 0.89496 0.000 0.000 0.960 0.040 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.85557 1.000 0.000 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.5667 0.61401 0.332 0.596 0.028 0.000 0.044
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.2124 0.83354 0.004 0.000 0.000 0.900 0.096
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.1197 0.85757 0.048 0.000 0.000 0.952 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.3395 0.55010 0.000 0.236 0.764 0.000 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.1043 0.89496 0.000 0.000 0.960 0.040 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.79433 0.000 1.000 0.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.5667 0.61401 0.332 0.596 0.028 0.000 0.044
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.2130 0.86180 0.000 0.000 0.908 0.080 0.012
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0671 0.87260 0.000 0.016 0.980 0.000 0.004
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.4074 0.45423 0.000 0.000 0.636 0.364 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.2329 0.84487 0.124 0.000 0.000 0.876 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.2074 0.85466 0.104 0.000 0.000 0.896 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.3203 0.76000 0.000 0.000 0.168 0.820 0.012
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.3395 0.52698 0.764 0.000 0.000 0.236 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.5416 0.66271 0.276 0.652 0.028 0.000 0.044
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.5667 0.61401 0.332 0.596 0.028 0.000 0.044
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.1907 0.78844 0.000 0.928 0.028 0.000 0.044
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.1907 0.78844 0.000 0.928 0.028 0.000 0.044
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.85557 1.000 0.000 0.000 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0290 0.79451 0.000 0.992 0.008 0.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.85557 1.000 0.000 0.000 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0000 0.84548 0.000 0.000 0.000 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.2124 0.83354 0.004 0.000 0.000 0.900 0.096
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.3783 0.49873 0.740 0.252 0.008 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.2124 0.83354 0.004 0.000 0.000 0.900 0.096
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.1792 0.83673 0.000 0.000 0.000 0.916 0.084
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.85557 1.000 0.000 0.000 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0290 0.79451 0.000 0.992 0.008 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 1 0.5890 -0.25462 0.496 0.432 0.028 0.000 0.044
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.2563 0.77014 0.120 0.872 0.008 0.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.85557 1.000 0.000 0.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.3074 0.78025 0.196 0.000 0.000 0.804 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.5667 0.61401 0.332 0.596 0.028 0.000 0.044
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0290 0.79451 0.000 0.992 0.008 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.5355 0.48033 0.292 0.000 0.624 0.084 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 4 0.5568 0.46227 0.308 0.000 0.000 0.596 0.096
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0290 0.79451 0.000 0.992 0.008 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 4 0.4273 0.76101 0.116 0.000 0.004 0.784 0.096
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.1372 0.85994 0.024 0.000 0.016 0.004 0.956
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.3364 0.70804 0.848 0.112 0.020 0.000 0.020
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.4552 -0.08720 0.524 0.000 0.008 0.468 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.85557 1.000 0.000 0.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0290 0.79451 0.000 0.992 0.008 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0290 0.79451 0.000 0.992 0.008 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.2130 0.86180 0.000 0.000 0.908 0.080 0.012
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0162 0.85261 0.996 0.004 0.000 0.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.85557 1.000 0.000 0.000 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.1725 0.78919 0.000 0.936 0.020 0.000 0.044
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.2124 0.83354 0.004 0.000 0.000 0.900 0.096
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.5667 0.61401 0.332 0.596 0.028 0.000 0.044
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.5568 0.38452 0.596 0.000 0.000 0.308 0.096
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 4 0.4343 0.74545 0.136 0.000 0.000 0.768 0.096
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.2074 0.85466 0.104 0.000 0.000 0.896 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0963 0.89563 0.000 0.000 0.964 0.036 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0290 0.79451 0.000 0.992 0.008 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.85557 1.000 0.000 0.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.2074 0.85466 0.104 0.000 0.000 0.896 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.1851 0.85754 0.088 0.000 0.000 0.912 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.5667 0.61401 0.332 0.596 0.028 0.000 0.044
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.2719 0.76472 0.004 0.000 0.000 0.144 0.852
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.85557 1.000 0.000 0.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.0451 0.84737 0.988 0.000 0.008 0.000 0.004
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.2074 0.85466 0.104 0.000 0.000 0.896 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0290 0.79451 0.000 0.992 0.008 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.1197 0.89008 0.000 0.000 0.952 0.048 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.2951 0.83007 0.000 0.112 0.028 0.000 0.860
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.8992 1.000 0.000 0.000 0.000 0.0 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.3956 0.5276 0.036 0.712 0.000 0.000 0.0 0.252
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.2948 0.7215 0.000 0.000 0.804 0.188 0.0 0.008
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.8992 1.000 0.000 0.000 0.000 0.0 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.9053 0.000 0.000 1.000 0.000 0.0 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.8445 0.000 1.000 0.000 0.000 0.0 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0000 0.9053 0.000 0.000 1.000 0.000 0.0 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0363 0.8909 0.988 0.000 0.000 0.012 0.0 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0000 0.8992 1.000 0.000 0.000 0.000 0.0 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0000 0.9053 0.000 0.000 1.000 0.000 0.0 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.3789 0.1392 0.000 0.584 0.000 0.000 0.0 0.416
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.8445 0.000 1.000 0.000 0.000 0.0 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.8992 1.000 0.000 0.000 0.000 0.0 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0458 0.8353 0.000 0.984 0.000 0.000 0.0 0.016
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.0000 0.9750 0.000 0.000 0.000 0.000 1.0 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0000 0.9053 0.000 0.000 1.000 0.000 0.0 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0000 0.9750 0.000 0.000 0.000 0.000 1.0 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.3023 0.8325 0.004 0.000 0.008 0.808 0.0 0.180
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 6 0.2697 1.0000 0.000 0.188 0.000 0.000 0.0 0.812
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 6 0.2697 1.0000 0.000 0.188 0.000 0.000 0.0 0.812
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.2980 0.8353 0.012 0.000 0.000 0.808 0.0 0.180
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 6 0.2697 1.0000 0.000 0.188 0.000 0.000 0.0 0.812
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.0000 0.9750 0.000 0.000 0.000 0.000 1.0 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.1124 0.7782 0.036 0.000 0.000 0.956 0.0 0.008
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.8445 0.000 1.000 0.000 0.000 0.0 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.2980 0.8353 0.012 0.000 0.000 0.808 0.0 0.180
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.2980 0.8353 0.012 0.000 0.000 0.808 0.0 0.180
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.2882 0.8350 0.008 0.000 0.000 0.812 0.0 0.180
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.0000 0.9750 0.000 0.000 0.000 0.000 1.0 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.3857 0.1488 0.468 0.532 0.000 0.000 0.0 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 6 0.2697 1.0000 0.000 0.188 0.000 0.000 0.0 0.812
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.8992 1.000 0.000 0.000 0.000 0.0 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.0622 0.7855 0.012 0.000 0.000 0.980 0.0 0.008
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.5255 0.6946 0.164 0.000 0.012 0.644 0.0 0.180
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.2980 0.8353 0.012 0.000 0.000 0.808 0.0 0.180
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0000 0.9750 0.000 0.000 0.000 0.000 1.0 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.3244 0.5535 0.732 0.000 0.000 0.268 0.0 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0000 0.9053 0.000 0.000 1.000 0.000 0.0 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 6 0.2697 1.0000 0.000 0.188 0.000 0.000 0.0 0.812
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.8992 1.000 0.000 0.000 0.000 0.0 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.8445 0.000 1.000 0.000 0.000 0.0 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.0891 0.7824 0.024 0.000 0.000 0.968 0.0 0.008
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.1866 0.7431 0.084 0.000 0.000 0.908 0.0 0.008
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.4044 0.7892 0.000 0.000 0.076 0.744 0.0 0.180
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0000 0.9053 0.000 0.000 1.000 0.000 0.0 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0000 0.9750 0.000 0.000 0.000 0.000 1.0 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.3266 0.5567 0.272 0.728 0.000 0.000 0.0 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0000 0.8445 0.000 1.000 0.000 0.000 0.0 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0865 0.8706 0.964 0.000 0.000 0.036 0.0 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.0000 0.9053 0.000 0.000 1.000 0.000 0.0 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0260 0.8402 0.000 0.992 0.000 0.000 0.0 0.008
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0000 0.9053 0.000 0.000 1.000 0.000 0.0 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0000 0.9053 0.000 0.000 1.000 0.000 0.0 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.8992 1.000 0.000 0.000 0.000 0.0 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.0000 0.9053 0.000 0.000 1.000 0.000 0.0 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0146 0.8968 0.996 0.000 0.000 0.004 0.0 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.8445 0.000 1.000 0.000 0.000 0.0 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.1124 0.7782 0.036 0.000 0.000 0.956 0.0 0.008
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.2631 0.8329 0.000 0.000 0.000 0.820 0.0 0.180
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.2762 0.6617 0.000 0.196 0.804 0.000 0.0 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0000 0.9053 0.000 0.000 1.000 0.000 0.0 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.3706 0.2575 0.000 0.620 0.000 0.000 0.0 0.380
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.8445 0.000 1.000 0.000 0.000 0.0 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0520 0.8957 0.000 0.000 0.984 0.008 0.0 0.008
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0000 0.9053 0.000 0.000 1.000 0.000 0.0 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.5748 -0.0355 0.000 0.000 0.464 0.360 0.0 0.176
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.3312 0.8298 0.028 0.000 0.000 0.792 0.0 0.180
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.2980 0.8353 0.012 0.000 0.000 0.808 0.0 0.180
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.3349 0.6634 0.000 0.000 0.244 0.748 0.0 0.008
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.8992 1.000 0.000 0.000 0.000 0.0 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.8445 0.000 1.000 0.000 0.000 0.0 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.8445 0.000 1.000 0.000 0.000 0.0 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.8445 0.000 1.000 0.000 0.000 0.0 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0146 0.8426 0.000 0.996 0.000 0.000 0.0 0.004
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.8992 1.000 0.000 0.000 0.000 0.0 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 6 0.2697 1.0000 0.000 0.188 0.000 0.000 0.0 0.812
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.8992 1.000 0.000 0.000 0.000 0.0 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.2631 0.8329 0.000 0.000 0.000 0.820 0.0 0.180
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.1196 0.7760 0.040 0.000 0.000 0.952 0.0 0.008
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.3789 0.2291 0.584 0.416 0.000 0.000 0.0 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.1124 0.7782 0.036 0.000 0.000 0.956 0.0 0.008
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0000 0.7907 0.000 0.000 0.000 1.000 0.0 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.8992 1.000 0.000 0.000 0.000 0.0 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.3789 0.1392 0.000 0.584 0.000 0.000 0.0 0.416
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.1556 0.7615 0.080 0.920 0.000 0.000 0.0 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.4910 0.4388 0.116 0.640 0.000 0.000 0.0 0.244
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.8992 1.000 0.000 0.000 0.000 0.0 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.4094 0.7928 0.080 0.000 0.000 0.740 0.0 0.180
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.8445 0.000 1.000 0.000 0.000 0.0 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 6 0.2697 1.0000 0.000 0.188 0.000 0.000 0.0 0.812
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.6091 0.1182 0.400 0.000 0.460 0.088 0.0 0.052
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 4 0.4095 -0.0320 0.480 0.000 0.000 0.512 0.0 0.008
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 6 0.2697 1.0000 0.000 0.188 0.000 0.000 0.0 0.812
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 4 0.4010 0.1910 0.408 0.000 0.000 0.584 0.0 0.008
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.0000 0.9750 0.000 0.000 0.000 0.000 1.0 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.3843 0.1509 0.548 0.452 0.000 0.000 0.0 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.5726 0.0658 0.512 0.000 0.004 0.320 0.0 0.164
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.8992 1.000 0.000 0.000 0.000 0.0 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 6 0.2697 1.0000 0.000 0.188 0.000 0.000 0.0 0.812
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 6 0.2697 1.0000 0.000 0.188 0.000 0.000 0.0 0.812
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.0520 0.8957 0.000 0.000 0.984 0.008 0.0 0.008
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0865 0.8697 0.964 0.036 0.000 0.000 0.0 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.8992 1.000 0.000 0.000 0.000 0.0 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.1957 0.7385 0.000 0.888 0.000 0.000 0.0 0.112
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.1124 0.7782 0.036 0.000 0.000 0.956 0.0 0.008
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.8445 0.000 1.000 0.000 0.000 0.0 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.3874 0.4419 0.636 0.000 0.000 0.356 0.0 0.008
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 4 0.4062 0.0956 0.440 0.000 0.000 0.552 0.0 0.008
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.2980 0.8353 0.012 0.000 0.000 0.808 0.0 0.180
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0000 0.9053 0.000 0.000 1.000 0.000 0.0 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 6 0.2697 1.0000 0.000 0.188 0.000 0.000 0.0 0.812
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.8992 1.000 0.000 0.000 0.000 0.0 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.2980 0.8353 0.012 0.000 0.000 0.808 0.0 0.180
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.2882 0.8350 0.008 0.000 0.000 0.812 0.0 0.180
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0000 0.8445 0.000 1.000 0.000 0.000 0.0 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.2980 0.7907 0.000 0.000 0.000 0.192 0.8 0.008
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.8992 1.000 0.000 0.000 0.000 0.0 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.0790 0.8766 0.968 0.032 0.000 0.000 0.0 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.2980 0.8353 0.012 0.000 0.000 0.808 0.0 0.180
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 6 0.2697 1.0000 0.000 0.188 0.000 0.000 0.0 0.812
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.0363 0.8964 0.000 0.000 0.988 0.012 0.0 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0000 0.9750 0.000 0.000 0.000 0.000 1.0 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["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 17548 rows and 122 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 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 0.996 0.994 0.5000 0.497 0.497
#> 3 3 0.539 0.732 0.786 0.2455 0.875 0.748
#> 4 4 0.785 0.838 0.898 0.1677 0.822 0.556
#> 5 5 0.741 0.697 0.870 0.0557 0.955 0.835
#> 6 6 0.842 0.706 0.834 0.0596 0.936 0.746
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 2 0.1184 0.996 0.016 0.984
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.0938 0.994 0.012 0.988
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.999 1.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 2 0.1184 0.996 0.016 0.984
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 1 0.0000 0.999 1.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.1184 0.996 0.016 0.984
#> 9264567D-4524-46AF-A851-C091C3CD76CF 1 0.0000 0.999 1.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 2 0.1184 0.996 0.016 0.984
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 2 0.1184 0.996 0.016 0.984
#> 806616FE-1855-4284-9265-42842104CB21 1 0.0000 0.999 1.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.989 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.1184 0.996 0.016 0.984
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 2 0.1184 0.996 0.016 0.984
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0938 0.994 0.012 0.988
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 1 0.0376 0.997 0.996 0.004
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 1 0.0000 0.999 1.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 1 0.0376 0.997 0.996 0.004
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.999 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.989 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.989 0.000 1.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.999 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.989 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 1 0.0376 0.997 0.996 0.004
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.999 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.1184 0.996 0.016 0.984
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.999 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.999 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.999 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.0376 0.997 0.996 0.004
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.1184 0.996 0.016 0.984
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.989 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 2 0.1184 0.996 0.016 0.984
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.999 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.0000 0.999 1.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.999 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 1 0.0376 0.997 0.996 0.004
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0000 0.999 1.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.0000 0.999 1.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.989 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 2 0.1184 0.996 0.016 0.984
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.1184 0.996 0.016 0.984
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.999 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0376 0.997 0.996 0.004
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.999 1.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 1 0.0000 0.999 1.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 1 0.0376 0.997 0.996 0.004
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.1184 0.996 0.016 0.984
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.1184 0.996 0.016 0.984
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 2 0.1184 0.996 0.016 0.984
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.0000 0.999 1.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.989 0.000 1.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.0000 0.999 1.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.0000 0.999 1.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 2 0.1184 0.996 0.016 0.984
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.0000 0.999 1.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 2 0.1184 0.996 0.016 0.984
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.1184 0.996 0.016 0.984
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.999 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.999 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 1 0.0376 0.996 0.996 0.004
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.0000 0.999 1.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.989 0.000 1.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.1184 0.996 0.016 0.984
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.0000 0.999 1.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 1 0.0000 0.999 1.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.0000 0.999 1.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.999 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.999 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0000 0.999 1.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 2 0.1184 0.996 0.016 0.984
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.1184 0.996 0.016 0.984
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.1184 0.996 0.016 0.984
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0938 0.994 0.012 0.988
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.989 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 2 0.1184 0.996 0.016 0.984
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0938 0.994 0.012 0.988
#> A314C4E6-B245-4F10-A555-50B9B819040D 2 0.1184 0.996 0.016 0.984
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.999 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.999 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.1184 0.996 0.016 0.984
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.999 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.999 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 2 0.1184 0.996 0.016 0.984
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.989 0.000 1.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.1184 0.996 0.016 0.984
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.1184 0.996 0.016 0.984
#> 6F7DB73C-FE46-402C-9001-DC2005278069 2 0.1184 0.996 0.016 0.984
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.999 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.1184 0.996 0.016 0.984
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.989 0.000 1.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.0000 0.999 1.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 2 0.1184 0.996 0.016 0.984
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.989 0.000 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 2 0.1184 0.996 0.016 0.984
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.0376 0.997 0.996 0.004
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.1184 0.996 0.016 0.984
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.999 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 2 0.1184 0.996 0.016 0.984
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.989 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.989 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0000 0.999 1.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 2 0.1184 0.996 0.016 0.984
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 2 0.1184 0.996 0.016 0.984
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.989 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.999 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.1184 0.996 0.016 0.984
#> F900E9BE-2400-4451-9434-EE8BC513BA94 2 0.1184 0.996 0.016 0.984
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 2 0.1184 0.996 0.016 0.984
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.999 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 1 0.0000 0.999 1.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.989 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 2 0.1184 0.996 0.016 0.984
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.999 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.999 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.1184 0.996 0.016 0.984
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0376 0.997 0.996 0.004
#> 12F54761-4F68-4181-8421-88EA858902FC 2 0.1184 0.996 0.016 0.984
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.1184 0.996 0.016 0.984
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.999 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.989 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.0000 0.999 1.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 1 0.0376 0.997 0.996 0.004
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.6302 1.0000 0.520 0.480 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.5650 -0.2664 0.312 0.688 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.2261 0.8508 0.068 0.000 0.932
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.6302 1.0000 0.520 0.480 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.8441 0.000 0.000 1.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.1753 0.6470 0.048 0.952 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.1643 0.8318 0.044 0.000 0.956
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.6302 1.0000 0.520 0.480 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 2 0.6280 -0.8512 0.460 0.540 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0000 0.8441 0.000 0.000 1.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.3752 0.6911 0.144 0.856 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.1753 0.6470 0.048 0.952 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.6302 1.0000 0.520 0.480 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.4974 0.6626 0.236 0.764 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.1753 0.8303 0.048 0.000 0.952
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0592 0.8412 0.012 0.000 0.988
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.1753 0.8303 0.048 0.000 0.952
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 3 0.5926 0.8274 0.356 0.000 0.644
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.4346 0.6813 0.184 0.816 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.4346 0.6813 0.184 0.816 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 3 0.5926 0.8274 0.356 0.000 0.644
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.4346 0.6813 0.184 0.816 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.1753 0.8303 0.048 0.000 0.952
#> F5A814F6-E824-4DB2-8497-4B99E151D450 3 0.4235 0.8570 0.176 0.000 0.824
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.1753 0.6470 0.048 0.952 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 3 0.5926 0.8274 0.356 0.000 0.644
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 3 0.5926 0.8274 0.356 0.000 0.644
#> 4496EE84-2C36-413B-A328-A5B598A6C387 3 0.5926 0.8274 0.356 0.000 0.644
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.1753 0.8303 0.048 0.000 0.952
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.5529 -0.1764 0.296 0.704 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.4346 0.6813 0.184 0.816 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.6302 1.0000 0.520 0.480 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 3 0.4235 0.8570 0.176 0.000 0.824
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.5678 0.8301 0.316 0.000 0.684
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 3 0.5926 0.8274 0.356 0.000 0.644
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.1753 0.8303 0.048 0.000 0.952
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 3 0.4235 0.8570 0.176 0.000 0.824
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.1643 0.8318 0.044 0.000 0.956
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.4346 0.6813 0.184 0.816 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.6302 1.0000 0.520 0.480 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.1031 0.6640 0.024 0.976 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 3 0.4235 0.8570 0.176 0.000 0.824
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.3267 0.8433 0.116 0.000 0.884
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.5859 0.8290 0.344 0.000 0.656
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.1643 0.8318 0.044 0.000 0.956
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.1753 0.8303 0.048 0.000 0.952
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.2356 0.6291 0.072 0.928 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.2356 0.6415 0.072 0.928 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.6302 1.0000 0.520 0.480 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.3116 0.8533 0.108 0.000 0.892
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.3551 0.6935 0.132 0.868 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.3116 0.8533 0.108 0.000 0.892
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.1289 0.8356 0.032 0.000 0.968
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.6302 1.0000 0.520 0.480 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.5363 0.8337 0.276 0.000 0.724
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.6302 1.0000 0.520 0.480 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.1964 0.6383 0.056 0.944 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 3 0.4235 0.8570 0.176 0.000 0.824
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 3 0.5926 0.8274 0.356 0.000 0.644
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.1711 0.8347 0.032 0.008 0.960
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.3116 0.8533 0.108 0.000 0.892
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.4346 0.6813 0.184 0.816 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.2066 0.6335 0.060 0.940 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0000 0.8441 0.000 0.000 1.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.1643 0.8318 0.044 0.000 0.956
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.5835 0.8293 0.340 0.000 0.660
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 3 0.5926 0.8274 0.356 0.000 0.644
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 3 0.5926 0.8274 0.356 0.000 0.644
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.4178 0.8571 0.172 0.000 0.828
#> 352471DC-A881-4EA8-B646-EB1200291893 2 0.6291 -0.8725 0.468 0.532 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.1411 0.6565 0.036 0.964 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.1753 0.6470 0.048 0.952 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.1289 0.6683 0.032 0.968 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.1860 0.6877 0.052 0.948 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.6302 1.0000 0.520 0.480 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.4974 0.6626 0.236 0.764 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.6302 1.0000 0.520 0.480 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 3 0.5926 0.8274 0.356 0.000 0.644
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 3 0.4235 0.8570 0.176 0.000 0.824
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.5706 -0.2794 0.320 0.680 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 3 0.4235 0.8570 0.176 0.000 0.824
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 3 0.5926 0.8274 0.356 0.000 0.644
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.6302 1.0000 0.520 0.480 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.4346 0.6813 0.184 0.816 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.2066 0.6335 0.060 0.940 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.4750 0.1870 0.216 0.784 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.6302 1.0000 0.520 0.480 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 3 0.5926 0.8274 0.356 0.000 0.644
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.2066 0.6335 0.060 0.940 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.1411 0.6697 0.036 0.964 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.5882 0.8286 0.348 0.000 0.652
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.6302 1.0000 0.520 0.480 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.4346 0.6813 0.184 0.816 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 2 0.7752 -0.8572 0.456 0.496 0.048
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.1753 0.8303 0.048 0.000 0.952
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.5397 -0.1062 0.280 0.720 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 3 0.5926 0.8274 0.356 0.000 0.644
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.6302 1.0000 0.520 0.480 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.4346 0.6813 0.184 0.816 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.3879 0.6895 0.152 0.848 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.2796 0.8467 0.092 0.000 0.908
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.6302 1.0000 0.520 0.480 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.6302 1.0000 0.520 0.480 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.3038 0.6935 0.104 0.896 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 3 0.4235 0.8570 0.176 0.000 0.824
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.2448 0.6076 0.076 0.924 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.6302 1.0000 0.520 0.480 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.6302 1.0000 0.520 0.480 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 3 0.5926 0.8274 0.356 0.000 0.644
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.1289 0.8356 0.032 0.000 0.968
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.4346 0.6813 0.184 0.816 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.6302 1.0000 0.520 0.480 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 3 0.5926 0.8274 0.356 0.000 0.644
#> F205F9FC-F2D5-4164-9A40-1279647F900B 3 0.5926 0.8274 0.356 0.000 0.644
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.5098 0.0695 0.248 0.752 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.3267 0.8433 0.116 0.000 0.884
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.6302 1.0000 0.520 0.480 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.6302 1.0000 0.520 0.480 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 3 0.5926 0.8274 0.356 0.000 0.644
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.4346 0.6813 0.184 0.816 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.3116 0.8533 0.108 0.000 0.892
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.1753 0.8303 0.048 0.000 0.952
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.4996 0.706 0.192 0.752 0.056 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.4985 0.575 0.000 0.000 0.532 0.468
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 4 0.4804 -0.103 0.000 0.000 0.384 0.616
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.4655 0.805 0.208 0.760 0.032 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.4925 0.649 0.000 0.000 0.572 0.428
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.2469 0.864 0.892 0.108 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 4 0.4356 0.319 0.000 0.000 0.292 0.708
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0376 0.876 0.004 0.992 0.004 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.4655 0.805 0.208 0.760 0.032 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 1 0.5784 0.372 0.556 0.412 0.032 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.1716 0.743 0.000 0.000 0.936 0.064
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.4955 0.624 0.000 0.000 0.556 0.444
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.1637 0.742 0.000 0.000 0.940 0.060
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.965 0.000 0.000 0.000 1.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0188 0.875 0.000 0.996 0.004 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0188 0.875 0.000 0.996 0.004 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.965 0.000 0.000 0.000 1.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0188 0.875 0.000 0.996 0.004 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.1716 0.743 0.000 0.000 0.936 0.064
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.0336 0.962 0.000 0.000 0.008 0.992
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.4655 0.805 0.208 0.760 0.032 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.965 0.000 0.000 0.000 1.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.965 0.000 0.000 0.000 1.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.965 0.000 0.000 0.000 1.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.1637 0.742 0.000 0.000 0.940 0.060
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.4507 0.790 0.788 0.168 0.044 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0188 0.875 0.000 0.996 0.004 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.0336 0.962 0.000 0.000 0.008 0.992
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.0000 0.965 0.000 0.000 0.000 1.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.965 0.000 0.000 0.000 1.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.1637 0.742 0.000 0.000 0.940 0.060
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.3268 0.822 0.024 0.056 0.028 0.892
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.4898 0.657 0.000 0.000 0.584 0.416
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0188 0.875 0.000 0.996 0.004 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.4655 0.805 0.208 0.760 0.032 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.0336 0.962 0.000 0.000 0.008 0.992
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.1557 0.737 0.000 0.000 0.944 0.056
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.0000 0.965 0.000 0.000 0.000 1.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.4925 0.649 0.000 0.000 0.572 0.428
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.2469 0.739 0.000 0.000 0.892 0.108
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.4289 0.793 0.796 0.172 0.032 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 1 0.4423 0.787 0.788 0.176 0.036 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.0000 0.965 0.000 0.000 0.000 1.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0779 0.874 0.004 0.980 0.016 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.0336 0.962 0.000 0.000 0.008 0.992
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.4948 0.630 0.000 0.000 0.560 0.440
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.0188 0.964 0.000 0.000 0.004 0.996
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.4655 0.805 0.208 0.760 0.032 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.0336 0.962 0.000 0.000 0.008 0.992
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0000 0.965 0.000 0.000 0.000 1.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.5598 0.649 0.004 0.016 0.564 0.416
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.0188 0.964 0.000 0.000 0.004 0.996
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0657 0.872 0.012 0.984 0.004 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.4655 0.805 0.208 0.760 0.032 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.4994 0.546 0.000 0.000 0.520 0.480
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.4925 0.649 0.000 0.000 0.572 0.428
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.0000 0.965 0.000 0.000 0.000 1.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0000 0.965 0.000 0.000 0.000 1.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.965 0.000 0.000 0.000 1.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.0188 0.964 0.000 0.000 0.004 0.996
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.1867 0.883 0.928 0.072 0.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.4655 0.805 0.208 0.760 0.032 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.4655 0.805 0.208 0.760 0.032 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.1661 0.868 0.004 0.944 0.052 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0469 0.876 0.012 0.988 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 1 0.5530 0.555 0.632 0.336 0.032 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0000 0.965 0.000 0.000 0.000 1.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.0336 0.962 0.000 0.000 0.008 0.992
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.6090 0.505 0.384 0.564 0.052 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.0336 0.962 0.000 0.000 0.008 0.992
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0000 0.965 0.000 0.000 0.000 1.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0817 0.872 0.000 0.976 0.024 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 1 0.4728 0.656 0.752 0.216 0.032 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.5279 0.707 0.232 0.716 0.052 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.965 0.000 0.000 0.000 1.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.4655 0.805 0.208 0.760 0.032 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0524 0.875 0.004 0.988 0.008 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.0336 0.962 0.000 0.000 0.008 0.992
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0188 0.875 0.000 0.996 0.004 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.4706 0.798 0.788 0.072 0.140 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.1557 0.740 0.000 0.000 0.944 0.056
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.5072 0.796 0.208 0.740 0.052 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0000 0.965 0.000 0.000 0.000 1.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0188 0.875 0.000 0.996 0.004 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0376 0.876 0.004 0.992 0.004 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.4925 0.649 0.000 0.000 0.572 0.428
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0336 0.921 0.992 0.008 0.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0376 0.876 0.004 0.992 0.004 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.0336 0.962 0.000 0.000 0.008 0.992
#> B3561356-5A80-4C79-B23A-D518425565FE 1 0.3803 0.829 0.836 0.132 0.032 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.1807 0.894 0.940 0.052 0.008 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.965 0.000 0.000 0.000 1.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.4925 0.649 0.000 0.000 0.572 0.428
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0188 0.875 0.000 0.996 0.004 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0336 0.962 0.000 0.000 0.008 0.992
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0000 0.965 0.000 0.000 0.000 1.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.5144 0.790 0.216 0.732 0.052 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.1389 0.734 0.000 0.000 0.952 0.048
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.965 0.000 0.000 0.000 1.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0188 0.875 0.000 0.996 0.004 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.0188 0.964 0.000 0.000 0.004 0.996
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.1716 0.743 0.000 0.000 0.936 0.064
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.9104 1.000 0.000 0.000 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.4970 0.6748 0.140 0.712 0.000 0.000 0.148
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.3847 0.3039 0.000 0.000 0.784 0.180 0.036
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.9104 1.000 0.000 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.2648 0.3896 0.000 0.000 0.848 0.152 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.4163 0.7297 0.228 0.740 0.000 0.000 0.032
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0000 0.5828 0.000 0.000 1.000 0.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.9104 1.000 0.000 0.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0404 0.9026 0.988 0.012 0.000 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.3999 0.1328 0.000 0.000 0.656 0.344 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0510 0.8173 0.000 0.984 0.000 0.000 0.016
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.4163 0.7297 0.228 0.740 0.000 0.000 0.032
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.9104 1.000 0.000 0.000 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.4276 0.3525 0.380 0.616 0.000 0.000 0.004
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.4182 -0.1344 0.000 0.000 0.600 0.000 0.400
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0510 0.5714 0.000 0.000 0.984 0.016 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.4182 -0.1344 0.000 0.000 0.600 0.000 0.400
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.8927 0.000 0.000 0.000 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0162 0.8178 0.000 0.996 0.000 0.000 0.004
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0162 0.8178 0.000 0.996 0.000 0.000 0.004
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.8927 0.000 0.000 0.000 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0162 0.8178 0.000 0.996 0.000 0.000 0.004
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.4182 -0.1344 0.000 0.000 0.600 0.000 0.400
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.1661 0.8749 0.000 0.000 0.024 0.940 0.036
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.4163 0.7297 0.228 0.740 0.000 0.000 0.032
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.8927 0.000 0.000 0.000 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.8927 0.000 0.000 0.000 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.8927 0.000 0.000 0.000 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.4182 -0.1344 0.000 0.000 0.600 0.000 0.400
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.3460 0.8033 0.828 0.044 0.000 0.000 0.128
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.8178 0.000 1.000 0.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.9104 1.000 0.000 0.000 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.1493 0.8779 0.000 0.000 0.024 0.948 0.028
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.0290 0.8904 0.000 0.000 0.008 0.992 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.8927 0.000 0.000 0.000 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.4182 -0.1344 0.000 0.000 0.600 0.000 0.400
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.1605 0.8749 0.004 0.000 0.040 0.944 0.012
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0000 0.5828 0.000 0.000 1.000 0.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0162 0.8178 0.000 0.996 0.000 0.000 0.004
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.9104 1.000 0.000 0.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.3977 0.7473 0.204 0.764 0.000 0.000 0.032
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.1661 0.8749 0.000 0.000 0.024 0.940 0.036
#> CB925BF0-1249-4350-A175-9A4129C43B8D 5 0.5996 0.7141 0.000 0.000 0.316 0.136 0.548
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.3242 0.7254 0.000 0.000 0.216 0.784 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0000 0.5828 0.000 0.000 1.000 0.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.3857 0.0506 0.000 0.000 0.688 0.000 0.312
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.3543 0.7863 0.828 0.112 0.000 0.000 0.060
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 1 0.3727 0.6853 0.768 0.216 0.000 0.000 0.016
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.9104 1.000 0.000 0.000 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.4182 0.4917 0.000 0.000 0.400 0.600 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0609 0.8166 0.000 0.980 0.000 0.000 0.020
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.4210 0.4739 0.000 0.000 0.412 0.588 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0000 0.5828 0.000 0.000 1.000 0.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.9104 1.000 0.000 0.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.4161 0.5044 0.000 0.000 0.392 0.608 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.9104 1.000 0.000 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.4163 0.7297 0.228 0.740 0.000 0.000 0.032
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.1661 0.8749 0.000 0.000 0.024 0.940 0.036
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0000 0.8927 0.000 0.000 0.000 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.0162 0.5808 0.000 0.000 0.996 0.000 0.004
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.4201 0.4813 0.000 0.000 0.408 0.592 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0703 0.8153 0.024 0.976 0.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.3890 0.7114 0.252 0.736 0.000 0.000 0.012
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.1908 0.4932 0.000 0.000 0.908 0.092 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0000 0.5828 0.000 0.000 1.000 0.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.2813 0.7742 0.000 0.000 0.168 0.832 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0000 0.8927 0.000 0.000 0.000 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.8927 0.000 0.000 0.000 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.3596 0.7329 0.000 0.000 0.212 0.776 0.012
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.9104 1.000 0.000 0.000 0.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.4073 0.7391 0.216 0.752 0.000 0.000 0.032
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.4163 0.7297 0.228 0.740 0.000 0.000 0.032
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.2605 0.7431 0.000 0.852 0.000 0.000 0.148
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0510 0.8173 0.000 0.984 0.000 0.000 0.016
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.9104 1.000 0.000 0.000 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.4455 0.2807 0.404 0.588 0.000 0.000 0.008
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.9104 1.000 0.000 0.000 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0000 0.8927 0.000 0.000 0.000 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.1893 0.8686 0.000 0.000 0.024 0.928 0.048
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.6153 -0.0664 0.484 0.380 0.000 0.000 0.136
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.1661 0.8749 0.000 0.000 0.024 0.940 0.036
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0000 0.8927 0.000 0.000 0.000 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.9104 1.000 0.000 0.000 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0162 0.8182 0.000 0.996 0.000 0.000 0.004
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 1 0.2930 0.7538 0.832 0.164 0.000 0.000 0.004
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.5901 0.5943 0.268 0.584 0.000 0.000 0.148
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.9104 1.000 0.000 0.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.8927 0.000 0.000 0.000 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.4141 0.7228 0.236 0.736 0.000 0.000 0.028
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0880 0.8124 0.000 0.968 0.000 0.000 0.032
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.1522 0.8755 0.000 0.000 0.044 0.944 0.012
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.3684 0.7072 0.720 0.000 0.000 0.000 0.280
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0162 0.8178 0.000 0.996 0.000 0.000 0.004
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.4538 0.5847 0.620 0.000 0.000 0.016 0.364
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.4300 -0.4037 0.000 0.000 0.524 0.000 0.476
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.6080 0.4727 0.344 0.520 0.000 0.000 0.136
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0162 0.8917 0.000 0.000 0.004 0.996 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.9104 1.000 0.000 0.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0162 0.8178 0.000 0.996 0.000 0.000 0.004
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0404 0.8177 0.000 0.988 0.000 0.000 0.012
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.1582 0.5337 0.000 0.000 0.944 0.028 0.028
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0000 0.9104 1.000 0.000 0.000 0.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.9104 1.000 0.000 0.000 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0510 0.8173 0.000 0.984 0.000 0.000 0.016
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.1661 0.8749 0.000 0.000 0.024 0.940 0.036
#> B3561356-5A80-4C79-B23A-D518425565FE 1 0.3011 0.7763 0.844 0.140 0.000 0.000 0.016
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.3684 0.7072 0.720 0.000 0.000 0.000 0.280
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.3684 0.7072 0.720 0.000 0.000 0.000 0.280
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.8927 0.000 0.000 0.000 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0000 0.5828 0.000 0.000 1.000 0.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0162 0.8178 0.000 0.996 0.000 0.000 0.004
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.9104 1.000 0.000 0.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0000 0.8927 0.000 0.000 0.000 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0000 0.8927 0.000 0.000 0.000 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.5921 0.5680 0.296 0.568 0.000 0.000 0.136
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.5352 0.6674 0.000 0.000 0.408 0.056 0.536
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.9104 1.000 0.000 0.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.0000 0.9104 1.000 0.000 0.000 0.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.8927 0.000 0.000 0.000 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.8178 0.000 1.000 0.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.4210 0.4739 0.000 0.000 0.412 0.588 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.4182 -0.1344 0.000 0.000 0.600 0.000 0.400
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0865 0.81051 0.964 0.000 0.000 0.000 0.000 0.036
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.7378 -0.03185 0.012 0.356 0.080 0.000 0.220 0.332
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.1237 0.78843 0.000 0.000 0.956 0.020 0.004 0.020
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.1141 0.80747 0.948 0.000 0.000 0.000 0.000 0.052
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.1615 0.75979 0.000 0.000 0.928 0.064 0.004 0.004
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 5 0.0260 0.70201 0.000 0.008 0.000 0.000 0.992 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0405 0.80063 0.000 0.000 0.988 0.008 0.000 0.004
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0865 0.81319 0.964 0.000 0.000 0.000 0.000 0.036
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.3268 0.68047 0.824 0.000 0.000 0.000 0.100 0.076
#> 806616FE-1855-4284-9265-42842104CB21 3 0.1897 0.73652 0.000 0.000 0.908 0.084 0.004 0.004
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0547 0.84908 0.000 0.980 0.000 0.000 0.000 0.020
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 5 0.0260 0.70201 0.000 0.008 0.000 0.000 0.992 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0865 0.81051 0.964 0.000 0.000 0.000 0.000 0.036
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.7262 0.01569 0.008 0.384 0.080 0.000 0.212 0.316
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.3531 0.70752 0.000 0.000 0.672 0.000 0.000 0.328
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0777 0.79434 0.000 0.000 0.972 0.024 0.000 0.004
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.3531 0.70752 0.000 0.000 0.672 0.000 0.000 0.328
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0508 0.88925 0.000 0.000 0.012 0.984 0.000 0.004
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.85042 0.000 1.000 0.000 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.85042 0.000 1.000 0.000 0.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0146 0.88960 0.000 0.000 0.000 0.996 0.000 0.004
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.85042 0.000 1.000 0.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.3563 0.70435 0.000 0.000 0.664 0.000 0.000 0.336
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.1857 0.87843 0.000 0.000 0.028 0.924 0.004 0.044
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 5 0.0260 0.70201 0.000 0.008 0.000 0.000 0.992 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0405 0.88947 0.000 0.000 0.008 0.988 0.000 0.004
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0146 0.88960 0.000 0.000 0.000 0.996 0.000 0.004
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0717 0.88985 0.000 0.000 0.008 0.976 0.000 0.016
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.3531 0.70752 0.000 0.000 0.672 0.000 0.000 0.328
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 6 0.7192 0.42603 0.292 0.000 0.088 0.000 0.252 0.368
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.1444 0.83792 0.000 0.928 0.000 0.000 0.072 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0865 0.81051 0.964 0.000 0.000 0.000 0.000 0.036
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.1794 0.88220 0.000 0.000 0.036 0.924 0.000 0.040
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.1967 0.86399 0.000 0.000 0.012 0.904 0.000 0.084
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0146 0.88960 0.000 0.000 0.000 0.996 0.000 0.004
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.3531 0.70752 0.000 0.000 0.672 0.000 0.000 0.328
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.3275 0.79470 0.004 0.000 0.144 0.816 0.000 0.036
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0146 0.80172 0.000 0.000 0.996 0.004 0.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.85042 0.000 1.000 0.000 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0865 0.81319 0.964 0.000 0.000 0.000 0.000 0.036
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 5 0.2300 0.59359 0.000 0.144 0.000 0.000 0.856 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.1857 0.87843 0.000 0.000 0.028 0.924 0.004 0.044
#> CB925BF0-1249-4350-A175-9A4129C43B8D 6 0.5119 -0.60414 0.000 0.000 0.452 0.068 0.004 0.476
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.4349 0.73102 0.000 0.000 0.208 0.708 0.000 0.084
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0692 0.79651 0.000 0.000 0.976 0.020 0.000 0.004
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.3634 0.71884 0.000 0.000 0.696 0.008 0.000 0.296
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 5 0.6077 0.20888 0.088 0.000 0.060 0.000 0.520 0.332
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 6 0.7194 0.41229 0.256 0.000 0.088 0.000 0.288 0.368
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0865 0.81319 0.964 0.000 0.000 0.000 0.000 0.036
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.4659 0.68896 0.000 0.000 0.260 0.656 0.000 0.084
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0806 0.84927 0.000 0.972 0.000 0.000 0.008 0.020
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.4816 0.68111 0.000 0.000 0.264 0.648 0.004 0.084
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0458 0.80268 0.000 0.000 0.984 0.000 0.000 0.016
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0790 0.81166 0.968 0.000 0.000 0.000 0.000 0.032
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.4816 0.68111 0.000 0.000 0.264 0.648 0.004 0.084
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.4834 0.29156 0.596 0.000 0.060 0.000 0.004 0.340
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 5 0.0260 0.70201 0.000 0.008 0.000 0.000 0.992 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.1857 0.87843 0.000 0.000 0.028 0.924 0.004 0.044
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0806 0.88850 0.000 0.000 0.008 0.972 0.000 0.020
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.0508 0.80248 0.000 0.004 0.984 0.000 0.000 0.012
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.4816 0.68111 0.000 0.000 0.264 0.648 0.004 0.084
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.3663 0.72708 0.000 0.784 0.000 0.000 0.148 0.068
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 5 0.1065 0.69558 0.008 0.008 0.000 0.000 0.964 0.020
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.1138 0.78898 0.000 0.000 0.960 0.024 0.004 0.012
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0260 0.80252 0.000 0.000 0.992 0.000 0.000 0.008
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.4321 0.73453 0.000 0.000 0.204 0.712 0.000 0.084
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0405 0.88963 0.000 0.000 0.004 0.988 0.000 0.008
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0405 0.88947 0.000 0.000 0.008 0.988 0.000 0.004
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.2667 0.84835 0.000 0.000 0.128 0.852 0.000 0.020
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.3864 0.42848 0.648 0.000 0.004 0.004 0.000 0.344
#> F779417A-9E29-4B27-BEA3-B23273A66021 5 0.1910 0.62514 0.000 0.108 0.000 0.000 0.892 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 5 0.0260 0.70201 0.000 0.008 0.000 0.000 0.992 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.1334 0.84865 0.000 0.948 0.000 0.000 0.032 0.020
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0547 0.84908 0.000 0.980 0.000 0.000 0.000 0.020
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0865 0.81051 0.964 0.000 0.000 0.000 0.000 0.036
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.7228 0.12658 0.016 0.424 0.080 0.000 0.168 0.312
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.1007 0.81072 0.956 0.000 0.000 0.000 0.000 0.044
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0891 0.88797 0.000 0.000 0.008 0.968 0.000 0.024
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.3233 0.81723 0.000 0.000 0.104 0.832 0.004 0.060
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 5 0.5731 0.31938 0.044 0.000 0.076 0.000 0.552 0.328
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.1924 0.87712 0.000 0.000 0.028 0.920 0.004 0.048
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0713 0.88435 0.000 0.000 0.000 0.972 0.000 0.028
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0865 0.81051 0.964 0.000 0.000 0.000 0.000 0.036
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.2558 0.77928 0.000 0.840 0.000 0.000 0.156 0.004
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 5 0.5411 0.41429 0.036 0.004 0.060 0.000 0.612 0.288
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 5 0.6639 0.27726 0.020 0.076 0.080 0.000 0.496 0.328
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0865 0.81319 0.964 0.000 0.000 0.000 0.000 0.036
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0146 0.88960 0.000 0.000 0.000 0.996 0.000 0.004
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 5 0.0622 0.70089 0.000 0.008 0.000 0.000 0.980 0.012
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0806 0.84927 0.000 0.972 0.000 0.000 0.008 0.020
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.4972 0.67254 0.000 0.000 0.256 0.628 0.000 0.116
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.5029 0.20685 0.544 0.000 0.080 0.000 0.000 0.376
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.85042 0.000 1.000 0.000 0.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.5378 -0.00455 0.460 0.000 0.084 0.008 0.000 0.448
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.3714 0.69963 0.000 0.000 0.656 0.000 0.004 0.340
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 5 0.5663 0.34780 0.028 0.004 0.080 0.000 0.560 0.328
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0858 0.88827 0.000 0.000 0.004 0.968 0.000 0.028
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.81430 1.000 0.000 0.000 0.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.1444 0.83792 0.000 0.928 0.000 0.000 0.072 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.2667 0.79961 0.000 0.852 0.000 0.000 0.128 0.020
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.0547 0.80236 0.000 0.000 0.980 0.000 0.000 0.020
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0790 0.81169 0.968 0.000 0.000 0.000 0.000 0.032
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0790 0.81166 0.968 0.000 0.000 0.000 0.000 0.032
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.1983 0.83509 0.000 0.908 0.000 0.000 0.072 0.020
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.1857 0.87843 0.000 0.000 0.028 0.924 0.004 0.044
#> B3561356-5A80-4C79-B23A-D518425565FE 6 0.7010 0.37660 0.256 0.000 0.064 0.000 0.312 0.368
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.5029 0.20685 0.544 0.000 0.080 0.000 0.000 0.376
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.5207 0.13693 0.512 0.000 0.080 0.004 0.000 0.404
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0405 0.88947 0.000 0.000 0.008 0.988 0.000 0.004
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0603 0.79821 0.000 0.000 0.980 0.016 0.000 0.004
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.85042 0.000 1.000 0.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0865 0.81319 0.964 0.000 0.000 0.000 0.000 0.036
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.1082 0.88269 0.000 0.000 0.004 0.956 0.000 0.040
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0547 0.88683 0.000 0.000 0.000 0.980 0.000 0.020
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 5 0.5428 0.38122 0.028 0.004 0.060 0.000 0.580 0.328
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.3971 0.58974 0.000 0.000 0.548 0.000 0.004 0.448
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.1327 0.80082 0.936 0.000 0.000 0.000 0.000 0.064
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.0865 0.81051 0.964 0.000 0.000 0.000 0.000 0.036
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0603 0.88854 0.000 0.000 0.004 0.980 0.000 0.016
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.1444 0.83792 0.000 0.928 0.000 0.000 0.072 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.4816 0.68111 0.000 0.000 0.264 0.648 0.004 0.084
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.3531 0.70752 0.000 0.000 0.672 0.000 0.000 0.328
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 17548 rows and 122 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 1.000 0.973 0.988 0.4972 0.505 0.505
#> 3 3 0.813 0.921 0.960 0.3427 0.727 0.507
#> 4 4 0.761 0.740 0.864 0.0977 0.888 0.680
#> 5 5 0.830 0.848 0.916 0.0602 0.910 0.683
#> 6 6 0.788 0.787 0.863 0.0566 0.909 0.621
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.983 1.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.0000 0.993 0.000 1.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.983 1.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.983 1.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 1 0.3431 0.925 0.936 0.064
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.993 0.000 1.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 2 0.0000 0.993 0.000 1.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.983 1.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0000 0.983 1.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 1 0.0000 0.983 1.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.993 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.993 0.000 1.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.983 1.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.993 0.000 1.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 2 0.0000 0.993 0.000 1.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 1 0.9491 0.433 0.632 0.368
#> 853120F0-857B-4108-9EC8-727189630C5F 2 0.0000 0.993 0.000 1.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.983 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.993 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.993 0.000 1.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.983 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.993 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 2 0.1184 0.978 0.016 0.984
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.983 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.993 0.000 1.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.983 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.983 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.983 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 2 0.0000 0.993 0.000 1.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.1843 0.967 0.028 0.972
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.993 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.983 1.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.983 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.0000 0.983 1.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.983 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 2 0.0000 0.993 0.000 1.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0000 0.983 1.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 2 0.0000 0.993 0.000 1.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.993 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.983 1.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.993 0.000 1.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.983 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.983 1.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.983 1.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.0000 0.993 0.000 1.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.0000 0.993 0.000 1.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.993 0.000 1.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0000 0.993 0.000 1.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.983 1.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.0000 0.983 1.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.993 0.000 1.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.0000 0.983 1.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.4939 0.877 0.892 0.108
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.983 1.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.0000 0.983 1.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.2778 0.941 0.952 0.048
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.993 0.000 1.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.983 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.983 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.0000 0.993 0.000 1.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.0000 0.983 1.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.993 0.000 1.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.993 0.000 1.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.0000 0.983 1.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.0000 0.993 0.000 1.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.0000 0.983 1.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.983 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.983 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0000 0.983 1.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.983 1.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.993 0.000 1.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.993 0.000 1.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.993 0.000 1.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.993 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.1184 0.970 0.984 0.016
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.993 0.000 1.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.983 1.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.983 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.983 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.0000 0.993 0.000 1.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.983 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.983 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0376 0.980 0.996 0.004
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.993 0.000 1.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0000 0.993 0.000 1.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0000 0.993 0.000 1.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.983 1.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.983 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.993 0.000 1.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.993 0.000 1.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.0000 0.983 1.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.983 1.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.993 0.000 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.983 1.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 2 0.8144 0.658 0.252 0.748
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.0000 0.993 0.000 1.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.983 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0376 0.980 0.996 0.004
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.993 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.993 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0000 0.983 1.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.7376 0.746 0.792 0.208
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.983 1.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.993 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.983 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.993 0.000 1.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.983 1.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.983 1.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.983 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.3114 0.937 0.056 0.944
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.993 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.983 1.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.983 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.983 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0000 0.993 0.000 1.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.1633 0.963 0.976 0.024
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.983 1.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.8443 0.640 0.728 0.272
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.983 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.993 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.0000 0.983 1.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.0000 0.993 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0592 0.942 0.988 0.012 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.4002 0.818 0.000 0.840 0.160
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.0000 0.937 0.000 0.000 1.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.947 1.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.937 0.000 0.000 1.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0237 0.982 0.004 0.996 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0237 0.937 0.000 0.004 0.996
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.947 1.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.2959 0.869 0.900 0.100 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0000 0.937 0.000 0.000 1.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.982 0.000 1.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0237 0.982 0.004 0.996 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.947 1.000 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0592 0.974 0.000 0.988 0.012
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.0237 0.937 0.000 0.004 0.996
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0000 0.937 0.000 0.000 1.000
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.0237 0.937 0.000 0.004 0.996
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.4235 0.783 0.824 0.000 0.176
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.982 0.000 1.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.982 0.000 1.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0237 0.947 0.996 0.000 0.004
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.982 0.000 1.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.2959 0.882 0.000 0.100 0.900
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.3116 0.886 0.892 0.000 0.108
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0237 0.982 0.004 0.996 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0237 0.947 0.996 0.000 0.004
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0237 0.947 0.996 0.000 0.004
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0237 0.947 0.996 0.000 0.004
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.0237 0.937 0.000 0.004 0.996
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.0424 0.977 0.000 0.992 0.008
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.982 0.000 1.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.947 1.000 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 3 0.3686 0.815 0.140 0.000 0.860
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.3412 0.865 0.124 0.000 0.876
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0237 0.947 0.996 0.000 0.004
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.0237 0.937 0.000 0.004 0.996
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.3551 0.853 0.868 0.000 0.132
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0237 0.937 0.000 0.004 0.996
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.982 0.000 1.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.947 1.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0237 0.982 0.004 0.996 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.3116 0.886 0.892 0.000 0.108
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.5859 0.444 0.344 0.000 0.656
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.3192 0.875 0.112 0.000 0.888
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.3038 0.880 0.000 0.104 0.896
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.3038 0.880 0.000 0.104 0.896
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0237 0.982 0.004 0.996 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.1163 0.959 0.000 0.972 0.028
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.947 1.000 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.0237 0.936 0.004 0.000 0.996
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.982 0.000 1.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0000 0.937 0.000 0.000 1.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0000 0.937 0.000 0.000 1.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0424 0.944 0.992 0.008 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.3377 0.887 0.092 0.012 0.896
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.4995 0.808 0.824 0.144 0.032
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0237 0.982 0.004 0.996 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.3412 0.872 0.876 0.000 0.124
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0747 0.942 0.984 0.000 0.016
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.3192 0.873 0.000 0.112 0.888
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.1964 0.913 0.056 0.000 0.944
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.982 0.000 1.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0237 0.982 0.004 0.996 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0000 0.937 0.000 0.000 1.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.1643 0.921 0.000 0.044 0.956
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.2625 0.896 0.084 0.000 0.916
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0237 0.947 0.996 0.000 0.004
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0237 0.947 0.996 0.000 0.004
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.0000 0.937 0.000 0.000 1.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.947 1.000 0.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0237 0.982 0.004 0.996 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0237 0.982 0.004 0.996 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.982 0.000 1.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.982 0.000 1.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.6192 0.275 0.580 0.420 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.982 0.000 1.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.947 1.000 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.4887 0.711 0.772 0.000 0.228
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.3192 0.882 0.888 0.000 0.112
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.0237 0.982 0.004 0.996 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 3 0.5733 0.494 0.324 0.000 0.676
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0237 0.947 0.996 0.000 0.004
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.1031 0.934 0.976 0.024 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.982 0.000 1.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0237 0.982 0.004 0.996 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0000 0.982 0.000 1.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.947 1.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0237 0.947 0.996 0.000 0.004
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0237 0.982 0.004 0.996 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.982 0.000 1.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.3038 0.880 0.104 0.000 0.896
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.947 1.000 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.982 0.000 1.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.3192 0.880 0.888 0.000 0.112
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.0237 0.937 0.000 0.004 0.996
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.1878 0.944 0.004 0.952 0.044
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0237 0.947 0.996 0.000 0.004
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.1753 0.917 0.952 0.048 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.982 0.000 1.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.982 0.000 1.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.0000 0.937 0.000 0.000 1.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 2 0.4399 0.776 0.188 0.812 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.947 1.000 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.982 0.000 1.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.3038 0.889 0.896 0.000 0.104
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0237 0.982 0.004 0.996 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.2448 0.906 0.924 0.000 0.076
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.2959 0.889 0.900 0.000 0.100
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0237 0.947 0.996 0.000 0.004
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0424 0.936 0.000 0.008 0.992
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.982 0.000 1.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.947 1.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0237 0.947 0.996 0.000 0.004
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0424 0.946 0.992 0.000 0.008
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0237 0.982 0.004 0.996 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.0000 0.937 0.000 0.000 1.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.947 1.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.4178 0.798 0.172 0.828 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0237 0.947 0.996 0.000 0.004
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.982 0.000 1.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.0000 0.937 0.000 0.000 1.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.3116 0.877 0.000 0.108 0.892
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.2053 0.818834 0.004 0.072 0.000 0.924
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.3764 0.483067 0.784 0.216 0.000 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.4994 -0.000817 0.520 0.000 0.480 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.4996 0.450151 0.516 0.000 0.000 0.484
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.823653 0.000 0.000 1.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.933642 0.000 1.000 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0376 0.821795 0.004 0.004 0.992 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.0000 0.910798 0.000 0.000 0.000 1.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.2589 0.754925 0.000 0.116 0.000 0.884
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0592 0.823780 0.016 0.000 0.984 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.933642 0.000 1.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.933642 0.000 1.000 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.0188 0.909261 0.004 0.000 0.000 0.996
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.5083 0.669865 0.248 0.716 0.036 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.4941 0.576315 0.436 0.000 0.564 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0592 0.823780 0.016 0.000 0.984 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.4967 0.559431 0.452 0.000 0.548 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0592 0.903482 0.000 0.000 0.016 0.984
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.933642 0.000 1.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.933642 0.000 1.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0188 0.912310 0.000 0.000 0.004 0.996
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0188 0.932480 0.004 0.996 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.4916 0.584426 0.424 0.000 0.576 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.5372 0.539653 0.544 0.000 0.012 0.444
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.933642 0.000 1.000 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0188 0.912310 0.000 0.000 0.004 0.996
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0188 0.912310 0.000 0.000 0.004 0.996
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0188 0.912310 0.000 0.000 0.004 0.996
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.4955 0.568901 0.444 0.000 0.556 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.4857 0.639986 0.284 0.700 0.016 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.1151 0.919577 0.008 0.968 0.024 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.0188 0.909261 0.004 0.000 0.000 0.996
#> F5940915-4123-49B3-95EE-4A0412BE8C30 3 0.6688 -0.038757 0.420 0.000 0.492 0.088
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.4624 0.421472 0.000 0.000 0.660 0.340
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0188 0.912310 0.000 0.000 0.004 0.996
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.4961 0.566895 0.448 0.000 0.552 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.3497 0.746023 0.036 0.000 0.104 0.860
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0707 0.822491 0.020 0.000 0.980 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.933642 0.000 1.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.0469 0.904171 0.012 0.000 0.000 0.988
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.933642 0.000 1.000 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.5564 0.549938 0.544 0.000 0.020 0.436
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0779 0.456094 0.980 0.000 0.016 0.004
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.3074 0.697507 0.000 0.000 0.848 0.152
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0524 0.820383 0.008 0.004 0.988 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.4843 0.602252 0.396 0.000 0.604 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.933642 0.000 1.000 0.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.6194 0.572542 0.260 0.644 0.096 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 4 0.0000 0.910798 0.000 0.000 0.000 1.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.0469 0.824238 0.012 0.000 0.988 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.933642 0.000 1.000 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0592 0.823780 0.016 0.000 0.984 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0592 0.823780 0.016 0.000 0.984 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.6360 0.530571 0.516 0.064 0.000 0.420
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.0524 0.821488 0.008 0.000 0.988 0.004
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.6973 0.566929 0.584 0.220 0.000 0.196
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.933642 0.000 1.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.5775 0.576309 0.560 0.000 0.032 0.408
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0817 0.895487 0.000 0.000 0.024 0.976
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.0469 0.821679 0.012 0.000 0.988 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0376 0.823966 0.004 0.000 0.992 0.004
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.1151 0.919577 0.008 0.968 0.024 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0188 0.931998 0.000 0.996 0.000 0.004
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.1389 0.809109 0.048 0.000 0.952 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0188 0.822901 0.000 0.004 0.996 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.0817 0.816921 0.000 0.000 0.976 0.024
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0188 0.912310 0.000 0.000 0.004 0.996
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0188 0.912310 0.000 0.000 0.004 0.996
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.1722 0.806034 0.048 0.000 0.944 0.008
#> 352471DC-A881-4EA8-B646-EB1200291893 4 0.4998 -0.426624 0.488 0.000 0.000 0.512
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.933642 0.000 1.000 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.933642 0.000 1.000 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0188 0.932203 0.004 0.996 0.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.933642 0.000 1.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 2 0.4964 0.397792 0.004 0.616 0.000 0.380
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.2919 0.866347 0.060 0.896 0.044 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.0592 0.900764 0.016 0.000 0.000 0.984
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.2662 0.807513 0.016 0.000 0.084 0.900
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.5339 0.599994 0.600 0.000 0.016 0.384
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.2654 0.839066 0.108 0.888 0.000 0.004
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.6950 0.530050 0.584 0.000 0.236 0.180
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0188 0.912310 0.000 0.000 0.004 0.996
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.3257 0.681090 0.004 0.152 0.000 0.844
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.933642 0.000 1.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0188 0.931998 0.000 0.996 0.000 0.004
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0921 0.917911 0.028 0.972 0.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.0188 0.909261 0.004 0.000 0.000 0.996
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0188 0.912310 0.000 0.000 0.004 0.996
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0376 0.931757 0.004 0.992 0.000 0.004
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.933642 0.000 1.000 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.1302 0.803853 0.000 0.000 0.956 0.044
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.4907 0.566328 0.580 0.000 0.000 0.420
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.933642 0.000 1.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.4868 0.619997 0.684 0.000 0.012 0.304
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.4713 -0.293453 0.640 0.000 0.360 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.5126 0.165687 0.552 0.444 0.000 0.004
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.1867 0.836317 0.000 0.000 0.072 0.928
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.7778 0.435718 0.416 0.332 0.000 0.252
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0927 0.923890 0.008 0.976 0.016 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.1042 0.921806 0.008 0.972 0.020 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.4985 0.079067 0.468 0.000 0.532 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 2 0.4916 0.318982 0.000 0.576 0.000 0.424
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.0469 0.904939 0.012 0.000 0.000 0.988
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.933642 0.000 1.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.5353 0.557119 0.556 0.000 0.012 0.432
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0992 0.925656 0.008 0.976 0.012 0.004
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.5004 0.595090 0.604 0.004 0.000 0.392
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.4776 0.605153 0.624 0.000 0.000 0.376
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0188 0.912310 0.000 0.000 0.004 0.996
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0188 0.824136 0.004 0.000 0.996 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.933642 0.000 1.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.0188 0.909261 0.004 0.000 0.000 0.996
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0188 0.912310 0.000 0.000 0.004 0.996
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0188 0.912310 0.000 0.000 0.004 0.996
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.1109 0.915780 0.028 0.968 0.000 0.004
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0469 0.445427 0.988 0.000 0.012 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 4 0.4998 -0.430755 0.488 0.000 0.000 0.512
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.3266 0.759048 0.000 0.832 0.000 0.168
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0188 0.912310 0.000 0.000 0.004 0.996
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.1042 0.921806 0.008 0.972 0.020 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.0524 0.824238 0.008 0.000 0.988 0.004
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.4843 0.599035 0.396 0.000 0.604 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.3561 0.6260 0.000 0.260 0.000 0.740 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.2928 0.7572 0.872 0.064 0.000 0.000 0.064
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.3586 0.6327 0.264 0.000 0.736 0.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.3333 0.7842 0.788 0.004 0.000 0.208 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.9636 0.000 0.000 1.000 0.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0324 0.9101 0.004 0.992 0.000 0.000 0.004
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0000 0.9636 0.000 0.000 1.000 0.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.0000 0.9390 0.000 0.000 0.000 1.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.2997 0.7707 0.012 0.148 0.000 0.840 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0000 0.9636 0.000 0.000 1.000 0.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0794 0.9085 0.028 0.972 0.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0451 0.9100 0.008 0.988 0.000 0.000 0.004
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.0404 0.9318 0.000 0.012 0.000 0.988 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 5 0.4171 0.7463 0.112 0.104 0.000 0.000 0.784
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.0451 0.8894 0.008 0.000 0.004 0.000 0.988
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0000 0.9636 0.000 0.000 1.000 0.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0451 0.8894 0.008 0.000 0.004 0.000 0.988
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.9390 0.000 0.000 0.000 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0880 0.9084 0.032 0.968 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0992 0.9087 0.024 0.968 0.008 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.9390 0.000 0.000 0.000 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.1205 0.9071 0.040 0.956 0.004 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.0290 0.8861 0.008 0.000 0.000 0.000 0.992
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.3395 0.7622 0.764 0.000 0.000 0.236 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.1205 0.9018 0.040 0.956 0.000 0.000 0.004
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.9390 0.000 0.000 0.000 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.9390 0.000 0.000 0.000 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.9390 0.000 0.000 0.000 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.0451 0.8894 0.008 0.000 0.004 0.000 0.988
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 5 0.5305 0.6362 0.172 0.152 0.000 0.000 0.676
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.2625 0.8604 0.108 0.876 0.000 0.000 0.016
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.0162 0.9370 0.000 0.004 0.000 0.996 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 3 0.3728 0.6529 0.244 0.000 0.748 0.008 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.1502 0.8930 0.004 0.000 0.056 0.940 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.9390 0.000 0.000 0.000 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0451 0.8894 0.008 0.000 0.004 0.000 0.988
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.3048 0.7680 0.004 0.000 0.000 0.820 0.176
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0162 0.9627 0.000 0.000 0.996 0.000 0.004
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0510 0.9101 0.016 0.984 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.0703 0.9251 0.024 0.000 0.000 0.976 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0451 0.9100 0.008 0.988 0.000 0.000 0.004
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.3675 0.7905 0.788 0.000 0.024 0.188 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.2605 0.7294 0.852 0.000 0.000 0.000 0.148
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.0404 0.9579 0.000 0.000 0.988 0.012 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0000 0.9636 0.000 0.000 1.000 0.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0162 0.8881 0.000 0.000 0.004 0.000 0.996
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.1082 0.9078 0.028 0.964 0.008 0.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 5 0.3493 0.7949 0.108 0.060 0.000 0.000 0.832
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 4 0.0000 0.9390 0.000 0.000 0.000 1.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.0162 0.9633 0.000 0.000 0.996 0.004 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0566 0.9095 0.012 0.984 0.000 0.000 0.004
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0162 0.9633 0.000 0.000 0.996 0.004 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0000 0.9636 0.000 0.000 1.000 0.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.3922 0.7309 0.780 0.180 0.000 0.040 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.0162 0.9633 0.000 0.000 0.996 0.004 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.2929 0.7496 0.840 0.152 0.000 0.008 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0566 0.9092 0.012 0.984 0.000 0.000 0.004
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.4083 0.7526 0.788 0.000 0.132 0.080 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0794 0.9197 0.000 0.000 0.028 0.972 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.0807 0.9516 0.012 0.000 0.976 0.000 0.012
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0162 0.9633 0.000 0.000 0.996 0.004 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0955 0.9083 0.028 0.968 0.004 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0324 0.9101 0.004 0.992 0.000 0.000 0.004
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0290 0.9609 0.008 0.000 0.992 0.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0000 0.9636 0.000 0.000 1.000 0.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.0290 0.9609 0.000 0.000 0.992 0.008 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0000 0.9390 0.000 0.000 0.000 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.9390 0.000 0.000 0.000 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.0290 0.9609 0.008 0.000 0.992 0.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.3003 0.7972 0.812 0.000 0.000 0.188 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0451 0.9100 0.008 0.988 0.000 0.000 0.004
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0771 0.9090 0.020 0.976 0.000 0.000 0.004
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.1041 0.9047 0.032 0.964 0.000 0.000 0.004
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0324 0.9101 0.004 0.992 0.000 0.000 0.004
#> D891BCA1-0323-4277-BAF7-6F505377EA45 2 0.3579 0.6657 0.000 0.756 0.000 0.240 0.004
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.5403 0.5259 0.108 0.644 0.000 0.000 0.248
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.0703 0.9251 0.024 0.000 0.000 0.976 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.3177 0.7118 0.000 0.000 0.208 0.792 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.3370 0.8055 0.824 0.000 0.000 0.148 0.028
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.2929 0.7771 0.180 0.820 0.000 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.3530 0.6795 0.784 0.000 0.204 0.012 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0510 0.9295 0.016 0.000 0.000 0.984 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 2 0.4546 0.1073 0.008 0.532 0.000 0.460 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0794 0.9085 0.028 0.972 0.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0955 0.9004 0.000 0.968 0.000 0.028 0.004
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.1965 0.8675 0.096 0.904 0.000 0.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.0162 0.9371 0.004 0.000 0.000 0.996 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.9390 0.000 0.000 0.000 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.2179 0.8687 0.100 0.896 0.000 0.000 0.004
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0703 0.9092 0.024 0.976 0.000 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.1478 0.9032 0.000 0.000 0.936 0.064 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.2561 0.8107 0.856 0.000 0.000 0.144 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0771 0.9100 0.020 0.976 0.004 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.2392 0.7541 0.888 0.004 0.000 0.004 0.104
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.0566 0.8878 0.012 0.000 0.004 0.000 0.984
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.2966 0.7096 0.816 0.184 0.000 0.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0162 0.9372 0.000 0.000 0.004 0.996 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.4457 0.4627 0.620 0.368 0.000 0.012 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.1831 0.8933 0.076 0.920 0.004 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.2621 0.8654 0.112 0.876 0.004 0.000 0.008
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.1043 0.9355 0.040 0.000 0.960 0.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 4 0.4310 0.3487 0.000 0.392 0.000 0.604 0.004
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.2209 0.8732 0.032 0.056 0.000 0.912 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.1502 0.8954 0.056 0.940 0.000 0.000 0.004
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.3177 0.7837 0.792 0.000 0.000 0.208 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.6092 0.0908 0.108 0.480 0.000 0.004 0.408
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.2653 0.8132 0.880 0.024 0.000 0.096 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.2871 0.7755 0.876 0.004 0.000 0.032 0.088
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.9390 0.000 0.000 0.000 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0000 0.9636 0.000 0.000 1.000 0.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.1043 0.9066 0.040 0.960 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.0000 0.9390 0.000 0.000 0.000 1.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0000 0.9390 0.000 0.000 0.000 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0000 0.9390 0.000 0.000 0.000 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.2068 0.8701 0.092 0.904 0.000 0.000 0.004
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.4182 0.2352 0.400 0.000 0.000 0.000 0.600
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.4364 0.7583 0.736 0.048 0.000 0.216 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.2930 0.7668 0.000 0.832 0.000 0.164 0.004
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.9390 0.000 0.000 0.000 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.1956 0.8915 0.076 0.916 0.008 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.0162 0.9633 0.000 0.000 0.996 0.004 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0771 0.8808 0.020 0.000 0.004 0.000 0.976
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.5579 0.3215 0.500 0.084 0.000 0.396 0.000 0.020
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 6 0.4815 0.6684 0.096 0.140 0.000 0.000 0.040 0.724
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.1765 0.8829 0.000 0.000 0.904 0.000 0.000 0.096
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 6 0.3893 0.7519 0.140 0.000 0.000 0.092 0.000 0.768
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.9510 0.000 0.000 1.000 0.000 0.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 1 0.2883 0.8120 0.788 0.212 0.000 0.000 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0146 0.9498 0.000 0.004 0.996 0.000 0.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.0603 0.8999 0.016 0.000 0.000 0.980 0.000 0.004
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 2 0.3851 0.1077 0.000 0.540 0.000 0.460 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0000 0.9510 0.000 0.000 1.000 0.000 0.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.2586 0.8032 0.100 0.868 0.000 0.000 0.000 0.032
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 1 0.4127 0.7733 0.680 0.284 0.000 0.000 0.000 0.036
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.3979 0.3921 0.628 0.000 0.000 0.360 0.000 0.012
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 5 0.5635 0.7291 0.164 0.060 0.000 0.000 0.648 0.128
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.0260 0.8789 0.000 0.000 0.000 0.000 0.992 0.008
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0000 0.9510 0.000 0.000 1.000 0.000 0.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0260 0.8789 0.000 0.000 0.000 0.000 0.992 0.008
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0146 0.9061 0.000 0.000 0.000 0.996 0.000 0.004
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0692 0.8965 0.020 0.976 0.000 0.000 0.000 0.004
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.1088 0.8903 0.016 0.960 0.000 0.000 0.000 0.024
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.9063 0.000 0.000 0.000 1.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0260 0.8975 0.008 0.992 0.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.3245 0.8151 0.172 0.000 0.000 0.000 0.800 0.028
#> F5A814F6-E824-4DB2-8497-4B99E151D450 6 0.3018 0.7318 0.004 0.000 0.012 0.168 0.000 0.816
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 1 0.3245 0.8050 0.764 0.228 0.000 0.000 0.000 0.008
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0260 0.9049 0.000 0.000 0.000 0.992 0.000 0.008
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.9063 0.000 0.000 0.000 1.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0363 0.9038 0.000 0.000 0.000 0.988 0.000 0.012
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.0291 0.8783 0.000 0.004 0.000 0.000 0.992 0.004
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 5 0.5918 0.6662 0.148 0.052 0.000 0.000 0.604 0.196
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0547 0.8915 0.020 0.980 0.000 0.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.3819 0.3816 0.372 0.000 0.000 0.624 0.000 0.004
#> F5940915-4123-49B3-95EE-4A0412BE8C30 3 0.2748 0.8287 0.000 0.000 0.848 0.024 0.000 0.128
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.5661 0.4686 0.144 0.000 0.232 0.604 0.008 0.012
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.9063 0.000 0.000 0.000 1.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0260 0.8789 0.000 0.000 0.000 0.000 0.992 0.008
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.3706 0.3748 0.000 0.000 0.000 0.620 0.380 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0146 0.9504 0.004 0.000 0.996 0.000 0.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0622 0.8975 0.012 0.980 0.000 0.000 0.000 0.008
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.1500 0.8753 0.052 0.000 0.000 0.936 0.000 0.012
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 1 0.3534 0.8033 0.740 0.244 0.000 0.000 0.000 0.016
#> 692C65BB-BF32-4846-806B-01A285BED1B9 6 0.2669 0.7434 0.000 0.000 0.008 0.156 0.000 0.836
#> CB925BF0-1249-4350-A175-9A4129C43B8D 6 0.1858 0.7292 0.004 0.000 0.000 0.000 0.092 0.904
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.0547 0.9421 0.000 0.000 0.980 0.020 0.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0146 0.9512 0.004 0.000 0.996 0.000 0.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0713 0.8727 0.000 0.028 0.000 0.000 0.972 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0914 0.8941 0.016 0.968 0.000 0.000 0.000 0.016
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 1 0.4897 0.1202 0.624 0.036 0.000 0.000 0.312 0.028
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 4 0.0405 0.9029 0.008 0.000 0.000 0.988 0.000 0.004
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.0146 0.9512 0.004 0.000 0.996 0.000 0.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 1 0.3770 0.7976 0.728 0.244 0.000 0.000 0.000 0.028
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0000 0.9510 0.000 0.000 1.000 0.000 0.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0146 0.9512 0.004 0.000 0.996 0.000 0.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 6 0.4878 0.1406 0.472 0.040 0.000 0.008 0.000 0.480
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.0146 0.9512 0.004 0.000 0.996 0.000 0.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 6 0.3269 0.7323 0.184 0.024 0.000 0.000 0.000 0.792
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 1 0.3023 0.8074 0.768 0.232 0.000 0.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 6 0.3186 0.7221 0.004 0.000 0.100 0.060 0.000 0.836
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.1950 0.8481 0.000 0.000 0.064 0.912 0.000 0.024
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.3300 0.7963 0.152 0.004 0.816 0.000 0.008 0.020
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0146 0.9512 0.004 0.000 0.996 0.000 0.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.1257 0.8882 0.020 0.952 0.000 0.000 0.000 0.028
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 1 0.3431 0.8090 0.756 0.228 0.000 0.000 0.000 0.016
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0000 0.9510 0.000 0.000 1.000 0.000 0.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0146 0.9512 0.004 0.000 0.996 0.000 0.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.0603 0.9436 0.000 0.000 0.980 0.016 0.000 0.004
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0632 0.8970 0.024 0.000 0.000 0.976 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0146 0.9061 0.000 0.000 0.000 0.996 0.000 0.004
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.1088 0.9296 0.000 0.000 0.960 0.016 0.000 0.024
#> 352471DC-A881-4EA8-B646-EB1200291893 6 0.3481 0.7447 0.048 0.000 0.000 0.160 0.000 0.792
#> F779417A-9E29-4B27-BEA3-B23273A66021 1 0.4110 0.7851 0.692 0.268 0.000 0.000 0.000 0.040
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 1 0.3011 0.8014 0.800 0.192 0.000 0.000 0.004 0.004
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 1 0.4095 0.7774 0.724 0.216 0.000 0.000 0.000 0.060
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 1 0.3126 0.8017 0.752 0.248 0.000 0.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.4396 0.7543 0.752 0.116 0.000 0.112 0.000 0.020
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.4408 0.4988 0.056 0.664 0.000 0.000 0.280 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.1225 0.8855 0.036 0.000 0.000 0.952 0.000 0.012
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 3 0.4868 0.1766 0.000 0.000 0.524 0.416 0.000 0.060
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 6 0.2454 0.7596 0.008 0.000 0.000 0.088 0.020 0.884
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 6 0.5145 0.5308 0.176 0.200 0.000 0.000 0.000 0.624
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 6 0.3000 0.6820 0.004 0.000 0.156 0.016 0.000 0.824
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.1910 0.8245 0.000 0.000 0.000 0.892 0.000 0.108
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.4426 0.7428 0.752 0.100 0.000 0.124 0.000 0.024
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.1261 0.8875 0.024 0.952 0.000 0.000 0.000 0.024
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 1 0.3614 0.8112 0.752 0.220 0.000 0.028 0.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.2554 0.8237 0.076 0.876 0.000 0.000 0.000 0.048
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.1196 0.8859 0.040 0.000 0.000 0.952 0.000 0.008
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.9063 0.000 0.000 0.000 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 1 0.4033 0.7595 0.692 0.284 0.000 0.004 0.004 0.016
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.2726 0.7915 0.112 0.856 0.000 0.000 0.000 0.032
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.0748 0.9413 0.004 0.000 0.976 0.016 0.000 0.004
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 6 0.2554 0.7756 0.048 0.000 0.000 0.076 0.000 0.876
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0405 0.8980 0.008 0.988 0.000 0.000 0.000 0.004
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 6 0.1845 0.7545 0.028 0.000 0.000 0.000 0.052 0.920
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.0260 0.8789 0.000 0.000 0.000 0.000 0.992 0.008
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 6 0.3453 0.7300 0.164 0.044 0.000 0.000 0.000 0.792
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0146 0.9059 0.000 0.000 0.004 0.996 0.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 6 0.5814 0.3482 0.320 0.128 0.000 0.020 0.000 0.532
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0405 0.8926 0.004 0.988 0.000 0.000 0.008 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0291 0.8947 0.004 0.992 0.000 0.000 0.004 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.0547 0.9433 0.000 0.000 0.980 0.000 0.000 0.020
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.4856 0.6110 0.640 0.072 0.000 0.280 0.000 0.008
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.4638 0.0943 0.448 0.012 0.000 0.520 0.000 0.020
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 1 0.3023 0.7758 0.808 0.180 0.000 0.000 0.004 0.008
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 6 0.2520 0.7430 0.004 0.000 0.000 0.152 0.000 0.844
#> B3561356-5A80-4C79-B23A-D518425565FE 5 0.6031 0.6351 0.144 0.160 0.000 0.040 0.632 0.024
#> F900E9BE-2400-4451-9434-EE8BC513BA94 6 0.2308 0.7724 0.076 0.012 0.000 0.016 0.000 0.896
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 6 0.2144 0.7576 0.040 0.000 0.000 0.004 0.048 0.908
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.9063 0.000 0.000 0.000 1.000 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0146 0.9512 0.004 0.000 0.996 0.000 0.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0260 0.8975 0.008 0.992 0.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.0692 0.8983 0.020 0.000 0.000 0.976 0.000 0.004
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0146 0.9061 0.000 0.000 0.000 0.996 0.000 0.004
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0458 0.9020 0.000 0.000 0.000 0.984 0.000 0.016
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.4575 0.7146 0.696 0.180 0.000 0.000 0.000 0.124
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.2664 0.7440 0.000 0.000 0.000 0.000 0.816 0.184
#> 12F54761-4F68-4181-8421-88EA858902FC 6 0.6098 0.6172 0.052 0.180 0.000 0.188 0.000 0.580
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.4103 0.7843 0.764 0.136 0.000 0.092 0.000 0.008
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0260 0.9049 0.000 0.000 0.000 0.992 0.000 0.008
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0146 0.8967 0.004 0.996 0.000 0.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.0146 0.9512 0.004 0.000 0.996 0.000 0.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0937 0.8683 0.000 0.040 0.000 0.000 0.960 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", "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 17548 rows and 122 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 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 0.722 0.854 0.926 0.4042 0.618 0.618
#> 3 3 0.600 0.765 0.890 0.3192 0.863 0.778
#> 4 4 0.629 0.702 0.853 0.1176 0.995 0.989
#> 5 5 0.751 0.805 0.910 0.1134 0.866 0.717
#> 6 6 0.716 0.720 0.838 0.0802 0.989 0.967
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.4022 0.8958 0.920 0.080
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.9922 0.0934 0.448 0.552
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.3274 0.9026 0.940 0.060
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.4022 0.8958 0.920 0.080
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 1 0.3274 0.9026 0.940 0.060
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.9261 0.000 1.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 1 0.3274 0.9026 0.940 0.060
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.4022 0.8958 0.920 0.080
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.4431 0.8904 0.908 0.092
#> 806616FE-1855-4284-9265-42842104CB21 1 0.3274 0.9026 0.940 0.060
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.2603 0.8992 0.044 0.956
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.9261 0.000 1.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.4298 0.8917 0.912 0.088
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.9963 0.0174 0.464 0.536
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 1 0.3431 0.9007 0.936 0.064
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 1 0.3274 0.9026 0.940 0.060
#> 853120F0-857B-4108-9EC8-727189630C5F 1 0.3431 0.9007 0.936 0.064
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0376 0.9160 0.996 0.004
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0938 0.9202 0.012 0.988
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.4431 0.8595 0.092 0.908
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0376 0.9160 0.996 0.004
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.9261 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 1 0.8016 0.7405 0.756 0.244
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0376 0.9160 0.996 0.004
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.9261 0.000 1.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0376 0.9160 0.996 0.004
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0376 0.9160 0.996 0.004
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0376 0.9160 0.996 0.004
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.3431 0.9007 0.936 0.064
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.9963 0.0174 0.464 0.536
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.9261 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.4022 0.8958 0.920 0.080
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0376 0.9160 0.996 0.004
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.0376 0.9155 0.996 0.004
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0376 0.9160 0.996 0.004
#> AA403EC3-FD44-4247-B06D-AEF415391E46 1 0.3431 0.9007 0.936 0.064
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0376 0.9160 0.996 0.004
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.3274 0.9026 0.940 0.060
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.9261 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.4022 0.8958 0.920 0.080
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.9261 0.000 1.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0376 0.9160 0.996 0.004
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0376 0.9160 0.996 0.004
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.1184 0.9142 0.984 0.016
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 1 0.3274 0.9026 0.940 0.060
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 1 0.3431 0.9007 0.936 0.064
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.9000 0.6026 0.684 0.316
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 1 0.8443 0.7006 0.728 0.272
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.4022 0.8958 0.920 0.080
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.2778 0.9069 0.952 0.048
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0376 0.9245 0.004 0.996
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.0376 0.9160 0.996 0.004
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.3274 0.9026 0.940 0.060
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.4022 0.8958 0.920 0.080
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.0672 0.9150 0.992 0.008
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.4022 0.8958 0.920 0.080
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.9261 0.000 1.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0376 0.9160 0.996 0.004
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0376 0.9160 0.996 0.004
#> 84E18629-1B13-4696-8E54-121ABE469CD2 1 0.8386 0.7070 0.732 0.268
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.2778 0.9069 0.952 0.048
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.4562 0.8553 0.096 0.904
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.9261 0.000 1.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.3274 0.9026 0.940 0.060
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 1 0.3274 0.9026 0.940 0.060
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.0000 0.9146 1.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0672 0.9161 0.992 0.008
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0376 0.9160 0.996 0.004
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0376 0.9160 0.996 0.004
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.3114 0.9059 0.944 0.056
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.9261 0.000 1.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.9261 0.000 1.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0376 0.9245 0.004 0.996
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.9261 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.9323 0.5072 0.652 0.348
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 1 0.9209 0.5810 0.664 0.336
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.4022 0.8958 0.920 0.080
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0376 0.9160 0.996 0.004
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0376 0.9160 0.996 0.004
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.8267 0.7161 0.740 0.260
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0376 0.9160 0.996 0.004
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0376 0.9160 0.996 0.004
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.4022 0.8958 0.920 0.080
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.4562 0.8553 0.096 0.904
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.4562 0.8552 0.096 0.904
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.9000 0.6026 0.684 0.316
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.4022 0.8958 0.920 0.080
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0376 0.9160 0.996 0.004
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.9261 0.000 1.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.9261 0.000 1.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.0376 0.9160 0.996 0.004
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.3274 0.9045 0.940 0.060
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.9261 0.000 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.3114 0.9059 0.944 0.056
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.3431 0.9007 0.936 0.064
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.8267 0.7161 0.740 0.260
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.9146 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.4022 0.8958 0.920 0.080
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.9261 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.9261 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.3274 0.9026 0.940 0.060
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.9323 0.5072 0.652 0.348
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.4022 0.8958 0.920 0.080
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.9261 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0376 0.9160 0.996 0.004
#> B3561356-5A80-4C79-B23A-D518425565FE 1 0.9323 0.5680 0.652 0.348
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.3274 0.9045 0.940 0.060
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.3114 0.9059 0.944 0.056
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0376 0.9160 0.996 0.004
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 1 0.3274 0.9026 0.940 0.060
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.9261 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.4022 0.8958 0.920 0.080
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0376 0.9160 0.996 0.004
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0376 0.9160 0.996 0.004
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.8267 0.7161 0.740 0.260
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0000 0.9146 1.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.3879 0.8977 0.924 0.076
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.9996 0.0762 0.512 0.488
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0376 0.9160 0.996 0.004
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.4431 0.8595 0.092 0.908
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.0376 0.9160 0.996 0.004
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 1 0.3431 0.9007 0.936 0.064
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.2845 0.8296 0.920 0.068 0.012
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.8487 0.1253 0.364 0.536 0.100
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.5363 0.5733 0.724 0.000 0.276
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.2845 0.8296 0.920 0.068 0.012
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 1 0.5363 0.5733 0.724 0.000 0.276
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.8954 0.000 1.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 1 0.5363 0.5733 0.724 0.000 0.276
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.2845 0.8296 0.920 0.068 0.012
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.3120 0.8200 0.908 0.080 0.012
#> 806616FE-1855-4284-9265-42842104CB21 1 0.5363 0.5733 0.724 0.000 0.276
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.1753 0.8565 0.048 0.952 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0237 0.8943 0.000 0.996 0.004
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.3031 0.8245 0.912 0.076 0.012
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.9389 -0.0191 0.352 0.468 0.180
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.2959 0.8215 0.100 0.000 0.900
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 1 0.5363 0.5733 0.724 0.000 0.276
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.2959 0.8215 0.100 0.000 0.900
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.8502 1.000 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0592 0.8891 0.012 0.988 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.2878 0.8005 0.096 0.904 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.8502 1.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.8954 0.000 1.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.7014 0.7350 0.208 0.080 0.712
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.8502 1.000 0.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.8954 0.000 1.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.8502 1.000 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.8502 1.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.8502 1.000 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.2959 0.8215 0.100 0.000 0.900
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.9389 -0.0191 0.352 0.468 0.180
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.8954 0.000 1.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.2845 0.8296 0.920 0.068 0.012
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0237 0.8494 0.996 0.000 0.004
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.1529 0.8352 0.960 0.000 0.040
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.8502 1.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.2959 0.8215 0.100 0.000 0.900
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0000 0.8502 1.000 0.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.5363 0.5733 0.724 0.000 0.276
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.8954 0.000 1.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.2845 0.8296 0.920 0.068 0.012
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0475 0.8938 0.004 0.992 0.004
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.8502 1.000 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.8502 1.000 0.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.4121 0.7203 0.832 0.000 0.168
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 1 0.5363 0.5733 0.724 0.000 0.276
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.2959 0.8215 0.100 0.000 0.900
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.6143 0.5281 0.684 0.304 0.012
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 3 0.7651 0.7121 0.220 0.108 0.672
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.2845 0.8296 0.920 0.068 0.012
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.5178 0.6046 0.744 0.000 0.256
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0592 0.8905 0.000 0.988 0.012
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.0237 0.8494 0.996 0.000 0.004
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.5363 0.5733 0.724 0.000 0.276
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.2845 0.8296 0.920 0.068 0.012
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.2537 0.8037 0.920 0.000 0.080
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.2845 0.8296 0.920 0.068 0.012
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.8954 0.000 1.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.8502 1.000 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0237 0.8494 0.996 0.000 0.004
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.7624 0.7119 0.224 0.104 0.672
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.5178 0.6046 0.744 0.000 0.256
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.2959 0.7947 0.100 0.900 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0475 0.8938 0.004 0.992 0.004
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.5363 0.5733 0.724 0.000 0.276
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 1 0.5363 0.5733 0.724 0.000 0.276
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.0892 0.8426 0.980 0.000 0.020
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0892 0.8441 0.980 0.000 0.020
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.8502 1.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0237 0.8494 0.996 0.000 0.004
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.1860 0.8396 0.948 0.052 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0475 0.8938 0.004 0.992 0.004
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.8954 0.000 1.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0829 0.8895 0.004 0.984 0.012
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.8954 0.000 1.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.6357 0.4257 0.652 0.336 0.012
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 3 0.8216 0.6712 0.188 0.172 0.640
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.2845 0.8296 0.920 0.068 0.012
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0237 0.8494 0.996 0.000 0.004
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.8502 1.000 0.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.5659 0.6269 0.740 0.248 0.012
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.8502 1.000 0.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.8502 1.000 0.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.2845 0.8296 0.920 0.068 0.012
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.2959 0.7947 0.100 0.900 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.3112 0.7940 0.096 0.900 0.004
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.6143 0.5281 0.684 0.304 0.012
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.2845 0.8296 0.920 0.068 0.012
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.8502 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.8954 0.000 1.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.8954 0.000 1.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.0000 0.8502 1.000 0.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.1964 0.8382 0.944 0.056 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.8954 0.000 1.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.1860 0.8396 0.948 0.052 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.2959 0.8215 0.100 0.000 0.900
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.5659 0.6269 0.740 0.248 0.012
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0747 0.8445 0.984 0.000 0.016
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.2845 0.8296 0.920 0.068 0.012
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.8954 0.000 1.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.8954 0.000 1.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.5363 0.5733 0.724 0.000 0.276
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.6357 0.4257 0.652 0.336 0.012
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.2845 0.8296 0.920 0.068 0.012
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.8954 0.000 1.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.8502 1.000 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 3 0.9278 0.5439 0.288 0.196 0.516
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.1964 0.8382 0.944 0.056 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.1860 0.8396 0.948 0.052 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.8502 1.000 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 1 0.5363 0.5733 0.724 0.000 0.276
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.8954 0.000 1.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.2845 0.8296 0.920 0.068 0.012
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.8502 1.000 0.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.8502 1.000 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.5659 0.6269 0.740 0.248 0.012
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.5706 0.6361 0.320 0.000 0.680
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.2749 0.8315 0.924 0.064 0.012
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.6822 0.0800 0.508 0.480 0.012
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.8502 1.000 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.2878 0.8005 0.096 0.904 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.0000 0.8502 1.000 0.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.2959 0.8215 0.100 0.000 0.900
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.2635 0.7777 0.076 0.020 0.000 0.904
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.8441 0.0884 0.140 0.488 0.068 0.304
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 4 0.6666 0.2690 0.088 0.000 0.404 0.508
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 4 0.2635 0.7777 0.076 0.020 0.000 0.904
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 4 0.6666 0.2690 0.088 0.000 0.404 0.508
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.8795 0.000 1.000 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 4 0.6764 0.2513 0.096 0.000 0.404 0.500
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.2635 0.7777 0.076 0.020 0.000 0.904
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.2722 0.7734 0.032 0.064 0.000 0.904
#> 806616FE-1855-4284-9265-42842104CB21 4 0.6666 0.2690 0.088 0.000 0.404 0.508
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.2500 0.8148 0.040 0.916 0.000 0.044
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0188 0.8782 0.004 0.996 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.2845 0.7744 0.076 0.028 0.000 0.896
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.8943 -0.0551 0.220 0.420 0.068 0.292
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.1211 0.9122 0.040 0.000 0.960 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 4 0.6666 0.2658 0.088 0.000 0.404 0.508
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.1211 0.9122 0.040 0.000 0.960 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0188 0.8018 0.004 0.000 0.000 0.996
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.1767 0.8413 0.044 0.944 0.000 0.012
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.3611 0.7514 0.060 0.860 0.000 0.080
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.8019 0.000 0.000 0.000 1.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.8795 0.000 1.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 1 0.2310 0.7845 0.928 0.004 0.040 0.028
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.0000 0.8019 0.000 0.000 0.000 1.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.8795 0.000 1.000 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.8019 0.000 0.000 0.000 1.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.8019 0.000 0.000 0.000 1.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.8019 0.000 0.000 0.000 1.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.1211 0.9122 0.040 0.000 0.960 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.8943 -0.0551 0.220 0.420 0.068 0.292
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.8795 0.000 1.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.2635 0.7777 0.076 0.020 0.000 0.904
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.0524 0.8004 0.008 0.000 0.004 0.988
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.3149 0.7471 0.088 0.000 0.032 0.880
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.8019 0.000 0.000 0.000 1.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.1211 0.9122 0.040 0.000 0.960 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.0524 0.8006 0.008 0.000 0.004 0.988
#> 50D620F3-5C52-42FB-89A1-6840A7444647 4 0.6764 0.2513 0.096 0.000 0.404 0.500
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.8795 0.000 1.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.2635 0.7777 0.076 0.020 0.000 0.904
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0376 0.8777 0.004 0.992 0.000 0.004
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.0336 0.8010 0.008 0.000 0.000 0.992
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.0000 0.8019 0.000 0.000 0.000 1.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.5677 0.5495 0.064 0.000 0.256 0.680
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 4 0.6716 0.2607 0.092 0.000 0.404 0.504
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.1211 0.9122 0.040 0.000 0.960 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 4 0.5837 0.5111 0.072 0.260 0.000 0.668
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 1 0.1610 0.8132 0.952 0.016 0.000 0.032
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 4 0.2635 0.7777 0.076 0.020 0.000 0.904
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.6264 0.3593 0.064 0.000 0.376 0.560
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0469 0.8742 0.012 0.988 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.1174 0.7937 0.020 0.000 0.012 0.968
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 4 0.6716 0.2607 0.092 0.000 0.404 0.504
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 4 0.2635 0.7777 0.076 0.020 0.000 0.904
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.4655 0.6717 0.088 0.000 0.116 0.796
#> B5474EEB-D585-4668-959C-38F240F55BC2 4 0.2635 0.7777 0.076 0.020 0.000 0.904
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.8795 0.000 1.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.0336 0.8010 0.008 0.000 0.000 0.992
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0376 0.8016 0.004 0.000 0.004 0.992
#> 84E18629-1B13-4696-8E54-121ABE469CD2 1 0.1488 0.8116 0.956 0.012 0.000 0.032
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.6617 0.3153 0.088 0.000 0.380 0.532
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.3679 0.7456 0.060 0.856 0.000 0.084
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0376 0.8777 0.004 0.992 0.000 0.004
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 4 0.6666 0.2690 0.088 0.000 0.404 0.508
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 4 0.6716 0.2607 0.092 0.000 0.404 0.504
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.2399 0.7680 0.048 0.000 0.032 0.920
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.1211 0.7905 0.040 0.000 0.000 0.960
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.8019 0.000 0.000 0.000 1.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.1059 0.7955 0.016 0.000 0.012 0.972
#> 352471DC-A881-4EA8-B646-EB1200291893 4 0.1902 0.7867 0.064 0.004 0.000 0.932
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0376 0.8777 0.004 0.992 0.000 0.004
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.8795 0.000 1.000 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0657 0.8732 0.012 0.984 0.000 0.004
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.8795 0.000 1.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 4 0.6075 0.4069 0.076 0.288 0.000 0.636
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 1 0.3013 0.7702 0.888 0.080 0.000 0.032
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.2635 0.7777 0.076 0.020 0.000 0.904
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0524 0.8004 0.008 0.000 0.004 0.988
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.0000 0.8019 0.000 0.000 0.000 1.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 4 0.5421 0.6107 0.076 0.200 0.000 0.724
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.0336 0.8010 0.008 0.000 0.000 0.992
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0188 0.8016 0.004 0.000 0.000 0.996
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.2635 0.7777 0.076 0.020 0.000 0.904
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.3679 0.7456 0.060 0.856 0.000 0.084
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.2466 0.7671 0.004 0.900 0.000 0.096
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 4 0.5837 0.5111 0.072 0.260 0.000 0.668
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.2635 0.7777 0.076 0.020 0.000 0.904
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.8019 0.000 0.000 0.000 1.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.8795 0.000 1.000 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.8795 0.000 1.000 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.0672 0.7997 0.008 0.000 0.008 0.984
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 4 0.2048 0.7856 0.064 0.008 0.000 0.928
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.8795 0.000 1.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 4 0.1902 0.7867 0.064 0.004 0.000 0.932
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.1211 0.9122 0.040 0.000 0.960 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 4 0.5421 0.6107 0.076 0.200 0.000 0.724
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.3051 0.7428 0.088 0.000 0.028 0.884
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 4 0.2635 0.7777 0.076 0.020 0.000 0.904
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.8795 0.000 1.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.8795 0.000 1.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 4 0.6666 0.2690 0.088 0.000 0.404 0.508
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 4 0.6075 0.4069 0.076 0.288 0.000 0.636
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.2635 0.7777 0.076 0.020 0.000 0.904
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.8795 0.000 1.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.0000 0.8019 0.000 0.000 0.000 1.000
#> B3561356-5A80-4C79-B23A-D518425565FE 1 0.6678 0.4694 0.612 0.148 0.000 0.240
#> F900E9BE-2400-4451-9434-EE8BC513BA94 4 0.2048 0.7856 0.064 0.008 0.000 0.928
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 4 0.1902 0.7867 0.064 0.004 0.000 0.932
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.8019 0.000 0.000 0.000 1.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 4 0.6666 0.2690 0.088 0.000 0.404 0.508
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.8795 0.000 1.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.2635 0.7777 0.076 0.020 0.000 0.904
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0000 0.8019 0.000 0.000 0.000 1.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0188 0.8016 0.004 0.000 0.000 0.996
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 4 0.5421 0.6107 0.076 0.200 0.000 0.724
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.4748 0.3216 0.016 0.000 0.716 0.268
#> 12F54761-4F68-4181-8421-88EA858902FC 4 0.2522 0.7793 0.076 0.016 0.000 0.908
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 4 0.5406 0.0715 0.012 0.480 0.000 0.508
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.8019 0.000 0.000 0.000 1.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.3611 0.7514 0.060 0.860 0.000 0.080
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.0672 0.7997 0.008 0.000 0.008 0.984
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.1211 0.9122 0.040 0.000 0.960 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.1469 0.875 0.948 0.016 0.000 0.036 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.7031 0.169 0.348 0.484 0.000 0.100 0.068
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.0794 0.884 0.028 0.000 0.972 0.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.1469 0.875 0.948 0.016 0.000 0.036 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0794 0.884 0.028 0.000 0.972 0.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.883 0.000 1.000 0.000 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0404 0.834 0.000 0.000 0.988 0.012 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.1469 0.875 0.948 0.016 0.000 0.036 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.2476 0.865 0.904 0.064 0.020 0.012 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0794 0.884 0.028 0.000 0.972 0.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.2046 0.823 0.068 0.916 0.000 0.016 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0162 0.882 0.000 0.996 0.000 0.004 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.1661 0.873 0.940 0.024 0.000 0.036 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.7598 0.036 0.336 0.416 0.000 0.180 0.068
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.0000 0.911 0.000 0.000 0.000 0.000 1.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0703 0.878 0.024 0.000 0.976 0.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0000 0.911 0.000 0.000 0.000 0.000 1.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.1197 0.886 0.952 0.000 0.048 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.1557 0.845 0.052 0.940 0.000 0.008 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.2964 0.760 0.120 0.856 0.000 0.024 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.1121 0.887 0.956 0.000 0.044 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.883 0.000 1.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 4 0.1444 0.766 0.012 0.000 0.000 0.948 0.040
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.1197 0.886 0.952 0.000 0.048 0.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.883 0.000 1.000 0.000 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.1121 0.887 0.956 0.000 0.044 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.1121 0.887 0.956 0.000 0.044 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.1121 0.887 0.956 0.000 0.044 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.0000 0.911 0.000 0.000 0.000 0.000 1.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.7598 0.036 0.336 0.416 0.000 0.180 0.068
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.883 0.000 1.000 0.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.1469 0.875 0.948 0.016 0.000 0.036 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.1341 0.884 0.944 0.000 0.056 0.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.4342 0.665 0.728 0.000 0.232 0.040 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.1121 0.887 0.956 0.000 0.044 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0000 0.911 0.000 0.000 0.000 0.000 1.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.1671 0.875 0.924 0.000 0.076 0.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0404 0.834 0.000 0.000 0.988 0.012 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.883 0.000 1.000 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.1469 0.875 0.948 0.016 0.000 0.036 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0324 0.881 0.004 0.992 0.000 0.004 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.1544 0.879 0.932 0.000 0.068 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.1121 0.887 0.956 0.000 0.044 0.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.3837 0.484 0.308 0.000 0.692 0.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0703 0.882 0.024 0.000 0.976 0.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0000 0.911 0.000 0.000 0.000 0.000 1.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.4430 0.598 0.708 0.256 0.000 0.036 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 4 0.0404 0.784 0.012 0.000 0.000 0.988 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.1469 0.875 0.948 0.016 0.000 0.036 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.2852 0.697 0.172 0.000 0.828 0.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0404 0.878 0.000 0.988 0.000 0.012 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.3003 0.771 0.812 0.000 0.188 0.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0703 0.882 0.024 0.000 0.976 0.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.1469 0.875 0.948 0.016 0.000 0.036 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.4171 0.321 0.396 0.000 0.604 0.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.1469 0.875 0.948 0.016 0.000 0.036 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.883 0.000 1.000 0.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.1544 0.879 0.932 0.000 0.068 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.1270 0.886 0.948 0.000 0.052 0.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 4 0.0693 0.784 0.012 0.000 0.008 0.980 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.1410 0.850 0.060 0.000 0.940 0.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.3012 0.755 0.124 0.852 0.000 0.024 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0324 0.881 0.004 0.992 0.000 0.004 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0794 0.884 0.028 0.000 0.972 0.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0703 0.882 0.024 0.000 0.976 0.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.3395 0.708 0.764 0.000 0.236 0.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.2153 0.873 0.916 0.000 0.044 0.040 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.1121 0.887 0.956 0.000 0.044 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.2516 0.827 0.860 0.000 0.140 0.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0703 0.881 0.976 0.000 0.000 0.024 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0324 0.881 0.004 0.992 0.000 0.004 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.883 0.000 1.000 0.000 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0566 0.877 0.004 0.984 0.000 0.012 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.883 0.000 1.000 0.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.4594 0.536 0.680 0.284 0.000 0.036 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 4 0.1877 0.743 0.012 0.064 0.000 0.924 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.1469 0.875 0.948 0.016 0.000 0.036 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.1671 0.875 0.924 0.000 0.076 0.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.1197 0.886 0.952 0.000 0.048 0.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.3988 0.682 0.768 0.196 0.000 0.036 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.1544 0.879 0.932 0.000 0.068 0.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.1341 0.884 0.944 0.000 0.056 0.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.1469 0.875 0.948 0.016 0.000 0.036 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.3012 0.755 0.124 0.852 0.000 0.024 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.2124 0.778 0.096 0.900 0.000 0.004 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.4430 0.598 0.708 0.256 0.000 0.036 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.1469 0.875 0.948 0.016 0.000 0.036 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.1121 0.887 0.956 0.000 0.044 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.883 0.000 1.000 0.000 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.883 0.000 1.000 0.000 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.1851 0.868 0.912 0.000 0.088 0.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0865 0.880 0.972 0.004 0.000 0.024 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.883 0.000 1.000 0.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0703 0.881 0.976 0.000 0.000 0.024 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.0000 0.911 0.000 0.000 0.000 0.000 1.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.3988 0.682 0.768 0.196 0.000 0.036 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.4138 0.416 0.616 0.000 0.384 0.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.1469 0.875 0.948 0.016 0.000 0.036 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.883 0.000 1.000 0.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.883 0.000 1.000 0.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.0794 0.884 0.028 0.000 0.972 0.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.4594 0.536 0.680 0.284 0.000 0.036 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.1469 0.875 0.948 0.016 0.000 0.036 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.883 0.000 1.000 0.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.1121 0.887 0.956 0.000 0.044 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 4 0.5940 0.384 0.284 0.144 0.000 0.572 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0865 0.880 0.972 0.004 0.000 0.024 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0703 0.881 0.976 0.000 0.000 0.024 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.1121 0.887 0.956 0.000 0.044 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0794 0.884 0.028 0.000 0.972 0.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.883 0.000 1.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.1469 0.875 0.948 0.016 0.000 0.036 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.1121 0.887 0.956 0.000 0.044 0.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.1410 0.883 0.940 0.000 0.060 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.3988 0.682 0.768 0.196 0.000 0.036 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.3612 0.357 0.268 0.000 0.000 0.000 0.732
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.1364 0.876 0.952 0.012 0.000 0.036 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.4656 0.141 0.508 0.480 0.000 0.012 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.1121 0.887 0.956 0.000 0.044 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.2964 0.760 0.120 0.856 0.000 0.024 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.1851 0.868 0.912 0.000 0.088 0.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0000 0.911 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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.3428 0.681 0.000 0.000 0.000 0.696 0.000 0.304
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 6 0.5765 0.878 0.064 0.156 0.000 0.068 0.036 0.676
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.1007 0.887 0.000 0.000 0.956 0.044 0.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 4 0.3428 0.681 0.000 0.000 0.000 0.696 0.000 0.304
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.1007 0.887 0.000 0.000 0.956 0.044 0.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0146 0.846 0.000 0.996 0.000 0.000 0.000 0.004
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0363 0.831 0.000 0.000 0.988 0.000 0.000 0.012
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.3428 0.681 0.000 0.000 0.000 0.696 0.000 0.304
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.3345 0.710 0.000 0.020 0.000 0.776 0.000 0.204
#> 806616FE-1855-4284-9265-42842104CB21 3 0.1007 0.887 0.000 0.000 0.956 0.044 0.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.3221 0.706 0.000 0.736 0.000 0.000 0.000 0.264
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.2697 0.750 0.000 0.812 0.000 0.000 0.000 0.188
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.3653 0.679 0.000 0.008 0.000 0.692 0.000 0.300
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 6 0.6118 0.934 0.144 0.108 0.000 0.064 0.036 0.648
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.0000 0.962 0.000 0.000 0.000 0.000 1.000 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.1007 0.881 0.000 0.000 0.956 0.044 0.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0000 0.962 0.000 0.000 0.000 0.000 1.000 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0291 0.756 0.000 0.000 0.004 0.992 0.000 0.004
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.2340 0.796 0.000 0.852 0.000 0.000 0.000 0.148
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.3371 0.615 0.000 0.708 0.000 0.000 0.000 0.292
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.756 0.000 0.000 0.000 1.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.1075 0.846 0.000 0.952 0.000 0.000 0.000 0.048
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 1 0.0937 0.793 0.960 0.000 0.000 0.000 0.040 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.0405 0.755 0.000 0.000 0.008 0.988 0.000 0.004
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0146 0.846 0.000 0.996 0.000 0.000 0.000 0.004
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0146 0.755 0.000 0.000 0.000 0.996 0.000 0.004
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.756 0.000 0.000 0.000 1.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0146 0.755 0.000 0.000 0.000 0.996 0.000 0.004
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.0000 0.962 0.000 0.000 0.000 0.000 1.000 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 6 0.6118 0.934 0.144 0.108 0.000 0.064 0.036 0.648
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.1075 0.846 0.000 0.952 0.000 0.000 0.000 0.048
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.3428 0.681 0.000 0.000 0.000 0.696 0.000 0.304
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.0363 0.756 0.000 0.000 0.012 0.988 0.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.3848 0.580 0.040 0.000 0.204 0.752 0.000 0.004
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.756 0.000 0.000 0.000 1.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0000 0.962 0.000 0.000 0.000 0.000 1.000 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.1151 0.747 0.000 0.000 0.032 0.956 0.000 0.012
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0363 0.831 0.000 0.000 0.988 0.000 0.000 0.012
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.1075 0.846 0.000 0.952 0.000 0.000 0.000 0.048
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.3428 0.681 0.000 0.000 0.000 0.696 0.000 0.304
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.2730 0.753 0.000 0.808 0.000 0.000 0.000 0.192
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.0777 0.751 0.000 0.000 0.024 0.972 0.000 0.004
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.3198 0.491 0.000 0.000 0.000 0.740 0.000 0.260
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.3563 0.501 0.000 0.000 0.664 0.336 0.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0937 0.885 0.000 0.000 0.960 0.040 0.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0000 0.962 0.000 0.000 0.000 0.000 1.000 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 4 0.5564 0.251 0.000 0.140 0.000 0.472 0.000 0.388
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 1 0.0000 0.810 1.000 0.000 0.000 0.000 0.000 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 4 0.3428 0.681 0.000 0.000 0.000 0.696 0.000 0.304
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.2793 0.711 0.000 0.000 0.800 0.200 0.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.3151 0.696 0.000 0.748 0.000 0.000 0.000 0.252
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.2378 0.679 0.000 0.000 0.152 0.848 0.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0937 0.885 0.000 0.000 0.960 0.040 0.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 4 0.3446 0.678 0.000 0.000 0.000 0.692 0.000 0.308
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.3907 0.283 0.000 0.000 0.588 0.408 0.000 0.004
#> B5474EEB-D585-4668-959C-38F240F55BC2 4 0.3446 0.678 0.000 0.000 0.000 0.692 0.000 0.308
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0146 0.846 0.000 0.996 0.000 0.000 0.000 0.004
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.0777 0.751 0.000 0.000 0.024 0.972 0.000 0.004
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0260 0.756 0.000 0.000 0.008 0.992 0.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 1 0.0260 0.809 0.992 0.000 0.008 0.000 0.000 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.1501 0.854 0.000 0.000 0.924 0.076 0.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.3428 0.596 0.000 0.696 0.000 0.000 0.000 0.304
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.2730 0.753 0.000 0.808 0.000 0.000 0.000 0.192
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.1007 0.887 0.000 0.000 0.956 0.044 0.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0937 0.885 0.000 0.000 0.960 0.040 0.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.2994 0.621 0.000 0.000 0.208 0.788 0.000 0.004
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.1082 0.738 0.040 0.000 0.000 0.956 0.000 0.004
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0146 0.755 0.000 0.000 0.000 0.996 0.000 0.004
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.1814 0.718 0.000 0.000 0.100 0.900 0.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 4 0.3175 0.705 0.000 0.000 0.000 0.744 0.000 0.256
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.2854 0.739 0.000 0.792 0.000 0.000 0.000 0.208
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0146 0.846 0.000 0.996 0.000 0.000 0.000 0.004
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.3175 0.692 0.000 0.744 0.000 0.000 0.000 0.256
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0146 0.846 0.000 0.996 0.000 0.000 0.000 0.004
#> D891BCA1-0323-4277-BAF7-6F505377EA45 4 0.5368 0.280 0.000 0.116 0.000 0.508 0.000 0.376
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 1 0.1633 0.783 0.932 0.024 0.000 0.000 0.000 0.044
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.3428 0.681 0.000 0.000 0.000 0.696 0.000 0.304
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0790 0.749 0.000 0.000 0.032 0.968 0.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.0405 0.755 0.000 0.000 0.008 0.988 0.000 0.004
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 4 0.5254 0.358 0.000 0.100 0.000 0.508 0.000 0.392
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.0777 0.751 0.000 0.000 0.024 0.972 0.000 0.004
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0508 0.754 0.000 0.000 0.012 0.984 0.000 0.004
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.3428 0.681 0.000 0.000 0.000 0.696 0.000 0.304
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.3428 0.594 0.000 0.696 0.000 0.000 0.000 0.304
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.4186 0.595 0.000 0.728 0.000 0.080 0.000 0.192
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 4 0.5564 0.251 0.000 0.140 0.000 0.472 0.000 0.388
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.3428 0.681 0.000 0.000 0.000 0.696 0.000 0.304
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.756 0.000 0.000 0.000 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0146 0.846 0.000 0.996 0.000 0.000 0.000 0.004
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.1075 0.846 0.000 0.952 0.000 0.000 0.000 0.048
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.1152 0.736 0.000 0.000 0.044 0.952 0.000 0.004
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 4 0.3351 0.692 0.000 0.000 0.000 0.712 0.000 0.288
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.1075 0.846 0.000 0.952 0.000 0.000 0.000 0.048
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 4 0.3309 0.695 0.000 0.000 0.000 0.720 0.000 0.280
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.0000 0.962 0.000 0.000 0.000 0.000 1.000 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 4 0.5254 0.358 0.000 0.100 0.000 0.508 0.000 0.392
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.3795 0.439 0.000 0.000 0.364 0.632 0.000 0.004
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 4 0.3446 0.678 0.000 0.000 0.000 0.692 0.000 0.308
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.1075 0.846 0.000 0.952 0.000 0.000 0.000 0.048
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.1075 0.846 0.000 0.952 0.000 0.000 0.000 0.048
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.1007 0.887 0.000 0.000 0.956 0.044 0.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 4 0.5368 0.280 0.000 0.116 0.000 0.508 0.000 0.376
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.3428 0.681 0.000 0.000 0.000 0.696 0.000 0.304
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0146 0.846 0.000 0.996 0.000 0.000 0.000 0.004
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.0146 0.755 0.000 0.000 0.000 0.996 0.000 0.004
#> B3561356-5A80-4C79-B23A-D518425565FE 1 0.5502 -0.278 0.548 0.036 0.000 0.060 0.000 0.356
#> F900E9BE-2400-4451-9434-EE8BC513BA94 4 0.3351 0.692 0.000 0.000 0.000 0.712 0.000 0.288
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 4 0.3309 0.695 0.000 0.000 0.000 0.720 0.000 0.280
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.756 0.000 0.000 0.000 1.000 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.1007 0.887 0.000 0.000 0.956 0.044 0.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.1075 0.846 0.000 0.952 0.000 0.000 0.000 0.048
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.3428 0.681 0.000 0.000 0.000 0.696 0.000 0.304
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0547 0.757 0.000 0.000 0.000 0.980 0.000 0.020
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0603 0.753 0.000 0.000 0.016 0.980 0.000 0.004
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 4 0.5254 0.358 0.000 0.100 0.000 0.508 0.000 0.392
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.4039 0.695 0.000 0.000 0.000 0.060 0.732 0.208
#> 12F54761-4F68-4181-8421-88EA858902FC 4 0.3390 0.686 0.000 0.000 0.000 0.704 0.000 0.296
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 4 0.5887 -0.034 0.000 0.312 0.000 0.464 0.000 0.224
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0146 0.755 0.000 0.000 0.000 0.996 0.000 0.004
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.3050 0.668 0.000 0.764 0.000 0.000 0.000 0.236
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.1219 0.735 0.000 0.000 0.048 0.948 0.000 0.004
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0000 0.962 0.000 0.000 0.000 0.000 1.000 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 17548 rows and 122 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 0.510 0.739 0.861 0.4515 0.545 0.545
#> 3 3 0.501 0.543 0.745 0.3773 0.834 0.705
#> 4 4 0.659 0.757 0.820 0.1246 0.758 0.491
#> 5 5 0.724 0.856 0.822 0.0833 0.919 0.727
#> 6 6 0.826 0.845 0.855 0.0556 0.959 0.815
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.895 0.56981 0.688 0.312
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.839 0.63208 0.268 0.732
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.000 0.83523 1.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.881 0.58705 0.700 0.300
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 1 0.000 0.83523 1.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.373 0.90407 0.072 0.928
#> 9264567D-4524-46AF-A851-C091C3CD76CF 1 0.730 0.65313 0.796 0.204
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.881 0.58705 0.700 0.300
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.895 0.56981 0.688 0.312
#> 806616FE-1855-4284-9265-42842104CB21 1 0.000 0.83523 1.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.373 0.90407 0.072 0.928
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.373 0.90407 0.072 0.928
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.895 0.56981 0.688 0.312
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.373 0.90407 0.072 0.928
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 1 0.921 0.47178 0.664 0.336
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 1 0.000 0.83523 1.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 1 0.921 0.47178 0.664 0.336
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.000 0.83523 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.373 0.90407 0.072 0.928
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.373 0.90407 0.072 0.928
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.000 0.83523 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.373 0.90407 0.072 0.928
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 1 0.925 0.46389 0.660 0.340
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.000 0.83523 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.373 0.90407 0.072 0.928
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.000 0.83523 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.000 0.83523 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.000 0.83523 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.925 0.46389 0.660 0.340
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.975 0.31450 0.408 0.592
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.373 0.90407 0.072 0.928
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.895 0.56981 0.688 0.312
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.000 0.83523 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.000 0.83523 1.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.000 0.83523 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 1 0.925 0.46389 0.660 0.340
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.000 0.83523 1.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.204 0.81030 0.968 0.032
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.373 0.90407 0.072 0.928
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.881 0.58705 0.700 0.300
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.373 0.90407 0.072 0.928
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.000 0.83523 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.000 0.83523 1.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.000 0.83523 1.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 1 0.402 0.77634 0.920 0.080
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 1.000 -0.01178 0.492 0.508
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.373 0.90407 0.072 0.928
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.827 0.63013 0.260 0.740
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.895 0.56981 0.688 0.312
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.000 0.83523 1.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.373 0.90407 0.072 0.928
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.000 0.83523 1.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.000 0.83523 1.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.895 0.56981 0.688 0.312
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.000 0.83523 1.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.895 0.56981 0.688 0.312
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.373 0.90407 0.072 0.928
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.000 0.83523 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.000 0.83523 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.978 0.33545 0.412 0.588
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.000 0.83523 1.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.373 0.90407 0.072 0.928
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.373 0.90407 0.072 0.928
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.000 0.83523 1.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 1 0.662 0.67261 0.828 0.172
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.000 0.83523 1.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.000 0.83523 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.000 0.83523 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.000 0.83523 1.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.204 0.81994 0.968 0.032
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.373 0.90407 0.072 0.928
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.373 0.90407 0.072 0.928
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.373 0.90407 0.072 0.928
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.373 0.90407 0.072 0.928
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.952 0.45118 0.628 0.372
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.482 0.83595 0.104 0.896
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.855 0.61085 0.720 0.280
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.000 0.83523 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.000 0.83523 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.999 0.12421 0.520 0.480
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.000 0.83523 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.000 0.83523 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.917 0.53183 0.668 0.332
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.373 0.90407 0.072 0.928
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.373 0.90407 0.072 0.928
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.697 0.76560 0.188 0.812
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.881 0.58705 0.700 0.300
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.000 0.83523 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.373 0.90407 0.072 0.928
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.373 0.90407 0.072 0.928
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.000 0.83523 1.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.881 0.58705 0.700 0.300
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.373 0.90407 0.072 0.928
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.311 0.80643 0.944 0.056
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.925 0.46389 0.660 0.340
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.913 0.51238 0.328 0.672
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.000 0.83523 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.895 0.56981 0.688 0.312
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.373 0.90407 0.072 0.928
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.373 0.90407 0.072 0.928
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.000 0.83523 1.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.999 0.12516 0.520 0.480
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.895 0.56981 0.688 0.312
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.373 0.90407 0.072 0.928
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.000 0.83523 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.373 0.90407 0.072 0.928
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.881 0.58705 0.700 0.300
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.662 0.71999 0.828 0.172
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.000 0.83523 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 1 0.373 0.78276 0.928 0.072
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.373 0.90407 0.072 0.928
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.895 0.56981 0.688 0.312
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.000 0.83523 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.000 0.83523 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.373 0.90407 0.072 0.928
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.373 0.77541 0.928 0.072
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.895 0.56981 0.688 0.312
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.999 0.01004 0.480 0.520
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.000 0.83523 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.373 0.90407 0.072 0.928
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.000 0.83523 1.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 1.000 0.00263 0.488 0.512
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.8841 0.516 0.580 0.216 0.204
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.7742 0.534 0.060 0.584 0.356
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.6168 -0.356 0.588 0.000 0.412
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.8804 0.518 0.584 0.212 0.204
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 1 0.6168 -0.356 0.588 0.000 0.412
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0237 0.926 0.000 0.996 0.004
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.6291 0.600 0.468 0.000 0.532
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.8804 0.518 0.584 0.212 0.204
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.8689 0.519 0.596 0.200 0.204
#> 806616FE-1855-4284-9265-42842104CB21 1 0.6168 -0.356 0.588 0.000 0.412
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0424 0.927 0.000 0.992 0.008
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0424 0.926 0.000 0.992 0.008
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.8841 0.516 0.580 0.216 0.204
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.2955 0.903 0.008 0.912 0.080
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.5036 0.757 0.172 0.020 0.808
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.6309 0.553 0.500 0.000 0.500
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.5036 0.757 0.172 0.020 0.808
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.2878 0.435 0.904 0.000 0.096
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.1643 0.923 0.000 0.956 0.044
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.1643 0.923 0.000 0.956 0.044
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.560 1.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.1643 0.923 0.000 0.956 0.044
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.5036 0.757 0.172 0.020 0.808
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.560 1.000 0.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.1031 0.924 0.000 0.976 0.024
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.560 1.000 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0237 0.561 0.996 0.000 0.004
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.560 1.000 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.5036 0.757 0.172 0.020 0.808
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 3 0.9087 0.230 0.188 0.268 0.544
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.1643 0.923 0.000 0.956 0.044
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.8841 0.516 0.580 0.216 0.204
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.560 1.000 0.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.6168 -0.356 0.588 0.000 0.412
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0237 0.561 0.996 0.000 0.004
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.5036 0.757 0.172 0.020 0.808
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.3192 0.408 0.888 0.000 0.112
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.6295 0.595 0.472 0.000 0.528
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.1643 0.923 0.000 0.956 0.044
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.8804 0.518 0.584 0.212 0.204
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0237 0.926 0.000 0.996 0.004
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.560 1.000 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0747 0.557 0.984 0.000 0.016
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.1860 0.510 0.948 0.000 0.052
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.6308 0.569 0.492 0.000 0.508
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.5292 0.755 0.172 0.028 0.800
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.5643 0.742 0.020 0.760 0.220
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 3 0.9283 0.490 0.180 0.320 0.500
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.8804 0.518 0.584 0.212 0.204
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.6140 -0.341 0.596 0.000 0.404
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0237 0.926 0.000 0.996 0.004
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.6168 -0.356 0.588 0.000 0.412
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.6168 -0.356 0.588 0.000 0.412
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.8841 0.516 0.580 0.216 0.204
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.6168 -0.356 0.588 0.000 0.412
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.8841 0.516 0.580 0.216 0.204
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0237 0.926 0.000 0.996 0.004
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.560 1.000 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.560 1.000 0.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.8288 0.673 0.332 0.096 0.572
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.6168 -0.356 0.588 0.000 0.412
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.2261 0.913 0.000 0.932 0.068
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0237 0.926 0.000 0.996 0.004
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.6168 -0.356 0.588 0.000 0.412
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.6308 0.569 0.492 0.000 0.508
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.6168 -0.356 0.588 0.000 0.412
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.560 1.000 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.560 1.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.1411 0.529 0.964 0.000 0.036
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.5708 0.534 0.768 0.028 0.204
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0237 0.926 0.000 0.996 0.004
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0237 0.926 0.000 0.996 0.004
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.1411 0.909 0.000 0.964 0.036
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0424 0.926 0.000 0.992 0.008
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.9156 0.462 0.540 0.256 0.204
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.4682 0.786 0.004 0.804 0.192
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.8290 0.525 0.632 0.164 0.204
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.560 1.000 0.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.560 1.000 0.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.9601 0.233 0.432 0.364 0.204
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0424 0.556 0.992 0.000 0.008
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.560 1.000 0.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.8841 0.516 0.580 0.216 0.204
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.2448 0.909 0.000 0.924 0.076
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0237 0.926 0.000 0.996 0.004
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.6124 0.724 0.036 0.744 0.220
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.8804 0.518 0.584 0.212 0.204
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.560 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.1031 0.924 0.000 0.976 0.024
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.1643 0.923 0.000 0.956 0.044
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.6168 -0.356 0.588 0.000 0.412
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.8689 0.521 0.596 0.200 0.204
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.1643 0.923 0.000 0.956 0.044
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.6542 0.533 0.736 0.060 0.204
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.5036 0.757 0.172 0.020 0.808
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.8608 0.438 0.192 0.604 0.204
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0592 0.554 0.988 0.000 0.012
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.8841 0.516 0.580 0.216 0.204
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.1643 0.923 0.000 0.956 0.044
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.1643 0.923 0.000 0.956 0.044
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.6168 -0.356 0.588 0.000 0.412
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.9527 0.317 0.464 0.332 0.204
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.8841 0.516 0.580 0.216 0.204
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.1031 0.924 0.000 0.976 0.024
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.560 1.000 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.2356 0.911 0.000 0.928 0.072
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.8689 0.521 0.596 0.200 0.204
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.8085 0.527 0.648 0.148 0.204
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0237 0.561 0.996 0.000 0.004
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.6309 0.562 0.496 0.000 0.504
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.1643 0.923 0.000 0.956 0.044
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.8841 0.516 0.580 0.216 0.204
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0237 0.561 0.996 0.000 0.004
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.560 1.000 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.4834 0.759 0.004 0.792 0.204
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.4750 0.738 0.216 0.000 0.784
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.8841 0.516 0.580 0.216 0.204
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.9579 0.235 0.432 0.368 0.200
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.560 1.000 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.1643 0.923 0.000 0.956 0.044
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.6168 -0.356 0.588 0.000 0.412
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.5292 0.755 0.172 0.028 0.800
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.5866 0.902 0.624 0.052 0.000 0.324
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.5552 0.551 0.708 0.236 0.008 0.048
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 4 0.6469 0.625 0.248 0.000 0.124 0.628
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.5812 0.900 0.624 0.048 0.000 0.328
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 4 0.6731 0.610 0.248 0.000 0.148 0.604
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0921 0.862 0.028 0.972 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 4 0.7622 0.413 0.248 0.000 0.280 0.472
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.5812 0.900 0.624 0.048 0.000 0.328
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.5736 0.898 0.628 0.044 0.000 0.328
#> 806616FE-1855-4284-9265-42842104CB21 4 0.6731 0.610 0.248 0.000 0.148 0.604
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.4337 0.863 0.140 0.808 0.052 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0921 0.862 0.028 0.972 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.5866 0.902 0.624 0.052 0.000 0.324
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.6551 0.714 0.240 0.624 0.136 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.2748 0.920 0.020 0.004 0.904 0.072
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 4 0.7369 0.506 0.248 0.000 0.228 0.524
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.2635 0.921 0.016 0.004 0.908 0.072
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.3672 0.698 0.164 0.000 0.012 0.824
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.4477 0.858 0.108 0.808 0.084 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.4581 0.856 0.120 0.800 0.080 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0707 0.728 0.020 0.000 0.000 0.980
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.4477 0.858 0.108 0.808 0.084 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.2856 0.918 0.024 0.004 0.900 0.072
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.0336 0.735 0.008 0.000 0.000 0.992
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0188 0.858 0.004 0.996 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0707 0.728 0.020 0.000 0.000 0.980
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0707 0.728 0.020 0.000 0.000 0.980
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0707 0.728 0.020 0.000 0.000 0.980
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.2635 0.921 0.016 0.004 0.908 0.072
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.8735 0.440 0.500 0.216 0.196 0.088
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.4477 0.858 0.108 0.808 0.084 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.5866 0.902 0.624 0.052 0.000 0.324
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.0336 0.735 0.008 0.000 0.000 0.992
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.6731 0.610 0.248 0.000 0.148 0.604
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0707 0.728 0.020 0.000 0.000 0.980
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.2635 0.921 0.016 0.004 0.908 0.072
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.1488 0.732 0.012 0.000 0.032 0.956
#> 50D620F3-5C52-42FB-89A1-6840A7444647 4 0.7606 0.421 0.248 0.000 0.276 0.476
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.4477 0.858 0.108 0.808 0.084 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.5866 0.902 0.624 0.052 0.000 0.324
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0921 0.862 0.028 0.972 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.0336 0.735 0.008 0.000 0.000 0.992
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.0817 0.723 0.024 0.000 0.000 0.976
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.4872 0.672 0.244 0.000 0.028 0.728
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 4 0.7459 0.480 0.248 0.000 0.244 0.508
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.2365 0.910 0.012 0.004 0.920 0.064
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.4331 0.426 0.712 0.288 0.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.8133 0.212 0.052 0.532 0.264 0.152
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.5866 0.902 0.624 0.052 0.000 0.324
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.6442 0.627 0.244 0.000 0.124 0.632
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0921 0.862 0.028 0.972 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.6469 0.625 0.248 0.000 0.124 0.628
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 4 0.6731 0.610 0.248 0.000 0.148 0.604
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.5866 0.902 0.624 0.052 0.000 0.324
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.6731 0.610 0.248 0.000 0.148 0.604
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.5866 0.902 0.624 0.052 0.000 0.324
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0921 0.862 0.028 0.972 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.0336 0.735 0.008 0.000 0.000 0.992
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0000 0.738 0.000 0.000 0.000 1.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.9254 -0.135 0.256 0.080 0.340 0.324
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.6469 0.625 0.248 0.000 0.124 0.628
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.5864 0.774 0.264 0.664 0.072 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0921 0.862 0.028 0.972 0.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 4 0.6731 0.610 0.248 0.000 0.148 0.604
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 4 0.7556 0.444 0.248 0.000 0.264 0.488
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.6469 0.625 0.248 0.000 0.124 0.628
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0592 0.731 0.016 0.000 0.000 0.984
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0707 0.728 0.020 0.000 0.000 0.980
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.4050 0.697 0.168 0.000 0.024 0.808
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.4790 0.839 0.620 0.000 0.000 0.380
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0921 0.862 0.028 0.972 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0921 0.862 0.028 0.972 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.3311 0.783 0.172 0.828 0.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0921 0.862 0.028 0.972 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.5966 0.897 0.624 0.060 0.000 0.316
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.5496 0.771 0.088 0.724 0.188 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.5511 0.877 0.620 0.028 0.000 0.352
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0188 0.738 0.004 0.000 0.000 0.996
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.0707 0.728 0.020 0.000 0.000 0.980
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.5953 0.867 0.656 0.076 0.000 0.268
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.0376 0.738 0.004 0.000 0.004 0.992
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0707 0.728 0.020 0.000 0.000 0.980
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.5866 0.902 0.624 0.052 0.000 0.324
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.5864 0.774 0.264 0.664 0.072 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0921 0.862 0.028 0.972 0.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.5025 0.520 0.716 0.252 0.000 0.032
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.5812 0.900 0.624 0.048 0.000 0.328
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0707 0.728 0.020 0.000 0.000 0.980
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0188 0.858 0.004 0.996 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.4477 0.858 0.108 0.808 0.084 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.6566 0.622 0.236 0.000 0.140 0.624
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.5773 0.894 0.620 0.044 0.000 0.336
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.4477 0.858 0.108 0.808 0.084 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.4790 0.839 0.620 0.000 0.000 0.380
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.2635 0.921 0.016 0.004 0.908 0.072
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.6308 0.722 0.656 0.208 0.000 0.136
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.1724 0.732 0.032 0.000 0.020 0.948
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.5866 0.902 0.624 0.052 0.000 0.324
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.4477 0.858 0.108 0.808 0.084 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.4477 0.858 0.108 0.808 0.084 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 4 0.6731 0.610 0.248 0.000 0.148 0.604
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.6515 0.848 0.624 0.128 0.000 0.248
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.5866 0.902 0.624 0.052 0.000 0.324
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0188 0.858 0.004 0.996 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.0707 0.728 0.020 0.000 0.000 0.980
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.4756 0.790 0.176 0.772 0.052 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.5773 0.894 0.620 0.044 0.000 0.336
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.5649 0.886 0.620 0.036 0.000 0.344
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0707 0.728 0.020 0.000 0.000 0.980
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 4 0.7369 0.506 0.248 0.000 0.228 0.524
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.4477 0.858 0.108 0.808 0.084 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.5866 0.902 0.624 0.052 0.000 0.324
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0707 0.728 0.020 0.000 0.000 0.980
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0336 0.735 0.008 0.000 0.000 0.992
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.4837 0.438 0.648 0.348 0.000 0.004
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.2821 0.916 0.020 0.004 0.900 0.076
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.5866 0.902 0.624 0.052 0.000 0.324
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.6823 0.790 0.604 0.196 0.000 0.200
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0707 0.728 0.020 0.000 0.000 0.980
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.4581 0.856 0.120 0.800 0.080 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.6265 0.634 0.220 0.000 0.124 0.656
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.1909 0.890 0.008 0.004 0.940 0.048
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.4160 0.798 0.804 0.048 0.000 0.124 0.024
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.0162 0.907 0.000 0.000 0.996 0.004 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.909 0.000 0.000 1.000 0.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.1270 0.757 0.052 0.948 0.000 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.1788 0.872 0.000 0.008 0.932 0.004 0.056
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.1059 0.929 0.968 0.004 0.000 0.020 0.008
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0000 0.909 0.000 0.000 1.000 0.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.5569 0.760 0.068 0.616 0.000 0.304 0.012
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.1270 0.757 0.052 0.948 0.000 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.7274 0.635 0.152 0.492 0.000 0.292 0.064
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.1220 0.989 0.004 0.004 0.020 0.008 0.964
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.1041 0.894 0.000 0.000 0.964 0.004 0.032
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0932 0.990 0.004 0.000 0.020 0.004 0.972
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.5755 0.771 0.052 0.000 0.416 0.516 0.016
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.5311 0.750 0.036 0.584 0.000 0.368 0.012
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.5389 0.739 0.036 0.552 0.000 0.400 0.012
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.6639 0.967 0.168 0.000 0.300 0.516 0.016
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.5311 0.750 0.036 0.584 0.000 0.368 0.012
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.2589 0.941 0.004 0.004 0.020 0.076 0.896
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.6573 0.960 0.152 0.000 0.316 0.516 0.016
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0703 0.754 0.024 0.976 0.000 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.6639 0.967 0.168 0.000 0.300 0.516 0.016
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.6784 0.965 0.168 0.004 0.300 0.512 0.016
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.6639 0.967 0.168 0.000 0.300 0.516 0.016
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.0932 0.990 0.004 0.000 0.020 0.004 0.972
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.5676 0.718 0.720 0.088 0.008 0.128 0.056
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.5311 0.750 0.036 0.584 0.000 0.368 0.012
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.6573 0.960 0.152 0.000 0.316 0.516 0.016
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.0290 0.905 0.000 0.000 0.992 0.008 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.6784 0.965 0.168 0.004 0.300 0.512 0.016
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0932 0.990 0.004 0.000 0.020 0.004 0.972
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.6753 0.964 0.160 0.004 0.308 0.512 0.016
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.1788 0.872 0.000 0.008 0.932 0.004 0.056
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.5311 0.750 0.036 0.584 0.000 0.368 0.012
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.1270 0.757 0.052 0.948 0.000 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.6573 0.960 0.152 0.000 0.316 0.516 0.016
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.6722 0.964 0.168 0.000 0.300 0.512 0.020
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.4551 -0.204 0.016 0.000 0.616 0.368 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.1443 0.885 0.000 0.004 0.948 0.004 0.044
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.1093 0.987 0.004 0.004 0.020 0.004 0.968
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.4034 0.792 0.812 0.060 0.000 0.112 0.016
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.6627 0.448 0.000 0.608 0.208 0.080 0.104
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.1410 0.849 0.000 0.000 0.940 0.060 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.1717 0.755 0.052 0.936 0.000 0.008 0.004
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0162 0.907 0.000 0.000 0.996 0.004 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0000 0.909 0.000 0.000 1.000 0.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.0000 0.909 0.000 0.000 1.000 0.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.1270 0.757 0.052 0.948 0.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.6573 0.960 0.152 0.000 0.316 0.516 0.016
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.6555 0.956 0.148 0.000 0.320 0.516 0.016
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.4723 0.635 0.000 0.032 0.772 0.076 0.120
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0162 0.907 0.000 0.000 0.996 0.004 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.6792 0.687 0.160 0.472 0.000 0.348 0.020
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.1270 0.757 0.052 0.948 0.000 0.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0000 0.909 0.000 0.000 1.000 0.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.1443 0.885 0.000 0.004 0.948 0.004 0.044
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.0404 0.902 0.000 0.000 0.988 0.012 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.6639 0.967 0.168 0.000 0.300 0.516 0.016
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.6639 0.967 0.168 0.000 0.300 0.516 0.016
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.5389 0.738 0.056 0.000 0.436 0.508 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.1410 0.880 0.940 0.000 0.000 0.060 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.1270 0.757 0.052 0.948 0.000 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.1270 0.757 0.052 0.948 0.000 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.4368 0.677 0.184 0.764 0.000 0.036 0.016
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.1270 0.757 0.052 0.948 0.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0566 0.933 0.984 0.012 0.000 0.000 0.004
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.6653 0.656 0.012 0.540 0.024 0.324 0.100
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0290 0.935 0.992 0.000 0.000 0.008 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.6555 0.956 0.148 0.000 0.320 0.516 0.016
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.6639 0.967 0.168 0.000 0.300 0.516 0.016
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.2244 0.894 0.920 0.040 0.000 0.024 0.016
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.6390 0.948 0.148 0.000 0.328 0.516 0.008
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.6639 0.967 0.168 0.000 0.300 0.516 0.016
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.6799 0.684 0.160 0.468 0.000 0.352 0.020
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.1270 0.757 0.052 0.948 0.000 0.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.3919 0.803 0.820 0.056 0.000 0.108 0.016
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.6784 0.965 0.168 0.004 0.300 0.512 0.016
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0703 0.754 0.024 0.976 0.000 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.5333 0.747 0.036 0.576 0.000 0.376 0.012
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.2170 0.801 0.000 0.004 0.904 0.088 0.004
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0324 0.937 0.992 0.000 0.000 0.004 0.004
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.5311 0.750 0.036 0.584 0.000 0.368 0.012
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.1357 0.892 0.948 0.000 0.000 0.048 0.004
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.0932 0.990 0.004 0.000 0.020 0.004 0.972
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.2605 0.877 0.900 0.060 0.000 0.024 0.016
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.6219 0.873 0.144 0.000 0.384 0.472 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.5311 0.750 0.036 0.584 0.000 0.368 0.012
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.5311 0.750 0.036 0.584 0.000 0.368 0.012
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.0000 0.909 0.000 0.000 1.000 0.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.1571 0.900 0.936 0.060 0.000 0.000 0.004
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0703 0.754 0.024 0.976 0.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.6639 0.967 0.168 0.000 0.300 0.516 0.016
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.5934 0.674 0.156 0.648 0.000 0.176 0.020
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0324 0.937 0.992 0.000 0.000 0.004 0.004
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0451 0.934 0.988 0.000 0.000 0.008 0.004
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.6639 0.967 0.168 0.000 0.300 0.516 0.016
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.1041 0.894 0.000 0.000 0.964 0.004 0.032
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.5311 0.750 0.036 0.584 0.000 0.368 0.012
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.6639 0.967 0.168 0.000 0.300 0.516 0.016
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.6591 0.963 0.156 0.000 0.312 0.516 0.016
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.3446 0.819 0.844 0.112 0.000 0.028 0.016
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.0994 0.987 0.004 0.004 0.016 0.004 0.972
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0162 0.939 0.996 0.004 0.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.3109 0.772 0.800 0.200 0.000 0.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.6639 0.967 0.168 0.000 0.300 0.516 0.016
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.5372 0.742 0.036 0.560 0.000 0.392 0.012
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.3123 0.619 0.000 0.004 0.812 0.184 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0932 0.987 0.004 0.000 0.020 0.004 0.972
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.1267 0.937 0.940 0.000 0.000 0.060 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.3700 0.809 0.820 0.092 0.064 0.000 0.008 0.016
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.2488 0.922 0.000 0.008 0.864 0.124 0.004 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.1219 0.935 0.948 0.004 0.000 0.048 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.2234 0.924 0.000 0.004 0.872 0.124 0.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.4310 0.792 0.024 0.580 0.000 0.000 0.000 0.396
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.3554 0.899 0.004 0.052 0.820 0.112 0.012 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.1267 0.937 0.940 0.000 0.000 0.060 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.2228 0.922 0.908 0.004 0.024 0.056 0.008 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.2234 0.924 0.000 0.004 0.872 0.124 0.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 6 0.4400 0.640 0.044 0.124 0.052 0.000 0.008 0.772
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.4310 0.792 0.024 0.580 0.000 0.000 0.000 0.396
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.1267 0.937 0.940 0.000 0.000 0.060 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.7199 -0.181 0.132 0.400 0.104 0.000 0.012 0.352
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.0696 0.966 0.004 0.000 0.004 0.008 0.980 0.004
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.2234 0.924 0.000 0.004 0.872 0.124 0.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0405 0.967 0.000 0.000 0.004 0.008 0.988 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.1124 0.920 0.000 0.008 0.036 0.956 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 6 0.0551 0.847 0.004 0.000 0.008 0.000 0.004 0.984
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 6 0.2442 0.798 0.008 0.048 0.036 0.000 0.008 0.900
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0458 0.947 0.000 0.016 0.000 0.984 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 6 0.0405 0.846 0.004 0.000 0.008 0.000 0.000 0.988
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.4698 0.736 0.016 0.232 0.048 0.008 0.696 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.1370 0.941 0.012 0.036 0.000 0.948 0.004 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.4056 0.766 0.004 0.576 0.004 0.000 0.000 0.416
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0260 0.948 0.000 0.008 0.000 0.992 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0820 0.942 0.012 0.016 0.000 0.972 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0547 0.948 0.000 0.020 0.000 0.980 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.0520 0.967 0.000 0.000 0.008 0.008 0.984 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.5734 0.339 0.508 0.368 0.108 0.000 0.012 0.004
#> F798E986-79BB-48FD-8514-95571EDB594B 6 0.0405 0.846 0.004 0.008 0.000 0.000 0.000 0.988
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.1267 0.937 0.940 0.000 0.000 0.060 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.1586 0.939 0.012 0.040 0.004 0.940 0.004 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.3551 0.886 0.000 0.048 0.784 0.168 0.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0820 0.942 0.012 0.016 0.000 0.972 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0405 0.967 0.000 0.000 0.004 0.008 0.988 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.0777 0.945 0.000 0.024 0.004 0.972 0.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.3554 0.899 0.004 0.052 0.820 0.112 0.012 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 6 0.0405 0.846 0.004 0.000 0.008 0.000 0.000 0.988
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.1267 0.937 0.940 0.000 0.000 0.060 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.4310 0.792 0.024 0.580 0.000 0.000 0.000 0.396
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.1586 0.939 0.012 0.040 0.004 0.940 0.004 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.1644 0.936 0.012 0.052 0.000 0.932 0.004 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.4018 0.389 0.000 0.020 0.324 0.656 0.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.2636 0.920 0.000 0.016 0.860 0.120 0.004 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0810 0.966 0.004 0.000 0.008 0.008 0.976 0.004
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.3648 0.820 0.832 0.064 0.064 0.000 0.008 0.032
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.6761 0.234 0.016 0.568 0.208 0.024 0.040 0.144
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.1267 0.937 0.940 0.000 0.000 0.060 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.3073 0.867 0.000 0.008 0.788 0.204 0.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.4292 0.785 0.024 0.588 0.000 0.000 0.000 0.388
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.2389 0.923 0.000 0.008 0.864 0.128 0.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.2234 0.924 0.000 0.004 0.872 0.124 0.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.1333 0.934 0.944 0.008 0.000 0.048 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.2346 0.924 0.000 0.008 0.868 0.124 0.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.1528 0.933 0.936 0.016 0.000 0.048 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.4310 0.792 0.024 0.580 0.000 0.000 0.000 0.396
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.1586 0.939 0.012 0.040 0.004 0.940 0.004 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0603 0.948 0.000 0.016 0.004 0.980 0.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.5320 0.562 0.016 0.256 0.648 0.044 0.036 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.2346 0.924 0.000 0.008 0.868 0.124 0.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 6 0.4753 0.608 0.136 0.048 0.064 0.000 0.008 0.744
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.4310 0.792 0.024 0.580 0.000 0.000 0.000 0.396
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.2234 0.924 0.000 0.004 0.872 0.124 0.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.2926 0.912 0.000 0.024 0.852 0.112 0.012 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.2841 0.903 0.000 0.012 0.824 0.164 0.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0458 0.947 0.000 0.016 0.000 0.984 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0363 0.948 0.000 0.012 0.000 0.988 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.2476 0.864 0.000 0.024 0.092 0.880 0.004 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.1983 0.917 0.908 0.020 0.000 0.072 0.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.4310 0.792 0.024 0.580 0.000 0.000 0.000 0.396
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.4310 0.792 0.024 0.580 0.000 0.000 0.000 0.396
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.6584 0.447 0.184 0.468 0.040 0.000 0.004 0.304
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.4310 0.792 0.024 0.580 0.000 0.000 0.000 0.396
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.1267 0.937 0.940 0.000 0.000 0.060 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 6 0.5842 0.308 0.016 0.328 0.056 0.000 0.040 0.560
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.1327 0.935 0.936 0.000 0.000 0.064 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0806 0.946 0.000 0.020 0.008 0.972 0.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.1442 0.941 0.012 0.040 0.000 0.944 0.004 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.2112 0.898 0.916 0.036 0.028 0.020 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.1699 0.938 0.012 0.040 0.008 0.936 0.004 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0713 0.947 0.000 0.028 0.000 0.972 0.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.1267 0.937 0.940 0.000 0.000 0.060 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 6 0.4909 0.596 0.140 0.056 0.064 0.000 0.008 0.732
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.4371 0.789 0.028 0.580 0.000 0.000 0.000 0.392
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.3472 0.825 0.840 0.068 0.064 0.000 0.008 0.020
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.1267 0.937 0.940 0.000 0.000 0.060 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0458 0.947 0.000 0.016 0.000 0.984 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.4056 0.766 0.004 0.576 0.004 0.000 0.000 0.416
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 6 0.0551 0.847 0.004 0.004 0.008 0.000 0.000 0.984
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.4312 0.612 0.000 0.028 0.604 0.368 0.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.1995 0.926 0.912 0.036 0.000 0.052 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 6 0.0146 0.847 0.004 0.000 0.000 0.000 0.000 0.996
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.2507 0.906 0.884 0.040 0.000 0.072 0.004 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.0405 0.967 0.000 0.000 0.004 0.008 0.988 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.1655 0.906 0.936 0.044 0.004 0.012 0.000 0.004
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.1918 0.872 0.000 0.008 0.088 0.904 0.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.1333 0.934 0.944 0.008 0.000 0.048 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 6 0.0146 0.847 0.004 0.000 0.000 0.000 0.000 0.996
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 6 0.0146 0.847 0.004 0.000 0.000 0.000 0.000 0.996
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.2234 0.924 0.000 0.004 0.872 0.124 0.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.1434 0.932 0.940 0.012 0.000 0.048 0.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.1267 0.937 0.940 0.000 0.000 0.060 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.4056 0.766 0.004 0.576 0.004 0.000 0.000 0.416
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.1370 0.941 0.012 0.036 0.000 0.948 0.004 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.6762 0.162 0.112 0.520 0.108 0.000 0.008 0.252
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.1995 0.926 0.912 0.036 0.000 0.052 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.2209 0.922 0.904 0.040 0.000 0.052 0.004 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0820 0.942 0.012 0.016 0.000 0.972 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.2234 0.924 0.000 0.004 0.872 0.124 0.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 6 0.0405 0.846 0.004 0.000 0.008 0.000 0.000 0.988
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.1267 0.937 0.940 0.000 0.000 0.060 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0820 0.942 0.012 0.016 0.000 0.972 0.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0692 0.947 0.000 0.020 0.004 0.976 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.2617 0.860 0.880 0.080 0.032 0.000 0.004 0.004
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.0405 0.964 0.000 0.000 0.000 0.008 0.988 0.004
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.1434 0.934 0.940 0.012 0.000 0.048 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.3315 0.747 0.780 0.200 0.000 0.020 0.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0363 0.948 0.000 0.012 0.000 0.988 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 6 0.2307 0.804 0.008 0.040 0.036 0.000 0.008 0.908
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.4229 0.459 0.000 0.016 0.548 0.436 0.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0665 0.966 0.004 0.000 0.008 0.008 0.980 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", "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 17548 rows and 122 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 0.520 0.700 0.873 0.5019 0.497 0.497
#> 3 3 0.622 0.769 0.872 0.3111 0.733 0.518
#> 4 4 0.871 0.870 0.944 0.1183 0.879 0.667
#> 5 5 0.967 0.927 0.961 0.0785 0.908 0.671
#> 6 6 0.915 0.877 0.926 0.0423 0.952 0.774
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 2 0.9754 0.4800 0.408 0.592
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.0000 0.8205 0.000 1.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.8390 1.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 2 0.9754 0.4800 0.408 0.592
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 1 0.0000 0.8390 1.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.8205 0.000 1.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 1 0.9754 0.4310 0.592 0.408
#> 45EAD449-C59A-463E-880A-C375CDD039BA 2 0.9754 0.4800 0.408 0.592
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 2 0.9754 0.4800 0.408 0.592
#> 806616FE-1855-4284-9265-42842104CB21 1 0.0000 0.8390 1.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.8205 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.8205 0.000 1.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 2 0.9754 0.4800 0.408 0.592
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.8205 0.000 1.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 1 0.9754 0.4310 0.592 0.408
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 1 0.7219 0.6762 0.800 0.200
#> 853120F0-857B-4108-9EC8-727189630C5F 1 0.9754 0.4310 0.592 0.408
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.8390 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.8205 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.8205 0.000 1.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.8390 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.8205 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 1 0.9754 0.4310 0.592 0.408
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.8390 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.8205 0.000 1.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.8390 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.8390 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.8390 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.9754 0.4310 0.592 0.408
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.0376 0.8172 0.004 0.996
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.8205 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 2 0.9754 0.4800 0.408 0.592
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.8390 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.0000 0.8390 1.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.8390 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 1 0.9754 0.4310 0.592 0.408
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0000 0.8390 1.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.7219 0.6762 0.800 0.200
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.8205 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 2 0.9754 0.4800 0.408 0.592
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.8205 0.000 1.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.8390 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.8390 1.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.8390 1.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 1 0.9710 0.4418 0.600 0.400
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 1 0.9754 0.4310 0.592 0.408
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.8205 0.000 1.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.9552 0.1488 0.376 0.624
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 2 0.9754 0.4800 0.408 0.592
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.0000 0.8390 1.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.8205 0.000 1.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.0000 0.8390 1.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.5946 0.7290 0.856 0.144
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 2 0.9754 0.4800 0.408 0.592
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.0000 0.8390 1.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 2 0.9754 0.4800 0.408 0.592
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.8205 0.000 1.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.8390 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.8390 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 1 0.9754 0.4310 0.592 0.408
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.0000 0.8390 1.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.8205 0.000 1.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.8205 0.000 1.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.0000 0.8390 1.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 1 0.9754 0.4310 0.592 0.408
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.0000 0.8390 1.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.8390 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.8390 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0000 0.8390 1.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.5059 0.7242 0.888 0.112
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.8205 0.000 1.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.8205 0.000 1.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.8205 0.000 1.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.8205 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 2 0.9710 0.4887 0.400 0.600
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.5178 0.6972 0.116 0.884
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.9710 0.0802 0.600 0.400
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.8390 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.8390 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.0000 0.8205 0.000 1.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.8390 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.8390 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 2 0.9754 0.4800 0.408 0.592
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.8205 0.000 1.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0000 0.8205 0.000 1.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0000 0.8205 0.000 1.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 2 0.9754 0.4800 0.408 0.592
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.8390 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.8205 0.000 1.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.8205 0.000 1.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.0000 0.8390 1.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.9710 0.0802 0.600 0.400
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.8205 0.000 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.6048 0.6756 0.852 0.148
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.9754 0.4310 0.592 0.408
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.0000 0.8205 0.000 1.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.8390 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 2 0.9754 0.4800 0.408 0.592
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.8205 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.8205 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0000 0.8390 1.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 2 0.7219 0.6831 0.200 0.800
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 2 0.9754 0.4800 0.408 0.592
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.8205 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.8390 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.8205 0.000 1.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.9850 -0.0284 0.572 0.428
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.9427 0.2134 0.640 0.360
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.8390 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 1 0.9710 0.4418 0.600 0.400
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.8205 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 2 0.9754 0.4800 0.408 0.592
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.8390 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.8390 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0000 0.8205 0.000 1.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0000 0.8390 1.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 2 0.9754 0.4800 0.408 0.592
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.7219 0.6831 0.200 0.800
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.8390 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.8205 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.0000 0.8390 1.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 1 0.9754 0.4310 0.592 0.408
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.9230 1.000 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.0592 0.8370 0.012 0.988 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.0000 0.7953 0.000 0.000 1.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.9230 1.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.7953 0.000 0.000 1.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.3879 0.7722 0.152 0.848 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0000 0.7953 0.000 0.000 1.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.9230 1.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0000 0.9230 1.000 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0000 0.7953 0.000 0.000 1.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.3879 0.7722 0.152 0.848 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.3879 0.7722 0.152 0.848 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.9230 1.000 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.8394 0.000 1.000 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 2 0.6286 0.3628 0.000 0.536 0.464
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0000 0.7953 0.000 0.000 1.000
#> 853120F0-857B-4108-9EC8-727189630C5F 2 0.6280 0.3720 0.000 0.540 0.460
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 3 0.0000 0.7953 0.000 0.000 1.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0424 0.8402 0.008 0.992 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0424 0.8402 0.008 0.992 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 3 0.5678 0.7540 0.316 0.000 0.684
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0237 0.8403 0.004 0.996 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 2 0.6280 0.3720 0.000 0.540 0.460
#> F5A814F6-E824-4DB2-8497-4B99E151D450 3 0.5678 0.7540 0.316 0.000 0.684
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0424 0.8402 0.008 0.992 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 3 0.5678 0.7540 0.316 0.000 0.684
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 3 0.5678 0.7540 0.316 0.000 0.684
#> 4496EE84-2C36-413B-A328-A5B598A6C387 3 0.5678 0.7540 0.316 0.000 0.684
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 2 0.6280 0.3720 0.000 0.540 0.460
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.0592 0.8370 0.012 0.988 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.8394 0.000 1.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.9230 1.000 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 3 0.5678 0.7540 0.316 0.000 0.684
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.0000 0.7953 0.000 0.000 1.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 3 0.5678 0.7540 0.316 0.000 0.684
#> AA403EC3-FD44-4247-B06D-AEF415391E46 2 0.6280 0.3720 0.000 0.540 0.460
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 3 0.0000 0.7953 0.000 0.000 1.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0000 0.7953 0.000 0.000 1.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0237 0.8403 0.004 0.996 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.9230 1.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.3879 0.7722 0.152 0.848 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 3 0.5678 0.7540 0.316 0.000 0.684
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.5678 0.7540 0.316 0.000 0.684
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.0000 0.7953 0.000 0.000 1.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0000 0.7953 0.000 0.000 1.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.6274 0.3799 0.000 0.544 0.456
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.5058 0.6566 0.244 0.756 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0424 0.8367 0.000 0.992 0.008
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.9230 1.000 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.0000 0.7953 0.000 0.000 1.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.3879 0.7722 0.152 0.848 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0000 0.7953 0.000 0.000 1.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0000 0.7953 0.000 0.000 1.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.9230 1.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.0000 0.7953 0.000 0.000 1.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.9230 1.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.3879 0.7722 0.152 0.848 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 3 0.5678 0.7540 0.316 0.000 0.684
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 3 0.5678 0.7540 0.316 0.000 0.684
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.5988 0.0672 0.000 0.368 0.632
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0000 0.7953 0.000 0.000 1.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0424 0.8402 0.008 0.992 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.3879 0.7722 0.152 0.848 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0000 0.7953 0.000 0.000 1.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0000 0.7953 0.000 0.000 1.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.0000 0.7953 0.000 0.000 1.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 3 0.5678 0.7540 0.316 0.000 0.684
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 3 0.5678 0.7540 0.316 0.000 0.684
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.4605 0.7707 0.204 0.000 0.796
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0424 0.9134 0.992 0.000 0.008
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.3879 0.7722 0.152 0.848 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.3879 0.7722 0.152 0.848 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.4346 0.7368 0.184 0.816 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.3879 0.7722 0.152 0.848 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.9230 1.000 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.8394 0.000 1.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.9230 1.000 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 3 0.5678 0.7540 0.316 0.000 0.684
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 3 0.5678 0.7540 0.316 0.000 0.684
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.5678 0.4735 0.684 0.316 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 3 0.5678 0.7540 0.316 0.000 0.684
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 3 0.5678 0.7540 0.316 0.000 0.684
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.9230 1.000 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0424 0.8402 0.008 0.992 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.3879 0.7722 0.152 0.848 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.6252 0.1710 0.556 0.444 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.9230 1.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 3 0.5678 0.7540 0.316 0.000 0.684
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0237 0.8403 0.004 0.996 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0424 0.8402 0.008 0.992 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.0000 0.7953 0.000 0.000 1.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.9230 1.000 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.8394 0.000 1.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0424 0.9134 0.992 0.000 0.008
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 2 0.6280 0.3720 0.000 0.540 0.460
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.5678 0.4735 0.684 0.316 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 3 0.5678 0.7540 0.316 0.000 0.684
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.9230 1.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.8394 0.000 1.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.8394 0.000 1.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.0000 0.7953 0.000 0.000 1.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.2448 0.8562 0.924 0.076 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.9230 1.000 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0424 0.8402 0.008 0.992 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 3 0.5678 0.7540 0.316 0.000 0.684
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.8394 0.000 1.000 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.9230 1.000 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.9230 1.000 0.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 3 0.5678 0.7540 0.316 0.000 0.684
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0000 0.7953 0.000 0.000 1.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0237 0.8403 0.004 0.996 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.9230 1.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 3 0.5678 0.7540 0.316 0.000 0.684
#> F205F9FC-F2D5-4164-9A40-1279647F900B 3 0.5678 0.7540 0.316 0.000 0.684
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.5926 0.3950 0.644 0.356 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.3941 0.6320 0.000 0.156 0.844
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.9230 1.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.2711 0.8478 0.912 0.088 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 3 0.5678 0.7540 0.316 0.000 0.684
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0237 0.8403 0.004 0.996 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.0000 0.7953 0.000 0.000 1.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.5706 0.5940 0.000 0.680 0.320
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.4008 0.701 0.000 0.756 0.244 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 4 0.3311 0.767 0.000 0.000 0.172 0.828
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 4 0.4585 0.526 0.000 0.000 0.332 0.668
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.3610 0.682 0.000 0.000 0.800 0.200
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 4 0.4477 0.565 0.000 0.000 0.312 0.688
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.4679 0.509 0.000 0.648 0.352 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.0000 0.819 0.000 0.000 1.000 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.4955 0.177 0.000 0.000 0.556 0.444
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.0000 0.819 0.000 0.000 1.000 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.923 0.000 0.000 0.000 1.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.0000 0.819 0.000 0.000 1.000 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.0000 0.819 0.000 0.000 1.000 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 3 0.4585 0.368 0.000 0.332 0.668 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.3688 0.722 0.000 0.000 0.208 0.792
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.0000 0.819 0.000 0.000 1.000 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.0336 0.919 0.000 0.000 0.008 0.992
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.3726 0.669 0.000 0.000 0.788 0.212
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.0524 0.919 0.004 0.000 0.008 0.988
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.0000 0.923 0.000 0.000 0.000 1.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.4697 0.421 0.000 0.000 0.644 0.356
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.0000 0.819 0.000 0.000 1.000 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.2081 0.883 0.084 0.916 0.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 3 0.3444 0.639 0.000 0.184 0.816 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.0000 0.923 0.000 0.000 0.000 1.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.0000 0.923 0.000 0.000 0.000 1.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 4 0.4605 0.518 0.000 0.000 0.336 0.664
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.4522 0.550 0.000 0.000 0.320 0.680
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.0000 0.819 0.000 0.000 1.000 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.1557 0.883 0.000 0.000 0.056 0.944
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 4 0.4454 0.572 0.000 0.000 0.308 0.692
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.3907 0.644 0.000 0.000 0.768 0.232
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.0336 0.919 0.000 0.000 0.008 0.992
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.0000 0.923 0.000 0.000 0.000 1.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 3 0.4543 0.388 0.000 0.324 0.676 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.0188 0.987 0.996 0.004 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.2589 0.850 0.116 0.884 0.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.3024 0.794 0.000 0.000 0.148 0.852
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.0000 0.819 0.000 0.000 1.000 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.0188 0.987 0.996 0.004 0.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0000 0.923 0.000 0.000 0.000 1.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 4 0.4605 0.518 0.000 0.000 0.336 0.664
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.3486 0.776 0.000 0.812 0.188 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.4967 0.149 0.000 0.000 0.548 0.452
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.4008 0.691 0.244 0.756 0.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.0000 0.819 0.000 0.000 1.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.3266 0.787 0.832 0.168 0.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0188 0.925 0.004 0.000 0.000 0.996
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.0000 0.923 0.000 0.000 0.000 1.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.0000 0.819 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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 5 0.2144 0.8832 0.000 0.068 0.020 0.000 0.912
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.0963 0.9274 0.000 0.000 0.964 0.036 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0703 0.9282 0.000 0.000 0.976 0.024 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0451 0.9527 0.000 0.988 0.004 0.000 0.008
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0807 0.9209 0.000 0.000 0.976 0.012 0.012
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0510 0.9763 0.984 0.000 0.016 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0794 0.9287 0.000 0.000 0.972 0.028 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0671 0.9531 0.000 0.980 0.004 0.000 0.016
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0451 0.9527 0.000 0.988 0.004 0.000 0.008
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 5 0.0992 0.9317 0.000 0.024 0.008 0.000 0.968
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.0703 0.9550 0.000 0.000 0.024 0.000 0.976
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0771 0.9265 0.000 0.000 0.976 0.020 0.004
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0703 0.9550 0.000 0.000 0.024 0.000 0.976
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0510 0.9594 0.000 0.000 0.016 0.984 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.1216 0.9525 0.000 0.960 0.020 0.000 0.020
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.1216 0.9525 0.000 0.960 0.020 0.000 0.020
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.1216 0.9525 0.000 0.960 0.020 0.000 0.020
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.0703 0.9550 0.000 0.000 0.024 0.000 0.976
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0451 0.9527 0.000 0.988 0.004 0.000 0.008
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.0703 0.9550 0.000 0.000 0.024 0.000 0.976
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 5 0.0693 0.9371 0.000 0.012 0.008 0.000 0.980
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.1216 0.9525 0.000 0.960 0.020 0.000 0.020
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.1043 0.9260 0.000 0.000 0.960 0.040 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0703 0.9550 0.000 0.000 0.024 0.000 0.976
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.0671 0.9574 0.000 0.000 0.016 0.980 0.004
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0807 0.9209 0.000 0.000 0.976 0.012 0.012
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.1216 0.9525 0.000 0.960 0.020 0.000 0.020
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0451 0.9527 0.000 0.988 0.004 0.000 0.008
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.4192 0.3404 0.000 0.000 0.596 0.404 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0807 0.9209 0.000 0.000 0.976 0.012 0.012
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0703 0.9550 0.000 0.000 0.024 0.000 0.976
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.2986 0.8805 0.084 0.876 0.020 0.000 0.020
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 5 0.0992 0.9395 0.000 0.024 0.008 0.000 0.968
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.1197 0.9224 0.000 0.000 0.952 0.048 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0451 0.9527 0.000 0.988 0.004 0.000 0.008
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.1197 0.9224 0.000 0.000 0.952 0.048 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0703 0.9282 0.000 0.000 0.976 0.024 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.0880 0.9283 0.000 0.000 0.968 0.032 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0451 0.9527 0.000 0.988 0.004 0.000 0.008
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.4306 -0.0172 0.000 0.000 0.508 0.000 0.492
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.1197 0.9224 0.000 0.000 0.952 0.048 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.1216 0.9525 0.000 0.960 0.020 0.000 0.020
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0451 0.9527 0.000 0.988 0.004 0.000 0.008
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0794 0.9287 0.000 0.000 0.972 0.028 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0807 0.9209 0.000 0.000 0.976 0.012 0.012
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.1197 0.9224 0.000 0.000 0.952 0.048 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.3857 0.5254 0.000 0.000 0.312 0.688 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0451 0.9527 0.000 0.988 0.004 0.000 0.008
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0451 0.9527 0.000 0.988 0.004 0.000 0.008
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0162 0.9529 0.000 0.996 0.004 0.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0451 0.9527 0.000 0.988 0.004 0.000 0.008
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 5 0.3934 0.5898 0.000 0.276 0.008 0.000 0.716
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.1216 0.9525 0.000 0.960 0.020 0.000 0.020
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0451 0.9527 0.000 0.988 0.004 0.000 0.008
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.3511 0.8349 0.124 0.836 0.020 0.000 0.020
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0451 0.9527 0.000 0.988 0.004 0.000 0.008
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.1216 0.9525 0.000 0.960 0.020 0.000 0.020
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.2074 0.8750 0.000 0.000 0.896 0.104 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.1216 0.9525 0.000 0.960 0.020 0.000 0.020
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.0703 0.9550 0.000 0.000 0.024 0.000 0.976
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.3774 0.5606 0.000 0.000 0.296 0.704 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.1216 0.9525 0.000 0.960 0.020 0.000 0.020
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.1216 0.9525 0.000 0.960 0.020 0.000 0.020
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.0703 0.9282 0.000 0.000 0.976 0.024 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0404 0.9801 0.988 0.012 0.000 0.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0451 0.9527 0.000 0.988 0.004 0.000 0.008
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.3210 0.7396 0.000 0.788 0.000 0.000 0.212
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0771 0.9265 0.000 0.000 0.976 0.020 0.004
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.1216 0.9525 0.000 0.960 0.020 0.000 0.020
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.9903 1.000 0.000 0.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.3992 0.6249 0.280 0.712 0.004 0.000 0.004
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.0703 0.9550 0.000 0.000 0.024 0.000 0.976
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0162 0.9870 0.996 0.000 0.004 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.3196 0.7537 0.804 0.192 0.004 0.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.9731 0.000 0.000 0.000 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.1216 0.9525 0.000 0.960 0.020 0.000 0.020
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.2891 0.7992 0.000 0.000 0.824 0.176 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0703 0.9550 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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.0551 0.772 0.000 0.984 0.004 0.000 0.008 0.004
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.0260 0.938 0.000 0.000 0.992 0.008 0.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0146 0.939 0.996 0.004 0.000 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0146 0.940 0.000 0.000 0.996 0.004 0.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 6 0.0000 0.912 0.000 0.000 0.000 0.000 0.000 1.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0146 0.940 0.000 0.000 0.996 0.004 0.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.3314 0.693 0.740 0.256 0.004 0.000 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0146 0.940 0.000 0.000 0.996 0.004 0.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.3672 0.664 0.000 0.632 0.000 0.000 0.000 0.368
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 6 0.0000 0.912 0.000 0.000 0.000 0.000 0.000 1.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.4234 0.202 0.000 0.608 0.004 0.000 0.372 0.016
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.0146 0.928 0.000 0.000 0.004 0.000 0.996 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0146 0.940 0.000 0.000 0.996 0.004 0.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0146 0.928 0.000 0.000 0.004 0.000 0.996 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0972 0.945 0.000 0.008 0.028 0.964 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.2823 0.904 0.000 0.796 0.000 0.000 0.000 0.204
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.2340 0.893 0.000 0.852 0.000 0.000 0.000 0.148
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0146 0.967 0.000 0.004 0.000 0.996 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.2823 0.904 0.000 0.796 0.000 0.000 0.000 0.204
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.0363 0.921 0.000 0.012 0.000 0.000 0.988 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.0146 0.967 0.000 0.004 0.000 0.996 0.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 6 0.0000 0.912 0.000 0.000 0.000 0.000 0.000 1.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0260 0.967 0.000 0.008 0.000 0.992 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0146 0.967 0.000 0.004 0.000 0.996 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0146 0.967 0.000 0.004 0.000 0.996 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.0146 0.928 0.000 0.000 0.004 0.000 0.996 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 5 0.3023 0.769 0.000 0.212 0.004 0.000 0.784 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.2730 0.903 0.000 0.808 0.000 0.000 0.000 0.192
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.0146 0.967 0.000 0.004 0.000 0.996 0.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.0436 0.936 0.000 0.004 0.988 0.004 0.004 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0146 0.967 0.000 0.004 0.000 0.996 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0146 0.928 0.000 0.000 0.004 0.000 0.996 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.0862 0.951 0.000 0.008 0.016 0.972 0.004 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0146 0.940 0.000 0.000 0.996 0.004 0.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.2823 0.904 0.000 0.796 0.000 0.000 0.000 0.204
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 6 0.0000 0.912 0.000 0.000 0.000 0.000 0.000 1.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.0146 0.967 0.000 0.004 0.000 0.996 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.0146 0.965 0.000 0.000 0.004 0.996 0.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.3961 0.165 0.000 0.004 0.556 0.440 0.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0146 0.940 0.000 0.000 0.996 0.004 0.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0146 0.928 0.000 0.000 0.004 0.000 0.996 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.1714 0.855 0.000 0.908 0.000 0.000 0.000 0.092
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 5 0.3743 0.651 0.000 0.024 0.000 0.000 0.724 0.252
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.0405 0.936 0.000 0.004 0.988 0.008 0.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 6 0.0000 0.912 0.000 0.000 0.000 0.000 0.000 1.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0260 0.938 0.000 0.000 0.992 0.008 0.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0146 0.940 0.000 0.000 0.996 0.004 0.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.1327 0.931 0.936 0.064 0.000 0.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.0146 0.940 0.000 0.000 0.996 0.004 0.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.1141 0.933 0.948 0.052 0.000 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 6 0.0000 0.912 0.000 0.000 0.000 0.000 0.000 1.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.0146 0.967 0.000 0.004 0.000 0.996 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0146 0.967 0.000 0.004 0.000 0.996 0.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.4273 0.306 0.000 0.024 0.596 0.000 0.380 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0260 0.938 0.000 0.000 0.992 0.008 0.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.2416 0.896 0.000 0.844 0.000 0.000 0.000 0.156
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 6 0.0000 0.912 0.000 0.000 0.000 0.000 0.000 1.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0146 0.940 0.000 0.000 0.996 0.004 0.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0146 0.940 0.000 0.000 0.996 0.004 0.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.0405 0.937 0.000 0.004 0.988 0.008 0.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0146 0.967 0.000 0.004 0.000 0.996 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0146 0.967 0.000 0.004 0.000 0.996 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.3619 0.534 0.000 0.004 0.316 0.680 0.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.1267 0.932 0.940 0.060 0.000 0.000 0.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 6 0.0000 0.912 0.000 0.000 0.000 0.000 0.000 1.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 6 0.0000 0.912 0.000 0.000 0.000 0.000 0.000 1.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 6 0.3052 0.687 0.004 0.216 0.000 0.000 0.000 0.780
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 6 0.0000 0.912 0.000 0.000 0.000 0.000 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 5 0.4752 0.596 0.000 0.184 0.000 0.000 0.676 0.140
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0146 0.967 0.000 0.004 0.000 0.996 0.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.0000 0.967 0.000 0.000 0.000 1.000 0.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.2300 0.882 0.856 0.144 0.000 0.000 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.0146 0.967 0.000 0.004 0.000 0.996 0.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0146 0.967 0.000 0.004 0.000 0.996 0.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.2260 0.889 0.000 0.860 0.000 0.000 0.000 0.140
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 6 0.0000 0.912 0.000 0.000 0.000 0.000 0.000 1.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0790 0.802 0.000 0.968 0.000 0.000 0.000 0.032
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0146 0.967 0.000 0.004 0.000 0.996 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 6 0.0000 0.912 0.000 0.000 0.000 0.000 0.000 1.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.2823 0.904 0.000 0.796 0.000 0.000 0.000 0.204
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.1615 0.882 0.000 0.004 0.928 0.064 0.004 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.1387 0.929 0.932 0.068 0.000 0.000 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.2823 0.904 0.000 0.796 0.000 0.000 0.000 0.204
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.1701 0.925 0.920 0.072 0.000 0.008 0.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.0146 0.928 0.000 0.000 0.004 0.000 0.996 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.2260 0.885 0.860 0.140 0.000 0.000 0.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.3619 0.534 0.000 0.004 0.316 0.680 0.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.1327 0.931 0.936 0.064 0.000 0.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.2823 0.904 0.000 0.796 0.000 0.000 0.000 0.204
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.2823 0.904 0.000 0.796 0.000 0.000 0.000 0.204
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.0146 0.940 0.000 0.000 0.996 0.004 0.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.3797 0.211 0.580 0.000 0.000 0.000 0.000 0.420
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 6 0.0000 0.912 0.000 0.000 0.000 0.000 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.0000 0.967 0.000 0.000 0.000 1.000 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 6 0.5177 0.443 0.000 0.152 0.000 0.000 0.236 0.612
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.1387 0.929 0.932 0.068 0.000 0.000 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.1444 0.928 0.928 0.072 0.000 0.000 0.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0146 0.967 0.000 0.004 0.000 0.996 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0146 0.940 0.000 0.000 0.996 0.004 0.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.2823 0.904 0.000 0.796 0.000 0.000 0.000 0.204
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0146 0.967 0.000 0.004 0.000 0.996 0.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0146 0.967 0.000 0.004 0.000 0.996 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 6 0.4815 0.583 0.188 0.144 0.000 0.000 0.000 0.668
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.0146 0.928 0.000 0.000 0.004 0.000 0.996 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.1910 0.909 0.892 0.108 0.000 0.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 6 0.3126 0.638 0.248 0.000 0.000 0.000 0.000 0.752
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0146 0.967 0.000 0.004 0.000 0.996 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.2562 0.900 0.000 0.828 0.000 0.000 0.000 0.172
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.2482 0.789 0.000 0.004 0.848 0.148 0.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0146 0.928 0.000 0.000 0.004 0.000 0.996 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", "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 17548 rows and 122 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 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 0.548 0.845 0.915 0.4892 0.496 0.496
#> 3 3 0.808 0.891 0.933 0.3205 0.835 0.674
#> 4 4 0.878 0.897 0.953 0.0874 0.910 0.757
#> 5 5 0.778 0.829 0.894 0.0502 0.936 0.799
#> 6 6 0.866 0.830 0.932 0.0818 0.926 0.730
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 2 0.7453 0.82983 0.212 0.788
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.7453 0.82983 0.212 0.788
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.92953 1.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 2 0.7453 0.82983 0.212 0.788
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 1 0.0000 0.92953 1.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.86811 0.000 1.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 1 0.7453 0.71882 0.788 0.212
#> 45EAD449-C59A-463E-880A-C375CDD039BA 2 0.7453 0.82983 0.212 0.788
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 2 0.7453 0.82983 0.212 0.788
#> 806616FE-1855-4284-9265-42842104CB21 1 0.0000 0.92953 1.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.86811 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.86811 0.000 1.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 2 0.7453 0.82983 0.212 0.788
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.1414 0.85791 0.020 0.980
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 1 0.1414 0.91702 0.980 0.020
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 1 0.0000 0.92953 1.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 1 0.2778 0.89663 0.952 0.048
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#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.86811 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.86811 0.000 1.000
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#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.86811 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 1 0.6712 0.74638 0.824 0.176
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#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.86811 0.000 1.000
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#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.6148 0.77123 0.848 0.152
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#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.7602 0.67298 0.780 0.220
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.9922 0.00958 0.552 0.448
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.86811 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 2 0.7453 0.82983 0.212 0.788
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#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.86811 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 2 0.7453 0.82983 0.212 0.788
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.86811 0.000 1.000
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#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.92953 1.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 1 0.5629 0.81172 0.868 0.132
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 1 0.8955 0.61020 0.688 0.312
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.1184 0.86616 0.016 0.984
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.8207 0.57066 0.256 0.744
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 2 0.7453 0.82983 0.212 0.788
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#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 2 0.8861 0.71196 0.304 0.696
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.92953 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 1 0.7056 0.74329 0.808 0.192
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.86811 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 2 0.7453 0.82983 0.212 0.788
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.92953 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.92953 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.6531 0.83987 0.168 0.832
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0376 0.92723 0.996 0.004
#> 12F54761-4F68-4181-8421-88EA858902FC 2 0.7453 0.82983 0.212 0.788
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.7453 0.82983 0.212 0.788
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.92953 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.86811 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.0000 0.92953 1.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 1 0.9998 0.14845 0.508 0.492
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.1860 0.9682 0.948 0.052 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.1860 0.9682 0.948 0.052 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.0000 0.9338 0.000 0.000 1.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.1860 0.9682 0.948 0.052 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.9338 0.000 0.000 1.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.9308 0.000 1.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0000 0.9338 0.000 0.000 1.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.1860 0.9682 0.948 0.052 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.1860 0.9682 0.948 0.052 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0000 0.9338 0.000 0.000 1.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.9308 0.000 1.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.9308 0.000 1.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.1860 0.9682 0.948 0.052 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.5958 0.6271 0.300 0.692 0.008
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.1860 0.9087 0.052 0.000 0.948
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0000 0.9338 0.000 0.000 1.000
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.2356 0.8993 0.072 0.000 0.928
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 3 0.0000 0.9338 0.000 0.000 1.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.3816 0.8497 0.148 0.852 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.3816 0.8497 0.148 0.852 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 3 0.1031 0.9231 0.024 0.000 0.976
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.9308 0.000 1.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.3752 0.8391 0.144 0.000 0.856
#> F5A814F6-E824-4DB2-8497-4B99E151D450 3 0.0000 0.9338 0.000 0.000 1.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.9308 0.000 1.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 3 0.0000 0.9338 0.000 0.000 1.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.4605 0.7212 0.796 0.000 0.204
#> 4496EE84-2C36-413B-A328-A5B598A6C387 3 0.1031 0.9231 0.024 0.000 0.976
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.4702 0.7661 0.212 0.000 0.788
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 3 0.7517 0.3491 0.364 0.048 0.588
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.9308 0.000 1.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.1860 0.9682 0.948 0.052 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 3 0.0000 0.9338 0.000 0.000 1.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.0000 0.9338 0.000 0.000 1.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.4796 0.6960 0.780 0.000 0.220
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.3482 0.8549 0.128 0.000 0.872
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 3 0.1031 0.9231 0.024 0.000 0.976
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0000 0.9338 0.000 0.000 1.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0237 0.9297 0.004 0.996 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.1860 0.9682 0.948 0.052 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.9308 0.000 1.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 3 0.0000 0.9338 0.000 0.000 1.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.5138 0.6797 0.252 0.000 0.748
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.0000 0.9338 0.000 0.000 1.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0000 0.9338 0.000 0.000 1.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.3116 0.8723 0.108 0.000 0.892
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.3551 0.8750 0.868 0.132 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.6981 0.7319 0.132 0.732 0.136
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.1860 0.9682 0.948 0.052 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.0000 0.9338 0.000 0.000 1.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.9308 0.000 1.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0000 0.9338 0.000 0.000 1.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0000 0.9338 0.000 0.000 1.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.1860 0.9682 0.948 0.052 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.0000 0.9338 0.000 0.000 1.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.1860 0.9682 0.948 0.052 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.9308 0.000 1.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 3 0.0000 0.9338 0.000 0.000 1.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 3 0.0000 0.9338 0.000 0.000 1.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.1031 0.9207 0.000 0.024 0.976
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0000 0.9338 0.000 0.000 1.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.3816 0.8497 0.148 0.852 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.9308 0.000 1.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0000 0.9338 0.000 0.000 1.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0000 0.9338 0.000 0.000 1.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.0000 0.9338 0.000 0.000 1.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 3 0.1031 0.9231 0.024 0.000 0.976
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 3 0.0892 0.9251 0.020 0.000 0.980
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.0000 0.9338 0.000 0.000 1.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.1989 0.9134 0.948 0.004 0.048
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.9308 0.000 1.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.9308 0.000 1.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.1964 0.9076 0.056 0.944 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.9308 0.000 1.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.1860 0.9682 0.948 0.052 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.3752 0.8538 0.144 0.856 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.1860 0.9682 0.948 0.052 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 3 0.0000 0.9338 0.000 0.000 1.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 3 0.5058 0.6880 0.244 0.000 0.756
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.1860 0.9682 0.948 0.052 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 3 0.0000 0.9338 0.000 0.000 1.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 3 0.0000 0.9338 0.000 0.000 1.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.1860 0.9682 0.948 0.052 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.4235 0.8212 0.176 0.824 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0892 0.9207 0.020 0.980 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.1860 0.9682 0.948 0.052 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.1860 0.9682 0.948 0.052 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 3 0.5706 0.5530 0.320 0.000 0.680
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.9308 0.000 1.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0892 0.9241 0.020 0.980 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.0000 0.9338 0.000 0.000 1.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.1860 0.9682 0.948 0.052 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.9308 0.000 1.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.2063 0.9183 0.948 0.008 0.044
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.2066 0.9051 0.060 0.000 0.940
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.1860 0.9682 0.948 0.052 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 3 0.5733 0.5504 0.324 0.000 0.676
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.1860 0.9682 0.948 0.052 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.9308 0.000 1.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.3412 0.8674 0.124 0.876 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.0000 0.9338 0.000 0.000 1.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.1860 0.9682 0.948 0.052 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.1860 0.9682 0.948 0.052 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.9308 0.000 1.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 3 0.0000 0.9338 0.000 0.000 1.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.5529 0.6419 0.296 0.704 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.1860 0.9682 0.948 0.052 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.1860 0.9682 0.948 0.052 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 3 0.3752 0.8205 0.144 0.000 0.856
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0000 0.9338 0.000 0.000 1.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.9308 0.000 1.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.1860 0.9682 0.948 0.052 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 3 0.4121 0.7962 0.168 0.000 0.832
#> F205F9FC-F2D5-4164-9A40-1279647F900B 3 0.0000 0.9338 0.000 0.000 1.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.1860 0.9682 0.948 0.052 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.5650 0.6222 0.312 0.000 0.688
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.1860 0.9682 0.948 0.052 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.4796 0.7763 0.780 0.220 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 3 0.0747 0.9270 0.016 0.000 0.984
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.3879 0.8461 0.152 0.848 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.0000 0.9338 0.000 0.000 1.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.9884 0.0178 0.364 0.260 0.376
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 4 0.0000 0.938 0.000 0.000 0.000 1.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 4 0.0188 0.939 0.000 0.000 0.004 0.996
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 4 0.0895 0.926 0.000 0.020 0.004 0.976
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 4 0.0188 0.939 0.000 0.000 0.004 0.996
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.4897 0.616 0.332 0.660 0.000 0.008
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.0000 0.995 0.000 0.000 1.000 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 4 0.0188 0.939 0.000 0.000 0.004 0.996
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.0188 0.998 0.004 0.000 0.996 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.938 0.000 0.000 0.000 1.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.3610 0.794 0.200 0.800 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.3610 0.794 0.200 0.800 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0817 0.929 0.024 0.000 0.000 0.976
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.0188 0.998 0.004 0.000 0.996 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.0188 0.939 0.004 0.000 0.000 0.996
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0188 0.939 0.004 0.000 0.000 0.996
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.4992 0.172 0.476 0.000 0.000 0.524
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0817 0.929 0.024 0.000 0.000 0.976
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.0188 0.998 0.004 0.000 0.996 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.5028 0.299 0.596 0.004 0.000 0.400
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.0188 0.939 0.004 0.000 0.000 0.996
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.0188 0.939 0.000 0.000 0.004 0.996
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.4985 0.198 0.468 0.000 0.000 0.532
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.0188 0.998 0.004 0.000 0.996 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.2921 0.820 0.140 0.000 0.000 0.860
#> 50D620F3-5C52-42FB-89A1-6840A7444647 4 0.0188 0.939 0.000 0.000 0.004 0.996
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0188 0.900 0.004 0.996 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.0188 0.939 0.004 0.000 0.000 0.996
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.4331 0.629 0.288 0.000 0.000 0.712
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.0188 0.939 0.000 0.000 0.004 0.996
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 4 0.0188 0.939 0.000 0.000 0.004 0.996
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.0000 0.995 0.000 0.000 1.000 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.1557 0.905 0.944 0.056 0.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.5212 0.745 0.192 0.740 0.000 0.068
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.0188 0.939 0.000 0.000 0.004 0.996
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.0188 0.939 0.000 0.000 0.004 0.996
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 4 0.0188 0.939 0.000 0.000 0.004 0.996
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.0188 0.939 0.000 0.000 0.004 0.996
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.0188 0.939 0.004 0.000 0.000 0.996
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0188 0.939 0.004 0.000 0.000 0.996
#> 84E18629-1B13-4696-8E54-121ABE469CD2 4 0.2530 0.845 0.000 0.100 0.004 0.896
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.0188 0.939 0.000 0.000 0.004 0.996
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.3610 0.794 0.200 0.800 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 4 0.0188 0.939 0.000 0.000 0.004 0.996
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 4 0.1082 0.924 0.004 0.020 0.004 0.972
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.0188 0.939 0.000 0.000 0.004 0.996
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0817 0.929 0.024 0.000 0.000 0.976
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0707 0.931 0.020 0.000 0.000 0.980
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.0000 0.938 0.000 0.000 0.000 1.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.2011 0.867 0.080 0.920 0.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.3528 0.801 0.192 0.808 0.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0188 0.939 0.004 0.000 0.000 0.996
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.3311 0.779 0.172 0.000 0.000 0.828
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.0188 0.939 0.004 0.000 0.000 0.996
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0188 0.939 0.004 0.000 0.000 0.996
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.3975 0.754 0.240 0.760 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.1022 0.883 0.032 0.968 0.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.3801 0.722 0.220 0.000 0.000 0.780
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0707 0.895 0.020 0.980 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.1004 0.928 0.024 0.000 0.004 0.972
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.1118 0.929 0.964 0.000 0.000 0.036
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.0188 0.998 0.004 0.000 0.996 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.3873 0.715 0.228 0.000 0.000 0.772
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.3219 0.819 0.164 0.836 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 4 0.0188 0.939 0.000 0.000 0.004 0.996
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.0188 0.939 0.004 0.000 0.000 0.996
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.4585 0.623 0.332 0.668 0.000 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0817 0.943 0.976 0.000 0.000 0.024
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.2011 0.883 0.080 0.000 0.000 0.920
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 4 0.0188 0.939 0.000 0.000 0.004 0.996
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.901 0.000 1.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.1940 0.888 0.076 0.000 0.000 0.924
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0188 0.939 0.004 0.000 0.000 0.996
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.0188 0.998 0.004 0.000 0.996 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.3764 0.694 0.784 0.216 0.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0707 0.931 0.020 0.000 0.000 0.980
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.3610 0.794 0.200 0.800 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.0188 0.939 0.000 0.000 0.004 0.996
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.0188 0.994 0.000 0.004 0.996 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 4 0.0290 0.8690 0.000 0.000 0.008 0.992 0
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 4 0.3336 0.8451 0.000 0.000 0.228 0.772 0
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.8626 0.000 1.000 0.000 0.000 0
#> 9264567D-4524-46AF-A851-C091C3CD76CF 4 0.3336 0.8451 0.000 0.000 0.228 0.772 0
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> 806616FE-1855-4284-9265-42842104CB21 4 0.3336 0.8451 0.000 0.000 0.228 0.772 0
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.3109 0.6365 0.200 0.800 0.000 0.000 0
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.8626 0.000 1.000 0.000 0.000 0
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 1 0.4297 0.0582 0.528 0.472 0.000 0.000 0
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 4 0.3336 0.8451 0.000 0.000 0.228 0.772 0
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.8689 0.000 0.000 0.000 1.000 0
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 3 0.5941 0.7059 0.180 0.228 0.592 0.000 0
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 3 0.6080 0.6778 0.200 0.228 0.572 0.000 0
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0510 0.8638 0.016 0.000 0.000 0.984 0
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 3 0.3336 0.8919 0.000 0.228 0.772 0.000 0
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.0000 0.8689 0.000 0.000 0.000 1.000 0
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.8626 0.000 1.000 0.000 0.000 0
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.8689 0.000 0.000 0.000 1.000 0
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.3612 0.5963 0.268 0.000 0.000 0.732 0
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0510 0.8638 0.016 0.000 0.000 0.984 0
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.2891 0.6906 0.824 0.000 0.000 0.176 0
#> F798E986-79BB-48FD-8514-95571EDB594B 3 0.3336 0.8919 0.000 0.228 0.772 0.000 0
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.0000 0.8689 0.000 0.000 0.000 1.000 0
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.3305 0.8461 0.000 0.000 0.224 0.776 0
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.3480 0.6268 0.248 0.000 0.000 0.752 0
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.2377 0.8104 0.128 0.000 0.000 0.872 0
#> 50D620F3-5C52-42FB-89A1-6840A7444647 4 0.3336 0.8451 0.000 0.000 0.228 0.772 0
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 3 0.3336 0.8919 0.000 0.228 0.772 0.000 0
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.8626 0.000 1.000 0.000 0.000 0
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.0000 0.8689 0.000 0.000 0.000 1.000 0
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.2690 0.7519 0.156 0.000 0.000 0.844 0
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.2329 0.8633 0.000 0.000 0.124 0.876 0
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 4 0.3336 0.8451 0.000 0.000 0.228 0.772 0
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.3266 0.6341 0.200 0.796 0.000 0.004 0
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.3274 0.8469 0.000 0.000 0.220 0.780 0
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.8626 0.000 1.000 0.000 0.000 0
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.3305 0.8461 0.000 0.000 0.224 0.776 0
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 4 0.3336 0.8451 0.000 0.000 0.228 0.772 0
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.3336 0.8451 0.000 0.000 0.228 0.772 0
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.8626 0.000 1.000 0.000 0.000 0
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.0000 0.8689 0.000 0.000 0.000 1.000 0
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0000 0.8689 0.000 0.000 0.000 1.000 0
#> 84E18629-1B13-4696-8E54-121ABE469CD2 4 0.4302 0.8223 0.000 0.048 0.208 0.744 0
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.3336 0.8451 0.000 0.000 0.228 0.772 0
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 1 0.4297 0.0582 0.528 0.472 0.000 0.000 0
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.8626 0.000 1.000 0.000 0.000 0
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 4 0.3336 0.8451 0.000 0.000 0.228 0.772 0
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 4 0.3491 0.8430 0.004 0.000 0.228 0.768 0
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.3274 0.8469 0.000 0.000 0.220 0.780 0
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0510 0.8638 0.016 0.000 0.000 0.984 0
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0404 0.8653 0.012 0.000 0.000 0.988 0
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.0000 0.8689 0.000 0.000 0.000 1.000 0
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.8626 0.000 1.000 0.000 0.000 0
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.8626 0.000 1.000 0.000 0.000 0
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.3177 0.6254 0.208 0.792 0.000 0.000 0
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.8626 0.000 1.000 0.000 0.000 0
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.3961 0.5393 0.248 0.736 0.016 0.000 0
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0000 0.8689 0.000 0.000 0.000 1.000 0
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.2179 0.7920 0.112 0.000 0.000 0.888 0
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.0000 0.8689 0.000 0.000 0.000 1.000 0
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0000 0.8689 0.000 0.000 0.000 1.000 0
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> 969A8063-FE1C-426C-821D-BDC714F1E385 1 0.5770 0.1571 0.532 0.372 0.096 0.000 0
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0609 0.8416 0.020 0.980 0.000 0.000 0
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.1851 0.8175 0.088 0.000 0.000 0.912 0
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.8626 0.000 1.000 0.000 0.000 0
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 3 0.3336 0.8919 0.000 0.228 0.772 0.000 0
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.3274 0.8469 0.000 0.000 0.220 0.780 0
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 3 0.3336 0.8919 0.000 0.228 0.772 0.000 0
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.3336 0.6536 0.772 0.000 0.000 0.228 0
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.3661 0.6811 0.276 0.000 0.000 0.724 0
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 3 0.3336 0.8919 0.000 0.228 0.772 0.000 0
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 3 0.3336 0.8919 0.000 0.228 0.772 0.000 0
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 4 0.3336 0.8451 0.000 0.000 0.228 0.772 0
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.8626 0.000 1.000 0.000 0.000 0
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.0000 0.8689 0.000 0.000 0.000 1.000 0
#> B3561356-5A80-4C79-B23A-D518425565FE 1 0.4294 0.0720 0.532 0.468 0.000 0.000 0
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.3039 0.7010 0.808 0.000 0.000 0.192 0
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.1341 0.8422 0.056 0.000 0.000 0.944 0
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 4 0.3336 0.8451 0.000 0.000 0.228 0.772 0
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 3 0.3336 0.8919 0.000 0.228 0.772 0.000 0
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.1270 0.8455 0.052 0.000 0.000 0.948 0
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0000 0.8689 0.000 0.000 0.000 1.000 0
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.9112 1.000 0.000 0.000 0.000 0
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.3983 0.4034 0.340 0.660 0.000 0.000 0
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0290 0.8667 0.008 0.000 0.000 0.992 0
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 3 0.6236 0.6434 0.208 0.248 0.544 0.000 0
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.2561 0.8607 0.000 0.000 0.144 0.856 0
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.0363 0.906 0.988 0.000 0.012 0.000 0 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 4 0.3659 0.372 0.000 0.000 0.364 0.636 0 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0363 0.882 0.000 0.000 0.988 0.012 0 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.889 0.000 1.000 0.000 0.000 0 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0363 0.882 0.000 0.000 0.988 0.012 0 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0790 0.884 0.000 0.000 0.968 0.032 0 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.3171 0.702 0.204 0.784 0.012 0.000 0 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.889 0.000 1.000 0.000 0.000 0 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 1 0.4165 0.125 0.536 0.452 0.012 0.000 0 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0363 0.882 0.000 0.000 0.988 0.012 0 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0146 0.920 0.000 0.000 0.004 0.996 0 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 6 0.2631 0.754 0.180 0.000 0.000 0.000 0 0.820
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 6 0.3043 0.730 0.200 0.000 0.008 0.000 0 0.792
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0146 0.918 0.004 0.000 0.000 0.996 0 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 6 0.0000 0.903 0.000 0.000 0.000 0.000 0 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.0146 0.920 0.000 0.000 0.004 0.996 0 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.889 0.000 1.000 0.000 0.000 0 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.919 0.000 0.000 0.000 1.000 0 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0363 0.913 0.012 0.000 0.000 0.988 0 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0146 0.918 0.004 0.000 0.000 0.996 0 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.0146 0.911 0.996 0.000 0.004 0.000 0 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 6 0.0000 0.903 0.000 0.000 0.000 0.000 0 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.0146 0.920 0.000 0.000 0.004 0.996 0 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.3659 0.469 0.000 0.000 0.636 0.364 0 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0363 0.913 0.012 0.000 0.000 0.988 0 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.2191 0.806 0.120 0.000 0.004 0.876 0 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0713 0.884 0.000 0.000 0.972 0.028 0 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 6 0.0000 0.903 0.000 0.000 0.000 0.000 0 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.889 0.000 1.000 0.000 0.000 0 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.0146 0.920 0.000 0.000 0.004 0.996 0 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.0777 0.903 0.024 0.000 0.004 0.972 0 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.2416 0.768 0.000 0.000 0.156 0.844 0 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0363 0.882 0.000 0.000 0.988 0.012 0 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.0146 0.911 0.996 0.000 0.004 0.000 0 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.3354 0.731 0.168 0.796 0.036 0.000 0 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.3817 0.132 0.000 0.000 0.432 0.568 0 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.889 0.000 1.000 0.000 0.000 0 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.2697 0.783 0.000 0.000 0.812 0.188 0 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0363 0.882 0.000 0.000 0.988 0.012 0 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.1075 0.878 0.000 0.000 0.952 0.048 0 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.889 0.000 1.000 0.000 0.000 0 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.0146 0.920 0.000 0.000 0.004 0.996 0 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0146 0.920 0.000 0.000 0.004 0.996 0 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.0363 0.882 0.000 0.000 0.988 0.012 0 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0865 0.883 0.000 0.000 0.964 0.036 0 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 1 0.4165 0.125 0.536 0.452 0.012 0.000 0 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.889 0.000 1.000 0.000 0.000 0 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.2219 0.818 0.000 0.000 0.864 0.136 0 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0790 0.884 0.000 0.000 0.968 0.032 0 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.3774 0.375 0.000 0.000 0.592 0.408 0 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0146 0.918 0.004 0.000 0.000 0.996 0 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.919 0.000 0.000 0.000 1.000 0 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.0547 0.910 0.000 0.000 0.020 0.980 0 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.889 0.000 1.000 0.000 0.000 0 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.889 0.000 1.000 0.000 0.000 0 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.2994 0.702 0.208 0.788 0.004 0.000 0 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.889 0.000 1.000 0.000 0.000 0 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.5036 0.539 0.228 0.632 0.000 0.000 0 0.140
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0146 0.920 0.000 0.000 0.004 0.996 0 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.0000 0.919 0.000 0.000 0.000 1.000 0 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.0146 0.911 0.996 0.000 0.004 0.000 0 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.0146 0.920 0.000 0.000 0.004 0.996 0 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0146 0.920 0.000 0.000 0.004 0.996 0 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 1 0.5821 0.306 0.544 0.268 0.012 0.000 0 0.176
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0000 0.889 0.000 1.000 0.000 0.000 0 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.0146 0.911 0.996 0.000 0.004 0.000 0 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0363 0.913 0.012 0.000 0.000 0.988 0 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.889 0.000 1.000 0.000 0.000 0 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 6 0.0000 0.903 0.000 0.000 0.000 0.000 0 1.000
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.3944 0.120 0.004 0.000 0.428 0.568 0 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 6 0.0000 0.903 0.000 0.000 0.000 0.000 0 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.3774 0.341 0.592 0.000 0.000 0.408 0 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.0146 0.911 0.996 0.000 0.004 0.000 0 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.3360 0.603 0.264 0.000 0.004 0.732 0 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 6 0.0000 0.903 0.000 0.000 0.000 0.000 0 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 6 0.0000 0.903 0.000 0.000 0.000 0.000 0 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.2941 0.745 0.000 0.000 0.780 0.220 0 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.889 0.000 1.000 0.000 0.000 0 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.0146 0.920 0.000 0.000 0.004 0.996 0 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 1 0.3971 0.153 0.548 0.448 0.004 0.000 0 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.3151 0.613 0.748 0.000 0.000 0.252 0 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0260 0.916 0.008 0.000 0.000 0.992 0 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0790 0.884 0.000 0.000 0.968 0.032 0 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 6 0.0000 0.903 0.000 0.000 0.000 0.000 0 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0146 0.918 0.004 0.000 0.000 0.996 0 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0146 0.920 0.000 0.000 0.004 0.996 0 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.0146 0.911 0.996 0.000 0.004 0.000 0 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.913 1.000 0.000 0.000 0.000 0 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.3810 0.247 0.428 0.572 0.000 0.000 0 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0146 0.920 0.000 0.000 0.004 0.996 0 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 6 0.3623 0.707 0.208 0.020 0.008 0.000 0 0.764
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.2491 0.751 0.000 0.000 0.164 0.836 0 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 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", "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 17548 rows and 122 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 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 0.505 0.796 0.898 0.4916 0.512 0.512
#> 3 3 0.661 0.769 0.804 0.2372 0.908 0.823
#> 4 4 0.746 0.820 0.887 0.1261 0.870 0.703
#> 5 5 0.831 0.842 0.915 0.1347 0.843 0.540
#> 6 6 0.885 0.850 0.922 0.0419 0.976 0.887
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 2 0.7950 0.752 0.240 0.760
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.0000 0.812 0.000 1.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.972 1.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 2 0.7950 0.752 0.240 0.760
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 1 0.0000 0.972 1.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.812 0.000 1.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 1 0.0000 0.972 1.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 2 0.7950 0.752 0.240 0.760
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 2 0.7950 0.752 0.240 0.760
#> 806616FE-1855-4284-9265-42842104CB21 1 0.0000 0.972 1.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.812 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.812 0.000 1.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 2 0.7950 0.752 0.240 0.760
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.812 0.000 1.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 2 0.9881 0.230 0.436 0.564
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 1 0.0000 0.972 1.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 2 0.9881 0.230 0.436 0.564
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.972 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.812 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.812 0.000 1.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0672 0.971 0.992 0.008
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.812 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 2 0.9881 0.230 0.436 0.564
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0672 0.971 0.992 0.008
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.812 0.000 1.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0672 0.971 0.992 0.008
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0672 0.971 0.992 0.008
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0672 0.971 0.992 0.008
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 2 0.9881 0.230 0.436 0.564
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.0672 0.810 0.008 0.992
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.812 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 2 0.7950 0.752 0.240 0.760
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0672 0.971 0.992 0.008
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.0000 0.972 1.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0672 0.971 0.992 0.008
#> AA403EC3-FD44-4247-B06D-AEF415391E46 2 0.9881 0.230 0.436 0.564
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0672 0.971 0.992 0.008
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.0938 0.960 0.988 0.012
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.812 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 2 0.7950 0.752 0.240 0.760
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.812 0.000 1.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0672 0.971 0.992 0.008
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.9922 0.142 0.552 0.448
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.972 1.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 1 0.0000 0.972 1.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.9881 0.230 0.436 0.564
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.6801 0.773 0.180 0.820
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.9896 0.274 0.440 0.560
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 2 0.7950 0.752 0.240 0.760
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.0000 0.972 1.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.812 0.000 1.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.0000 0.972 1.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.0000 0.972 1.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 2 0.7950 0.752 0.240 0.760
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.0000 0.972 1.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 2 0.7950 0.752 0.240 0.760
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.812 0.000 1.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0672 0.971 0.992 0.008
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0672 0.971 0.992 0.008
#> 84E18629-1B13-4696-8E54-121ABE469CD2 1 0.0000 0.972 1.000 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.0000 0.972 1.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.812 0.000 1.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.812 0.000 1.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.0000 0.972 1.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 1 0.0000 0.972 1.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.0000 0.972 1.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0672 0.971 0.992 0.008
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0672 0.971 0.992 0.008
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0000 0.972 1.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.9944 -0.138 0.544 0.456
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.812 0.000 1.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.812 0.000 1.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.812 0.000 1.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.812 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 2 0.7950 0.752 0.240 0.760
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.9129 0.462 0.328 0.672
#> A314C4E6-B245-4F10-A555-50B9B819040D 2 0.7950 0.752 0.240 0.760
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0672 0.971 0.992 0.008
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0672 0.971 0.992 0.008
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.7950 0.752 0.240 0.760
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0672 0.971 0.992 0.008
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0672 0.971 0.992 0.008
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 2 0.7950 0.752 0.240 0.760
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.812 0.000 1.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0000 0.812 0.000 1.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.5519 0.788 0.128 0.872
#> 6F7DB73C-FE46-402C-9001-DC2005278069 2 0.7950 0.752 0.240 0.760
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0672 0.971 0.992 0.008
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.812 0.000 1.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.812 0.000 1.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.0000 0.972 1.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 2 0.7950 0.752 0.240 0.760
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.812 0.000 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 2 0.9522 0.561 0.372 0.628
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 2 0.9881 0.230 0.436 0.564
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.7299 0.765 0.204 0.796
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.972 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 2 0.7950 0.752 0.240 0.760
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.812 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.812 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0000 0.972 1.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 2 0.7950 0.752 0.240 0.760
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 2 0.7950 0.752 0.240 0.760
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.812 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0672 0.971 0.992 0.008
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.812 0.000 1.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 2 0.7950 0.752 0.240 0.760
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 2 0.7950 0.752 0.240 0.760
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0672 0.971 0.992 0.008
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 1 0.0000 0.972 1.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.812 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 2 0.7950 0.752 0.240 0.760
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0672 0.971 0.992 0.008
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0672 0.971 0.992 0.008
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.2603 0.806 0.044 0.956
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 2 0.9881 0.230 0.436 0.564
#> 12F54761-4F68-4181-8421-88EA858902FC 2 0.7950 0.752 0.240 0.760
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.7950 0.752 0.240 0.760
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0672 0.971 0.992 0.008
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.812 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.0000 0.972 1.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.9881 0.230 0.436 0.564
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 2 0.6192 0.6797 0.000 0.580 0.420
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.1411 0.8176 0.000 0.964 0.036
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.7896 1.000 0.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 2 0.6192 0.6797 0.000 0.580 0.420
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 1 0.0000 0.7896 1.000 0.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.8196 0.000 1.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 1 0.0237 0.7861 0.996 0.000 0.004
#> 45EAD449-C59A-463E-880A-C375CDD039BA 2 0.6192 0.6797 0.000 0.580 0.420
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 2 0.2066 0.8127 0.000 0.940 0.060
#> 806616FE-1855-4284-9265-42842104CB21 1 0.0000 0.7896 1.000 0.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.8196 0.000 1.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.8196 0.000 1.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 2 0.6192 0.6797 0.000 0.580 0.420
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.1964 0.7946 0.000 0.944 0.056
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.7309 1.0000 0.416 0.032 0.552
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 1 0.0237 0.7861 0.996 0.000 0.004
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.7309 1.0000 0.416 0.032 0.552
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.7896 1.000 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.1163 0.8119 0.000 0.972 0.028
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.1031 0.8133 0.000 0.976 0.024
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.4654 0.7813 0.792 0.000 0.208
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.1163 0.8119 0.000 0.972 0.028
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.7309 1.0000 0.416 0.032 0.552
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.4654 0.7813 0.792 0.000 0.208
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.8196 0.000 1.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.4654 0.7813 0.792 0.000 0.208
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.4654 0.7813 0.792 0.000 0.208
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.4654 0.7813 0.792 0.000 0.208
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.7309 1.0000 0.416 0.032 0.552
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.1753 0.8118 0.000 0.952 0.048
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.1163 0.8119 0.000 0.972 0.028
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 2 0.6192 0.6797 0.000 0.580 0.420
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.4654 0.7813 0.792 0.000 0.208
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.0000 0.7896 1.000 0.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.4654 0.7813 0.792 0.000 0.208
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.7309 1.0000 0.416 0.032 0.552
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.1753 0.7945 0.952 0.000 0.048
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.2165 0.7019 0.936 0.000 0.064
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.1163 0.8119 0.000 0.972 0.028
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 2 0.6192 0.6797 0.000 0.580 0.420
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.8196 0.000 1.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.4654 0.7813 0.792 0.000 0.208
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.8665 -0.7326 0.508 0.108 0.384
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.7896 1.000 0.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 1 0.1163 0.7663 0.972 0.000 0.028
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.7309 1.0000 0.416 0.032 0.552
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0424 0.8199 0.000 0.992 0.008
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.9251 0.0785 0.260 0.528 0.212
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 2 0.6192 0.6797 0.000 0.580 0.420
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.0000 0.7896 1.000 0.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0237 0.8198 0.000 0.996 0.004
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.0000 0.7896 1.000 0.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.0000 0.7896 1.000 0.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 2 0.6192 0.6797 0.000 0.580 0.420
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.0000 0.7896 1.000 0.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 2 0.6192 0.6797 0.000 0.580 0.420
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.8196 0.000 1.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.4654 0.7813 0.792 0.000 0.208
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.4654 0.7813 0.792 0.000 0.208
#> 84E18629-1B13-4696-8E54-121ABE469CD2 1 0.1964 0.7327 0.944 0.000 0.056
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.0000 0.7896 1.000 0.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.1163 0.8119 0.000 0.972 0.028
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.8196 0.000 1.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.0000 0.7896 1.000 0.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 1 0.0237 0.7872 0.996 0.000 0.004
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.0000 0.7896 1.000 0.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.1753 0.7976 0.952 0.000 0.048
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.4654 0.7813 0.792 0.000 0.208
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.2448 0.7978 0.924 0.000 0.076
#> 352471DC-A881-4EA8-B646-EB1200291893 2 0.8202 0.6066 0.080 0.544 0.376
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.8196 0.000 1.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.8196 0.000 1.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0592 0.8200 0.000 0.988 0.012
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.8196 0.000 1.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 2 0.5465 0.7415 0.000 0.712 0.288
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.8282 0.3533 0.160 0.632 0.208
#> A314C4E6-B245-4F10-A555-50B9B819040D 2 0.6192 0.6797 0.000 0.580 0.420
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.2261 0.7983 0.932 0.000 0.068
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.4654 0.7813 0.792 0.000 0.208
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.2711 0.8071 0.000 0.912 0.088
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.4654 0.7813 0.792 0.000 0.208
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.4654 0.7813 0.792 0.000 0.208
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 2 0.6192 0.6797 0.000 0.580 0.420
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.1163 0.8119 0.000 0.972 0.028
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0000 0.8196 0.000 1.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.1163 0.8184 0.000 0.972 0.028
#> 6F7DB73C-FE46-402C-9001-DC2005278069 2 0.6192 0.6797 0.000 0.580 0.420
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.4654 0.7813 0.792 0.000 0.208
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.8196 0.000 1.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0424 0.8199 0.000 0.992 0.008
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.0000 0.7896 1.000 0.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 2 0.6215 0.6731 0.000 0.572 0.428
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.1163 0.8119 0.000 0.972 0.028
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 2 0.6881 0.6764 0.020 0.592 0.388
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.7309 1.0000 0.416 0.032 0.552
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.2796 0.8061 0.000 0.908 0.092
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.4654 0.7813 0.792 0.000 0.208
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 2 0.6192 0.6797 0.000 0.580 0.420
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.1163 0.8119 0.000 0.972 0.028
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.1163 0.8119 0.000 0.972 0.028
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0000 0.7896 1.000 0.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 2 0.3816 0.7912 0.000 0.852 0.148
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 2 0.6192 0.6797 0.000 0.580 0.420
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.8196 0.000 1.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.4654 0.7813 0.792 0.000 0.208
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.1411 0.8077 0.000 0.964 0.036
#> F900E9BE-2400-4451-9434-EE8BC513BA94 2 0.6215 0.6731 0.000 0.572 0.428
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 2 0.5948 0.7072 0.000 0.640 0.360
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.4654 0.7813 0.792 0.000 0.208
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 1 0.1031 0.7704 0.976 0.000 0.024
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.1163 0.8119 0.000 0.972 0.028
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 2 0.6192 0.6797 0.000 0.580 0.420
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.4702 0.7761 0.788 0.000 0.212
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.4654 0.7813 0.792 0.000 0.208
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.1411 0.8177 0.000 0.964 0.036
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.7309 1.0000 0.416 0.032 0.552
#> 12F54761-4F68-4181-8421-88EA858902FC 2 0.5706 0.7251 0.000 0.680 0.320
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.4974 0.7604 0.000 0.764 0.236
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.3879 0.7888 0.848 0.000 0.152
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.1163 0.8119 0.000 0.972 0.028
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.0000 0.7896 1.000 0.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.7309 1.0000 0.416 0.032 0.552
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.3123 0.91280 0.844 0.156 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.4175 0.70787 0.200 0.784 0.016 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 4 0.0188 0.94481 0.000 0.000 0.004 0.996
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.3123 0.91280 0.844 0.156 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 4 0.2216 0.92199 0.000 0.000 0.092 0.908
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.2011 0.81202 0.080 0.920 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 4 0.5121 0.80345 0.120 0.000 0.116 0.764
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.3123 0.91280 0.844 0.156 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 2 0.4608 0.55924 0.304 0.692 0.004 0.000
#> 806616FE-1855-4284-9265-42842104CB21 4 0.2334 0.92278 0.004 0.000 0.088 0.908
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.1940 0.81279 0.076 0.924 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.2011 0.81202 0.080 0.920 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.3266 0.90794 0.832 0.168 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.2987 0.76310 0.104 0.880 0.016 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.0000 0.97145 0.000 0.000 1.000 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 4 0.2408 0.91526 0.000 0.000 0.104 0.896
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.0000 0.97145 0.000 0.000 1.000 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.1637 0.93396 0.000 0.000 0.060 0.940
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.80227 0.000 1.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0469 0.80647 0.012 0.988 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0336 0.94405 0.008 0.000 0.000 0.992
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.80227 0.000 1.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.0817 0.94926 0.000 0.024 0.976 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.0336 0.94405 0.008 0.000 0.000 0.992
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.1940 0.81268 0.076 0.924 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0336 0.94405 0.008 0.000 0.000 0.992
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0336 0.94405 0.008 0.000 0.000 0.992
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0336 0.94405 0.008 0.000 0.000 0.992
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.0000 0.97145 0.000 0.000 1.000 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.3853 0.70444 0.160 0.820 0.020 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.2345 0.72509 0.100 0.900 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.3266 0.90794 0.832 0.168 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.0000 0.94447 0.000 0.000 0.000 1.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.2149 0.92354 0.000 0.000 0.088 0.912
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0336 0.94405 0.008 0.000 0.000 0.992
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.0000 0.97145 0.000 0.000 1.000 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.5181 0.82012 0.060 0.080 0.060 0.800
#> 50D620F3-5C52-42FB-89A1-6840A7444647 4 0.3962 0.87306 0.044 0.000 0.124 0.832
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.80227 0.000 1.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.3123 0.91280 0.844 0.156 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.2011 0.81202 0.080 0.920 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.0336 0.94405 0.008 0.000 0.000 0.992
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.5533 0.75455 0.032 0.064 0.764 0.140
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.2149 0.92354 0.000 0.000 0.088 0.912
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 4 0.2799 0.90930 0.008 0.000 0.108 0.884
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.0000 0.97145 0.000 0.000 1.000 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.4722 0.56964 0.300 0.692 0.008 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.6570 0.54512 0.248 0.656 0.036 0.060
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.3356 0.89794 0.824 0.176 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.0188 0.94481 0.000 0.000 0.004 0.996
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.2081 0.81058 0.084 0.916 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.0188 0.94481 0.000 0.000 0.004 0.996
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 4 0.2675 0.91470 0.008 0.000 0.100 0.892
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.3219 0.90942 0.836 0.164 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.2412 0.92361 0.008 0.000 0.084 0.908
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.3123 0.91280 0.844 0.156 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.2011 0.81202 0.080 0.920 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.0336 0.94405 0.008 0.000 0.000 0.992
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0000 0.94447 0.000 0.000 0.000 1.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 4 0.6383 0.72980 0.136 0.032 0.124 0.708
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.2081 0.92507 0.000 0.000 0.084 0.916
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0524 0.80396 0.008 0.988 0.004 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.2216 0.80635 0.092 0.908 0.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 4 0.0188 0.94481 0.000 0.000 0.004 0.996
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 4 0.4211 0.87236 0.016 0.032 0.120 0.832
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.1118 0.94001 0.000 0.000 0.036 0.964
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.2685 0.91707 0.004 0.044 0.040 0.912
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0336 0.94405 0.008 0.000 0.000 0.992
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.0188 0.94481 0.000 0.000 0.004 0.996
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.5798 0.46635 0.584 0.384 0.004 0.028
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.2011 0.81202 0.080 0.920 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.2011 0.81202 0.080 0.920 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.2345 0.80181 0.100 0.900 0.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.2011 0.81202 0.080 0.920 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.3837 0.84364 0.776 0.224 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.5984 0.58054 0.248 0.684 0.020 0.048
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.3266 0.90794 0.832 0.168 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0000 0.94447 0.000 0.000 0.000 1.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.0336 0.94405 0.008 0.000 0.000 0.992
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.4961 0.12226 0.448 0.552 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.0000 0.94447 0.000 0.000 0.000 1.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0336 0.94405 0.008 0.000 0.000 0.992
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.3123 0.91280 0.844 0.156 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0469 0.80584 0.012 0.988 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.2530 0.79414 0.112 0.888 0.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.4584 0.55764 0.300 0.696 0.004 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.3123 0.91280 0.844 0.156 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0336 0.94405 0.008 0.000 0.000 0.992
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.1867 0.81291 0.072 0.928 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.1022 0.81115 0.032 0.968 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.2345 0.91761 0.000 0.000 0.100 0.900
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.4164 0.78533 0.736 0.264 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.2345 0.72511 0.100 0.900 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 2 0.5229 0.17665 0.428 0.564 0.000 0.008
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.0000 0.97145 0.000 0.000 1.000 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.4989 0.00868 0.472 0.528 0.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0657 0.94439 0.012 0.000 0.004 0.984
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.3123 0.91280 0.844 0.156 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.2345 0.72509 0.100 0.900 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.1118 0.77999 0.036 0.964 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 4 0.2408 0.91526 0.000 0.000 0.104 0.896
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.4996 0.14589 0.516 0.484 0.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.3123 0.91280 0.844 0.156 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0188 0.80415 0.004 0.996 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.0336 0.94405 0.008 0.000 0.000 0.992
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.2861 0.76545 0.096 0.888 0.016 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.3123 0.91280 0.844 0.156 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 2 0.5161 -0.04010 0.476 0.520 0.000 0.004
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0336 0.94405 0.008 0.000 0.000 0.992
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 4 0.2737 0.91184 0.008 0.000 0.104 0.888
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0188 0.80067 0.004 0.996 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.3123 0.91280 0.844 0.156 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.2780 0.91646 0.016 0.048 0.024 0.912
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0188 0.94443 0.004 0.000 0.000 0.996
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.4431 0.55345 0.304 0.696 0.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.0000 0.97145 0.000 0.000 1.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 2 0.4866 0.28787 0.404 0.596 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.4972 0.28944 0.544 0.456 0.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.2494 0.91666 0.000 0.048 0.036 0.916
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0707 0.79937 0.020 0.980 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.0188 0.94481 0.000 0.000 0.004 0.996
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.0000 0.97145 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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.838 1.000 0.000 0.000 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.5865 0.533 0.568 0.344 0.016 0.000 0.072
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.2648 0.852 0.000 0.000 0.848 0.152 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.838 1.000 0.000 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0963 0.963 0.000 0.000 0.964 0.036 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0451 0.929 0.008 0.988 0.004 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0794 0.958 0.000 0.000 0.972 0.028 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.838 1.000 0.000 0.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.5072 0.643 0.652 0.300 0.016 0.000 0.032
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0963 0.963 0.000 0.000 0.964 0.036 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0579 0.927 0.008 0.984 0.008 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0451 0.929 0.008 0.988 0.004 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.838 1.000 0.000 0.000 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.5875 0.464 0.228 0.636 0.016 0.000 0.120
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.0000 0.945 0.000 0.000 0.000 0.000 1.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.1043 0.961 0.000 0.000 0.960 0.040 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0000 0.945 0.000 0.000 0.000 0.000 1.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.4300 0.138 0.000 0.000 0.476 0.524 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0162 0.928 0.004 0.996 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0451 0.928 0.008 0.988 0.004 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.1732 0.872 0.000 0.000 0.080 0.920 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.927 0.000 1.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.2338 0.838 0.000 0.112 0.004 0.000 0.884
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0451 0.929 0.008 0.988 0.004 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.0000 0.945 0.000 0.000 0.000 0.000 1.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.6386 0.471 0.520 0.344 0.016 0.000 0.120
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0290 0.924 0.000 0.992 0.008 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.838 1.000 0.000 0.000 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.0290 0.907 0.000 0.000 0.008 0.992 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.1341 0.953 0.000 0.000 0.944 0.056 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0000 0.945 0.000 0.000 0.000 0.000 1.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.5144 0.545 0.000 0.000 0.304 0.632 0.064
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.1357 0.951 0.000 0.004 0.948 0.048 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.927 0.000 1.000 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.838 1.000 0.000 0.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0451 0.929 0.008 0.988 0.004 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 5 0.6339 0.587 0.008 0.188 0.016 0.172 0.616
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.1043 0.963 0.000 0.000 0.960 0.040 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0963 0.963 0.000 0.000 0.964 0.036 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0000 0.945 0.000 0.000 0.000 0.000 1.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.5360 0.451 0.552 0.396 0.004 0.000 0.048
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.8302 0.153 0.192 0.400 0.276 0.008 0.124
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.838 1.000 0.000 0.000 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.1043 0.963 0.000 0.000 0.960 0.040 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0451 0.929 0.008 0.988 0.004 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.1341 0.955 0.000 0.000 0.944 0.056 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0963 0.963 0.000 0.000 0.964 0.036 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.838 1.000 0.000 0.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.0963 0.963 0.000 0.000 0.964 0.036 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.838 1.000 0.000 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0451 0.929 0.008 0.988 0.004 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.0794 0.958 0.000 0.000 0.972 0.028 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.1043 0.963 0.000 0.000 0.960 0.040 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.1644 0.886 0.048 0.940 0.004 0.000 0.008
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0451 0.929 0.008 0.988 0.004 0.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.1043 0.963 0.000 0.000 0.960 0.040 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0880 0.961 0.000 0.000 0.968 0.032 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.1043 0.963 0.000 0.000 0.960 0.040 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.2773 0.808 0.000 0.000 0.164 0.836 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.4101 0.407 0.000 0.000 0.628 0.372 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.5858 0.692 0.668 0.220 0.016 0.076 0.020
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0451 0.929 0.008 0.988 0.004 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0451 0.929 0.008 0.988 0.004 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0451 0.929 0.008 0.988 0.004 0.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0451 0.929 0.008 0.988 0.004 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.838 1.000 0.000 0.000 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.7785 0.332 0.192 0.500 0.180 0.004 0.124
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.838 1.000 0.000 0.000 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0703 0.903 0.000 0.000 0.024 0.976 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.0510 0.905 0.000 0.000 0.016 0.984 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.1732 0.822 0.920 0.080 0.000 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.838 1.000 0.000 0.000 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0865 0.917 0.024 0.972 0.004 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.1282 0.900 0.044 0.952 0.004 0.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.4333 0.587 0.640 0.352 0.004 0.000 0.004
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.838 1.000 0.000 0.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.1410 0.887 0.000 0.000 0.060 0.940 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0162 0.928 0.004 0.996 0.000 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0162 0.928 0.004 0.996 0.000 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.2280 0.883 0.000 0.000 0.880 0.120 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.2753 0.807 0.876 0.104 0.012 0.008 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0162 0.926 0.000 0.996 0.004 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.5135 0.723 0.704 0.228 0.016 0.044 0.008
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.0000 0.945 0.000 0.000 0.000 0.000 1.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.2230 0.811 0.884 0.116 0.000 0.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.4171 0.400 0.000 0.000 0.396 0.604 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.838 1.000 0.000 0.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0290 0.924 0.000 0.992 0.008 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.927 0.000 1.000 0.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.1043 0.963 0.000 0.000 0.960 0.040 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.2377 0.805 0.872 0.128 0.000 0.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.838 1.000 0.000 0.000 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0324 0.928 0.004 0.992 0.004 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.0794 0.902 0.000 0.000 0.028 0.972 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.5833 0.469 0.228 0.640 0.016 0.000 0.116
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.838 1.000 0.000 0.000 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.3962 0.772 0.788 0.180 0.016 0.012 0.004
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0963 0.963 0.000 0.000 0.964 0.036 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.927 0.000 1.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.838 1.000 0.000 0.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.1965 0.863 0.000 0.000 0.096 0.904 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.1341 0.889 0.000 0.000 0.056 0.944 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.4182 0.590 0.644 0.352 0.004 0.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.0000 0.945 0.000 0.000 0.000 0.000 1.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.4194 0.710 0.720 0.260 0.016 0.000 0.004
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.3398 0.758 0.780 0.216 0.004 0.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.2690 0.816 0.000 0.000 0.156 0.844 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0798 0.920 0.016 0.976 0.008 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.1671 0.938 0.000 0.000 0.924 0.076 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0000 0.945 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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0146 0.857 0.996 0.000 0.000 0.000 0.000 0.004
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 6 0.4317 0.556 0.252 0.060 0.000 0.000 0.000 0.688
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.1007 0.937 0.000 0.000 0.956 0.044 0.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0632 0.851 0.976 0.000 0.000 0.000 0.000 0.024
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0146 0.961 0.000 0.000 0.996 0.004 0.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.950 0.000 1.000 0.000 0.000 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0146 0.961 0.000 0.000 0.996 0.004 0.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.857 1.000 0.000 0.000 0.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.4251 0.512 0.624 0.028 0.000 0.000 0.000 0.348
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0458 0.951 0.000 0.984 0.000 0.000 0.000 0.016
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.950 0.000 1.000 0.000 0.000 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0146 0.857 0.996 0.000 0.000 0.000 0.000 0.004
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 6 0.2358 0.788 0.016 0.108 0.000 0.000 0.000 0.876
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.0146 0.918 0.000 0.000 0.000 0.000 0.996 0.004
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0146 0.961 0.000 0.000 0.996 0.004 0.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0000 0.917 0.000 0.000 0.000 0.000 1.000 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.3789 0.335 0.000 0.000 0.416 0.584 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.1007 0.947 0.000 0.956 0.000 0.000 0.000 0.044
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0937 0.947 0.000 0.960 0.000 0.000 0.000 0.040
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.1327 0.870 0.000 0.000 0.064 0.936 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.1524 0.948 0.000 0.932 0.000 0.008 0.000 0.060
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.2805 0.749 0.000 0.012 0.000 0.000 0.828 0.160
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.0260 0.905 0.000 0.000 0.008 0.992 0.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.1462 0.949 0.000 0.936 0.000 0.008 0.000 0.056
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0260 0.905 0.000 0.000 0.008 0.992 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.1333 0.885 0.000 0.000 0.008 0.944 0.000 0.048
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0260 0.905 0.000 0.000 0.008 0.992 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.0000 0.917 0.000 0.000 0.000 0.000 1.000 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 6 0.2934 0.768 0.112 0.044 0.000 0.000 0.000 0.844
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.1584 0.947 0.000 0.928 0.000 0.008 0.000 0.064
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0146 0.857 0.996 0.000 0.000 0.000 0.000 0.004
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.0363 0.904 0.000 0.000 0.012 0.988 0.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.0713 0.951 0.000 0.000 0.972 0.028 0.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.1196 0.889 0.000 0.000 0.008 0.952 0.000 0.040
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0000 0.917 0.000 0.000 0.000 0.000 1.000 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.4845 0.544 0.000 0.000 0.092 0.628 0.000 0.280
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0937 0.935 0.000 0.000 0.960 0.040 0.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.1524 0.948 0.000 0.932 0.000 0.008 0.000 0.060
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0146 0.857 0.996 0.000 0.000 0.000 0.000 0.004
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.950 0.000 1.000 0.000 0.000 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.0260 0.905 0.000 0.000 0.008 0.992 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 5 0.6448 0.193 0.020 0.020 0.004 0.136 0.512 0.308
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.0458 0.960 0.000 0.000 0.984 0.016 0.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0146 0.918 0.000 0.000 0.000 0.000 0.996 0.004
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.4751 0.501 0.616 0.072 0.000 0.000 0.000 0.312
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 6 0.2039 0.752 0.004 0.016 0.072 0.000 0.000 0.908
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.857 1.000 0.000 0.000 0.000 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.0363 0.961 0.000 0.000 0.988 0.012 0.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.950 0.000 1.000 0.000 0.000 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0458 0.960 0.000 0.000 0.984 0.016 0.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0146 0.857 0.996 0.000 0.000 0.000 0.000 0.004
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0260 0.856 0.992 0.000 0.000 0.000 0.000 0.008
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.950 0.000 1.000 0.000 0.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.0260 0.905 0.000 0.000 0.008 0.992 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0260 0.905 0.000 0.000 0.008 0.992 0.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0260 0.962 0.000 0.000 0.992 0.008 0.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.3003 0.788 0.016 0.812 0.000 0.000 0.000 0.172
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.950 0.000 1.000 0.000 0.000 0.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0363 0.961 0.000 0.000 0.988 0.012 0.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.0363 0.961 0.000 0.000 0.988 0.012 0.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.3841 0.742 0.000 0.000 0.068 0.764 0.000 0.168
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0260 0.905 0.000 0.000 0.008 0.992 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.3428 0.535 0.000 0.000 0.696 0.304 0.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.4418 0.488 0.604 0.012 0.000 0.016 0.000 0.368
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.950 0.000 1.000 0.000 0.000 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.950 0.000 1.000 0.000 0.000 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0891 0.935 0.024 0.968 0.000 0.000 0.000 0.008
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.950 0.000 1.000 0.000 0.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0146 0.857 0.996 0.000 0.000 0.000 0.000 0.004
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 6 0.2078 0.776 0.004 0.040 0.044 0.000 0.000 0.912
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.2092 0.788 0.876 0.000 0.000 0.000 0.000 0.124
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0458 0.903 0.000 0.000 0.016 0.984 0.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.0260 0.905 0.000 0.000 0.008 0.992 0.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.0000 0.857 1.000 0.000 0.000 0.000 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.0260 0.905 0.000 0.000 0.008 0.992 0.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0260 0.905 0.000 0.000 0.008 0.992 0.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0146 0.857 0.996 0.000 0.000 0.000 0.000 0.004
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.2510 0.859 0.028 0.872 0.000 0.000 0.000 0.100
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0790 0.928 0.032 0.968 0.000 0.000 0.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.4220 0.621 0.732 0.172 0.000 0.000 0.000 0.096
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0260 0.856 0.992 0.000 0.000 0.000 0.000 0.008
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0508 0.904 0.000 0.000 0.012 0.984 0.000 0.004
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.1462 0.949 0.000 0.936 0.000 0.008 0.000 0.056
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.1333 0.950 0.000 0.944 0.000 0.008 0.000 0.048
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.2416 0.794 0.000 0.000 0.844 0.156 0.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.3136 0.708 0.768 0.000 0.000 0.004 0.000 0.228
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.1524 0.948 0.000 0.932 0.000 0.008 0.000 0.060
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.3911 0.525 0.624 0.000 0.000 0.008 0.000 0.368
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.0000 0.917 0.000 0.000 0.000 0.000 1.000 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.0692 0.849 0.976 0.020 0.000 0.000 0.000 0.004
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.3907 0.364 0.000 0.000 0.408 0.588 0.000 0.004
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0146 0.857 0.996 0.000 0.000 0.000 0.000 0.004
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.1584 0.947 0.000 0.928 0.000 0.008 0.000 0.064
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.1866 0.936 0.000 0.908 0.000 0.008 0.000 0.084
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.0363 0.961 0.000 0.000 0.988 0.012 0.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.1082 0.836 0.956 0.040 0.000 0.000 0.000 0.004
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0146 0.857 0.996 0.000 0.000 0.000 0.000 0.004
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.1524 0.948 0.000 0.932 0.000 0.008 0.000 0.060
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.0260 0.905 0.000 0.000 0.008 0.992 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 6 0.3364 0.713 0.024 0.196 0.000 0.000 0.000 0.780
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.2454 0.765 0.840 0.000 0.000 0.000 0.000 0.160
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.3850 0.571 0.652 0.004 0.000 0.004 0.000 0.340
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.1265 0.887 0.000 0.000 0.008 0.948 0.000 0.044
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0000 0.960 0.000 0.000 1.000 0.000 0.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.1524 0.948 0.000 0.932 0.000 0.008 0.000 0.060
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.857 1.000 0.000 0.000 0.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.3645 0.762 0.000 0.000 0.064 0.784 0.000 0.152
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0363 0.904 0.000 0.000 0.012 0.988 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.4061 0.641 0.748 0.164 0.000 0.000 0.000 0.088
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.0146 0.918 0.000 0.000 0.000 0.000 0.996 0.004
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.4251 0.648 0.716 0.076 0.000 0.000 0.000 0.208
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.2805 0.715 0.828 0.160 0.000 0.000 0.000 0.012
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.3700 0.760 0.000 0.000 0.068 0.780 0.000 0.152
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.2092 0.869 0.000 0.876 0.000 0.000 0.000 0.124
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.0790 0.949 0.000 0.000 0.968 0.032 0.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0146 0.918 0.000 0.000 0.000 0.000 0.996 0.004
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 17548 rows and 122 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 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 0.753 0.882 0.948 0.5039 0.496 0.496
#> 3 3 0.637 0.701 0.842 0.2812 0.761 0.554
#> 4 4 0.726 0.775 0.857 0.1138 0.855 0.615
#> 5 5 0.735 0.678 0.817 0.0954 0.893 0.637
#> 6 6 0.860 0.771 0.890 0.0515 0.910 0.616
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 2 0.0000 0.939 0.000 1.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.0000 0.939 0.000 1.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.946 1.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 2 0.7950 0.714 0.240 0.760
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 1 0.0000 0.946 1.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.939 0.000 1.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 1 0.0000 0.946 1.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 2 0.7376 0.754 0.208 0.792
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 2 0.6623 0.795 0.172 0.828
#> 806616FE-1855-4284-9265-42842104CB21 1 0.0000 0.946 1.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.939 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.939 0.000 1.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 2 0.0672 0.934 0.008 0.992
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.939 0.000 1.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 1 0.1414 0.931 0.980 0.020
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 1 0.0000 0.946 1.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 1 0.7376 0.745 0.792 0.208
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.946 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.939 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.939 0.000 1.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.946 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.939 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 1 0.7299 0.750 0.796 0.204
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.946 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.939 0.000 1.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.946 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.946 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.946 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.7528 0.735 0.784 0.216
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.1184 0.927 0.016 0.984
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.939 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 2 0.3114 0.900 0.056 0.944
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.946 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.0000 0.946 1.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.946 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 1 0.7745 0.719 0.772 0.228
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0000 0.946 1.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.0000 0.946 1.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.939 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 2 0.8144 0.697 0.252 0.748
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.939 0.000 1.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.946 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.946 1.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.946 1.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 1 0.0376 0.943 0.996 0.004
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 1 0.7219 0.755 0.800 0.200
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.939 0.000 1.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 1 0.9896 0.283 0.560 0.440
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 2 0.7376 0.754 0.208 0.792
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.0000 0.946 1.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.939 0.000 1.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.0000 0.946 1.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.0000 0.946 1.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 2 0.0376 0.937 0.004 0.996
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.0000 0.946 1.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 2 0.0000 0.939 0.000 1.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.939 0.000 1.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.946 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.946 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 1 0.6048 0.815 0.852 0.148
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.0000 0.946 1.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.939 0.000 1.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.939 0.000 1.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.0000 0.946 1.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 1 0.3733 0.889 0.928 0.072
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.0000 0.946 1.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.946 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.946 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0000 0.946 1.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.9635 0.291 0.612 0.388
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.939 0.000 1.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.939 0.000 1.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.939 0.000 1.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.939 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 2 0.0000 0.939 0.000 1.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.4562 0.853 0.096 0.904
#> A314C4E6-B245-4F10-A555-50B9B819040D 2 0.9710 0.415 0.400 0.600
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.946 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.946 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.0000 0.939 0.000 1.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.946 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.946 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 2 0.0000 0.939 0.000 1.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.939 0.000 1.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0000 0.939 0.000 1.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0000 0.939 0.000 1.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 2 0.9000 0.594 0.316 0.684
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.946 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.939 0.000 1.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.939 0.000 1.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.0000 0.946 1.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 2 0.9710 0.415 0.400 0.600
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.939 0.000 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.9129 0.457 0.672 0.328
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.5294 0.844 0.880 0.120
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.0000 0.939 0.000 1.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.946 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 2 0.0000 0.939 0.000 1.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.939 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.939 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0000 0.946 1.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 2 0.0000 0.939 0.000 1.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 2 0.5737 0.831 0.136 0.864
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.939 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.946 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.939 0.000 1.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 2 0.8909 0.608 0.308 0.692
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 2 0.9795 0.373 0.416 0.584
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.946 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 1 0.0000 0.946 1.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.939 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 2 0.6343 0.807 0.160 0.840
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.946 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.946 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0000 0.939 0.000 1.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0000 0.946 1.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 2 0.0376 0.937 0.004 0.996
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.0000 0.939 0.000 1.000
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.946 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.939 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.0000 0.946 1.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 1 0.9710 0.391 0.600 0.400
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 2 0.4121 0.7474 0.168 0.832 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.5560 0.6227 0.000 0.700 0.300
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.6111 0.7674 0.396 0.000 0.604
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.6126 0.3422 0.600 0.400 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.6111 0.7674 0.396 0.000 0.604
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.9082 0.000 1.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.5397 0.7424 0.280 0.000 0.720
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.6168 0.3137 0.588 0.412 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 2 0.3412 0.8004 0.124 0.876 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.6111 0.7674 0.396 0.000 0.604
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.9082 0.000 1.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.9082 0.000 1.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 2 0.5465 0.5523 0.288 0.712 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.6168 0.4690 0.000 0.588 0.412
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.0000 0.6542 0.000 0.000 1.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.6111 0.7674 0.396 0.000 0.604
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.0000 0.6542 0.000 0.000 1.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 3 0.6111 0.7674 0.396 0.000 0.604
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.9082 0.000 1.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.9082 0.000 1.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.7077 1.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.9082 0.000 1.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.0000 0.6542 0.000 0.000 1.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.7077 1.000 0.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.9082 0.000 1.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.7077 1.000 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.7077 1.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.7077 1.000 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.0000 0.6542 0.000 0.000 1.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 3 0.4504 0.4462 0.000 0.196 0.804
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.4654 0.7273 0.000 0.792 0.208
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 2 0.5968 0.3790 0.364 0.636 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.5621 -0.0313 0.692 0.000 0.308
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.6111 0.7674 0.396 0.000 0.604
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.7077 1.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.0000 0.6542 0.000 0.000 1.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 3 0.6111 0.7674 0.396 0.000 0.604
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.4702 0.7248 0.212 0.000 0.788
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.9082 0.000 1.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.6126 0.3422 0.600 0.400 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.9082 0.000 1.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.7077 1.000 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.4062 0.7047 0.164 0.000 0.836
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.6126 0.7635 0.400 0.000 0.600
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.6095 0.7672 0.392 0.000 0.608
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.0000 0.6542 0.000 0.000 1.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.9082 0.000 1.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 3 0.0000 0.6542 0.000 0.000 1.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.6295 0.1397 0.528 0.472 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.6126 0.7635 0.400 0.000 0.600
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.9082 0.000 1.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.6111 0.7674 0.396 0.000 0.604
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.6111 0.7674 0.396 0.000 0.604
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 2 0.4605 0.6972 0.204 0.796 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.6111 0.7674 0.396 0.000 0.604
#> B5474EEB-D585-4668-959C-38F240F55BC2 2 0.5098 0.6267 0.248 0.752 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.9082 0.000 1.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.7077 1.000 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.7077 1.000 0.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.4555 0.7210 0.200 0.000 0.800
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.6111 0.7674 0.396 0.000 0.604
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.9082 0.000 1.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.9082 0.000 1.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.6111 0.7674 0.396 0.000 0.604
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.6079 0.7666 0.388 0.000 0.612
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.6111 0.7674 0.396 0.000 0.604
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.4235 0.4269 0.824 0.000 0.176
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.7077 1.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.6126 0.7635 0.400 0.000 0.600
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.4702 0.6264 0.788 0.212 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.9082 0.000 1.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.9082 0.000 1.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.9082 0.000 1.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.9082 0.000 1.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 2 0.0237 0.9058 0.004 0.996 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 3 0.5465 0.3024 0.000 0.288 0.712
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.6111 0.3501 0.604 0.396 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.6291 -0.5379 0.532 0.000 0.468
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.7077 1.000 0.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.0000 0.9082 0.000 1.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.5098 0.1912 0.752 0.000 0.248
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.7077 1.000 0.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 2 0.1860 0.8709 0.052 0.948 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.9082 0.000 1.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0000 0.9082 0.000 1.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0000 0.9082 0.000 1.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.6126 0.3422 0.600 0.400 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.7077 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.9082 0.000 1.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.9082 0.000 1.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.6126 0.7635 0.400 0.000 0.600
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.6126 0.3422 0.600 0.400 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0592 0.9007 0.000 0.988 0.012
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.4605 0.6295 0.796 0.204 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.0000 0.6542 0.000 0.000 1.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.0000 0.9082 0.000 1.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.4002 0.4632 0.840 0.000 0.160
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 2 0.1031 0.8924 0.024 0.976 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.5363 0.6513 0.000 0.724 0.276
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.2878 0.8351 0.000 0.904 0.096
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.6111 0.7674 0.396 0.000 0.604
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 2 0.0000 0.9082 0.000 1.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 2 0.6140 0.2658 0.404 0.596 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.9082 0.000 1.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.7077 1.000 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.4062 0.7721 0.000 0.836 0.164
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.6111 0.3501 0.604 0.396 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.6111 0.3501 0.604 0.396 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.7077 1.000 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.6111 0.7674 0.396 0.000 0.604
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.9082 0.000 1.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 2 0.6026 0.3466 0.376 0.624 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.7077 1.000 0.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.7077 1.000 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0000 0.9082 0.000 1.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.0000 0.6542 0.000 0.000 1.000
#> 12F54761-4F68-4181-8421-88EA858902FC 2 0.2165 0.8607 0.064 0.936 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.0000 0.9082 0.000 1.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.7077 1.000 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.9082 0.000 1.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.6126 0.7635 0.400 0.000 0.600
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.0000 0.6542 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 2 0.4535 0.757 0.292 0.704 0.004 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.4343 0.568 0.004 0.732 0.264 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 4 0.0188 0.952 0.000 0.000 0.004 0.996
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.2593 0.661 0.904 0.080 0.016 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 4 0.0188 0.952 0.000 0.000 0.004 0.996
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.3311 0.849 0.172 0.828 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 4 0.0188 0.952 0.000 0.000 0.004 0.996
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.3791 0.501 0.796 0.200 0.000 0.004
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 2 0.4137 0.682 0.208 0.780 0.012 0.000
#> 806616FE-1855-4284-9265-42842104CB21 4 0.0188 0.952 0.000 0.000 0.004 0.996
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0804 0.834 0.008 0.980 0.012 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.3266 0.850 0.168 0.832 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 2 0.5119 0.495 0.440 0.556 0.004 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 3 0.1004 0.903 0.000 0.024 0.972 0.004
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.1557 0.928 0.000 0.000 0.944 0.056
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 4 0.0188 0.952 0.000 0.000 0.004 0.996
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.1474 0.929 0.000 0.000 0.948 0.052
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.1302 0.924 0.044 0.000 0.000 0.956
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.1022 0.827 0.000 0.968 0.032 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0469 0.831 0.000 0.988 0.012 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.4008 0.671 0.756 0.000 0.000 0.244
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.1022 0.827 0.000 0.968 0.032 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.1118 0.927 0.000 0.000 0.964 0.036
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.4761 0.478 0.628 0.000 0.000 0.372
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.4244 0.848 0.168 0.800 0.032 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.4072 0.664 0.748 0.000 0.000 0.252
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.3649 0.693 0.796 0.000 0.000 0.204
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.3975 0.673 0.760 0.000 0.000 0.240
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.1557 0.928 0.000 0.000 0.944 0.056
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 3 0.1209 0.925 0.000 0.004 0.964 0.032
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.1557 0.815 0.000 0.944 0.056 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 2 0.5147 0.452 0.460 0.536 0.004 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.0469 0.945 0.012 0.000 0.000 0.988
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.0188 0.952 0.000 0.000 0.004 0.996
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.3688 0.692 0.792 0.000 0.000 0.208
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.1389 0.929 0.000 0.000 0.952 0.048
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 3 0.5056 0.669 0.044 0.000 0.732 0.224
#> 50D620F3-5C52-42FB-89A1-6840A7444647 4 0.0188 0.952 0.000 0.000 0.004 0.996
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0921 0.829 0.000 0.972 0.028 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.3105 0.593 0.856 0.140 0.004 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.3266 0.850 0.168 0.832 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.4454 0.489 0.308 0.000 0.000 0.692
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.2408 0.901 0.036 0.000 0.920 0.044
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.0707 0.939 0.020 0.000 0.000 0.980
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 4 0.0188 0.952 0.000 0.000 0.004 0.996
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.1389 0.929 0.000 0.000 0.952 0.048
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0469 0.831 0.000 0.988 0.012 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 3 0.1284 0.917 0.000 0.012 0.964 0.024
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.4483 0.318 0.712 0.284 0.000 0.004
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.0000 0.951 0.000 0.000 0.000 1.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.3266 0.850 0.168 0.832 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.0188 0.952 0.000 0.000 0.004 0.996
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 4 0.0188 0.952 0.000 0.000 0.004 0.996
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 2 0.5326 0.606 0.380 0.604 0.016 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.0188 0.952 0.000 0.000 0.004 0.996
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.5511 -0.344 0.500 0.484 0.016 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.4050 0.849 0.168 0.808 0.024 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.4679 0.375 0.352 0.000 0.000 0.648
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.2868 0.824 0.136 0.000 0.000 0.864
#> 84E18629-1B13-4696-8E54-121ABE469CD2 4 0.0707 0.942 0.000 0.000 0.020 0.980
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.0188 0.952 0.000 0.000 0.004 0.996
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.1022 0.827 0.000 0.968 0.032 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.3266 0.850 0.168 0.832 0.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 4 0.0188 0.952 0.000 0.000 0.004 0.996
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 4 0.0188 0.952 0.000 0.000 0.004 0.996
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.0188 0.952 0.000 0.000 0.004 0.996
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.5067 0.669 0.736 0.000 0.048 0.216
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.4072 0.664 0.748 0.000 0.000 0.252
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.0188 0.949 0.004 0.000 0.000 0.996
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0592 0.684 0.984 0.000 0.016 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.3266 0.850 0.168 0.832 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.4050 0.849 0.168 0.808 0.024 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.3311 0.849 0.172 0.828 0.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.3946 0.850 0.168 0.812 0.020 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 2 0.3764 0.831 0.216 0.784 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 3 0.1584 0.901 0.000 0.036 0.952 0.012
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.1151 0.685 0.968 0.024 0.008 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.1389 0.918 0.048 0.000 0.000 0.952
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.4576 0.657 0.728 0.000 0.012 0.260
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.4599 0.831 0.212 0.760 0.028 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.0657 0.946 0.012 0.000 0.004 0.984
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.4040 0.668 0.752 0.000 0.000 0.248
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 2 0.4399 0.817 0.224 0.760 0.016 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0469 0.831 0.000 0.988 0.012 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.3636 0.850 0.172 0.820 0.008 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.1109 0.829 0.004 0.968 0.028 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.1824 0.676 0.936 0.060 0.004 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.4008 0.671 0.756 0.000 0.000 0.244
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.2565 0.842 0.056 0.912 0.032 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0469 0.831 0.000 0.988 0.012 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.0921 0.934 0.028 0.000 0.000 0.972
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.1975 0.680 0.936 0.048 0.016 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0921 0.829 0.000 0.972 0.028 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.1610 0.681 0.952 0.032 0.016 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.1557 0.928 0.000 0.000 0.944 0.056
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.4253 0.828 0.208 0.776 0.016 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.1389 0.916 0.048 0.000 0.000 0.952
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 2 0.4535 0.804 0.240 0.744 0.016 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.1389 0.819 0.000 0.952 0.048 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.1474 0.818 0.000 0.948 0.052 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 4 0.0188 0.952 0.000 0.000 0.004 0.996
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 2 0.3764 0.835 0.216 0.784 0.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.5503 -0.290 0.516 0.468 0.016 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.4105 0.850 0.156 0.812 0.032 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.4134 0.657 0.740 0.000 0.000 0.260
#> B3561356-5A80-4C79-B23A-D518425565FE 3 0.4920 0.301 0.004 0.368 0.628 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.2300 0.675 0.920 0.064 0.016 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.2335 0.675 0.920 0.060 0.020 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.3688 0.692 0.792 0.000 0.000 0.208
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 4 0.0188 0.952 0.000 0.000 0.004 0.996
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.1022 0.827 0.000 0.968 0.032 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.4999 -0.373 0.508 0.492 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.3400 0.699 0.820 0.000 0.000 0.180
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.3444 0.739 0.184 0.000 0.000 0.816
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.4253 0.828 0.208 0.776 0.016 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.1637 0.925 0.000 0.000 0.940 0.060
#> 12F54761-4F68-4181-8421-88EA858902FC 2 0.4057 0.727 0.160 0.812 0.028 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.3649 0.837 0.204 0.796 0.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.3801 0.686 0.780 0.000 0.000 0.220
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.1022 0.827 0.000 0.968 0.032 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.0000 0.951 0.000 0.000 0.000 1.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.1118 0.927 0.000 0.000 0.964 0.036
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.3918 0.6132 0.804 0.096 0.000 0.100 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.5644 0.3797 0.440 0.484 0.000 0.076 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.0000 0.9401 0.000 0.000 1.000 0.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.3612 0.4480 0.732 0.000 0.000 0.268 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.9401 0.000 0.000 1.000 0.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 1 0.3969 0.5768 0.692 0.304 0.000 0.000 0.004
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0000 0.9401 0.000 0.000 1.000 0.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.4201 0.0414 0.408 0.000 0.000 0.592 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 2 0.5778 0.4882 0.132 0.596 0.000 0.272 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0000 0.9401 0.000 0.000 1.000 0.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.3876 0.6308 0.316 0.684 0.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 1 0.3766 0.5881 0.728 0.268 0.000 0.000 0.004
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.3355 0.5890 0.804 0.012 0.000 0.184 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 5 0.3949 0.5164 0.000 0.300 0.004 0.000 0.696
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.0162 0.8673 0.000 0.000 0.004 0.000 0.996
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0000 0.9401 0.000 0.000 1.000 0.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0162 0.8673 0.000 0.000 0.004 0.000 0.996
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 3 0.3039 0.7837 0.000 0.000 0.808 0.192 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0510 0.7450 0.016 0.984 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.3452 0.6932 0.244 0.756 0.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.1671 0.7839 0.000 0.000 0.076 0.924 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0510 0.7540 0.016 0.984 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.0162 0.8673 0.000 0.000 0.004 0.000 0.996
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.3921 0.7286 0.072 0.000 0.128 0.800 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 1 0.4403 0.4860 0.560 0.436 0.000 0.000 0.004
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.1792 0.7806 0.000 0.000 0.084 0.916 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.1671 0.7839 0.000 0.000 0.076 0.924 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.1671 0.7839 0.000 0.000 0.076 0.924 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.0162 0.8673 0.000 0.000 0.004 0.000 0.996
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 5 0.0451 0.8633 0.000 0.008 0.004 0.000 0.988
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0510 0.7313 0.016 0.984 0.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.3452 0.5581 0.756 0.000 0.000 0.244 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 3 0.0162 0.9388 0.000 0.000 0.996 0.004 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.0000 0.9401 0.000 0.000 1.000 0.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.1671 0.7839 0.000 0.000 0.076 0.924 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0162 0.8673 0.000 0.000 0.004 0.000 0.996
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 5 0.3798 0.6947 0.000 0.000 0.064 0.128 0.808
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0000 0.9401 0.000 0.000 1.000 0.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.1043 0.7578 0.040 0.960 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.4297 -0.0456 0.472 0.000 0.000 0.528 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 1 0.3928 0.5775 0.700 0.296 0.000 0.000 0.004
#> 692C65BB-BF32-4846-806B-01A285BED1B9 3 0.4777 0.5837 0.052 0.000 0.680 0.268 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 5 0.4449 0.6584 0.080 0.000 0.000 0.168 0.752
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.0162 0.9388 0.000 0.000 0.996 0.004 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0000 0.9401 0.000 0.000 1.000 0.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0162 0.8673 0.000 0.000 0.004 0.000 0.996
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.3534 0.6864 0.256 0.744 0.000 0.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 5 0.4730 0.6202 0.052 0.260 0.000 0.000 0.688
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.4297 0.1210 0.528 0.000 0.000 0.472 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.0162 0.9388 0.000 0.000 0.996 0.004 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 1 0.2536 0.6208 0.868 0.128 0.000 0.000 0.004
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0000 0.9401 0.000 0.000 1.000 0.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0000 0.9401 0.000 0.000 1.000 0.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.2761 0.5929 0.872 0.024 0.000 0.104 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.0000 0.9401 0.000 0.000 1.000 0.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.2448 0.6006 0.892 0.020 0.000 0.088 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 1 0.4403 0.4860 0.560 0.436 0.000 0.000 0.004
#> A533C39D-CE42-42AD-92AD-549157A43139 3 0.5160 0.4235 0.056 0.000 0.608 0.336 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 3 0.3242 0.7526 0.000 0.000 0.784 0.216 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.0955 0.9184 0.000 0.028 0.968 0.000 0.004
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0000 0.9401 0.000 0.000 1.000 0.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0880 0.7579 0.032 0.968 0.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 1 0.3861 0.5818 0.712 0.284 0.000 0.000 0.004
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0000 0.9401 0.000 0.000 1.000 0.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0000 0.9401 0.000 0.000 1.000 0.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.0000 0.9401 0.000 0.000 1.000 0.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.3051 0.7422 0.000 0.000 0.076 0.864 0.060
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.1732 0.7828 0.000 0.000 0.080 0.920 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.0162 0.9388 0.000 0.000 0.996 0.004 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 4 0.3003 0.6358 0.188 0.000 0.000 0.812 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 1 0.3196 0.6049 0.804 0.192 0.000 0.000 0.004
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 1 0.4397 0.4878 0.564 0.432 0.000 0.000 0.004
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 1 0.3814 0.6145 0.816 0.116 0.000 0.064 0.004
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 1 0.4397 0.4878 0.564 0.432 0.000 0.000 0.004
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.3063 0.6298 0.864 0.096 0.000 0.036 0.004
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 5 0.4555 0.5258 0.020 0.344 0.000 0.000 0.636
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.2966 0.5793 0.184 0.000 0.000 0.816 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 3 0.2230 0.8565 0.000 0.000 0.884 0.116 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.3303 0.7557 0.076 0.000 0.076 0.848 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.5520 -0.1226 0.560 0.364 0.000 0.076 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 3 0.2344 0.8791 0.032 0.000 0.904 0.064 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.1732 0.7828 0.000 0.000 0.080 0.920 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.3226 0.6218 0.852 0.088 0.000 0.060 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.3636 0.6744 0.272 0.728 0.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 1 0.4390 0.4914 0.568 0.428 0.000 0.000 0.004
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.4088 0.5728 0.368 0.632 0.000 0.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.4297 -0.0717 0.472 0.000 0.000 0.528 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.1732 0.7826 0.000 0.000 0.080 0.920 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 1 0.4403 0.4860 0.560 0.436 0.000 0.000 0.004
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.3452 0.6932 0.244 0.756 0.000 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.2377 0.8449 0.000 0.000 0.872 0.128 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 4 0.4528 0.2439 0.444 0.008 0.000 0.548 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.1341 0.7557 0.056 0.944 0.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 4 0.4878 0.2407 0.440 0.024 0.000 0.536 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.0162 0.8673 0.000 0.000 0.004 0.000 0.996
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.3980 0.5037 0.796 0.128 0.000 0.076 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 3 0.0290 0.9372 0.000 0.000 0.992 0.008 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.3033 0.5839 0.864 0.052 0.000 0.084 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0162 0.7482 0.004 0.996 0.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0404 0.7356 0.012 0.988 0.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.0000 0.9401 0.000 0.000 1.000 0.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.5240 0.5917 0.664 0.252 0.000 0.080 0.004
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.2813 0.5909 0.868 0.024 0.000 0.108 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 1 0.4403 0.4860 0.560 0.436 0.000 0.000 0.004
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.3242 0.7566 0.072 0.000 0.076 0.852 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 5 0.4150 0.4630 0.000 0.388 0.000 0.000 0.612
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.4726 0.0644 0.580 0.020 0.000 0.400 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 4 0.5761 0.1146 0.464 0.064 0.000 0.464 0.008
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.1671 0.7839 0.000 0.000 0.076 0.924 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0000 0.9401 0.000 0.000 1.000 0.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0510 0.7540 0.016 0.984 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.4510 0.2762 0.560 0.008 0.000 0.432 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.1671 0.7839 0.000 0.000 0.076 0.924 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 3 0.3177 0.7247 0.000 0.000 0.792 0.208 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.3051 0.5807 0.864 0.060 0.000 0.076 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.0162 0.8673 0.000 0.000 0.004 0.000 0.996
#> 12F54761-4F68-4181-8421-88EA858902FC 2 0.4262 0.5006 0.440 0.560 0.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.4067 0.6120 0.748 0.228 0.000 0.020 0.004
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.1671 0.7839 0.000 0.000 0.076 0.924 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0162 0.7487 0.004 0.996 0.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.0404 0.9353 0.000 0.000 0.988 0.012 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0162 0.8673 0.000 0.000 0.004 0.000 0.996
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.3552 0.7430 0.804 0.020 0.000 0.028 0.000 0.148
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.3941 0.6328 0.748 0.216 0.000 0.016 0.012 0.008
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.0000 0.8784 0.000 0.000 1.000 0.000 0.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.1167 0.8258 0.960 0.008 0.000 0.012 0.000 0.020
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.8784 0.000 0.000 1.000 0.000 0.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 6 0.1333 0.8764 0.048 0.008 0.000 0.000 0.000 0.944
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0146 0.8772 0.004 0.000 0.996 0.000 0.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.4109 -0.0690 0.412 0.000 0.000 0.576 0.000 0.012
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 2 0.1245 0.9409 0.016 0.952 0.000 0.032 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0000 0.8784 0.000 0.000 1.000 0.000 0.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.3081 0.7173 0.220 0.776 0.000 0.000 0.000 0.004
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 6 0.1970 0.8677 0.060 0.028 0.000 0.000 0.000 0.912
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 6 0.4367 0.3777 0.364 0.000 0.000 0.032 0.000 0.604
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 5 0.2351 0.8881 0.008 0.028 0.000 0.016 0.908 0.040
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.0000 0.9158 0.000 0.000 0.000 0.000 1.000 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0000 0.8784 0.000 0.000 1.000 0.000 0.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0000 0.9158 0.000 0.000 0.000 0.000 1.000 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.3797 0.1300 0.000 0.000 0.420 0.580 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0260 0.9622 0.000 0.992 0.000 0.000 0.000 0.008
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0260 0.9625 0.008 0.992 0.000 0.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0405 0.8561 0.004 0.000 0.008 0.988 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0146 0.9641 0.000 0.996 0.000 0.000 0.000 0.004
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.1036 0.9073 0.004 0.000 0.000 0.008 0.964 0.024
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.3915 0.7349 0.128 0.004 0.092 0.776 0.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 6 0.0909 0.8692 0.020 0.012 0.000 0.000 0.000 0.968
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0458 0.8594 0.000 0.000 0.016 0.984 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0458 0.8594 0.000 0.000 0.016 0.984 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0458 0.8594 0.000 0.000 0.016 0.984 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.0000 0.9158 0.000 0.000 0.000 0.000 1.000 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 5 0.1879 0.8993 0.016 0.008 0.000 0.016 0.932 0.028
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0547 0.9516 0.000 0.980 0.000 0.000 0.000 0.020
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.4617 0.5224 0.644 0.004 0.000 0.056 0.000 0.296
#> F5940915-4123-49B3-95EE-4A0412BE8C30 3 0.0717 0.8669 0.008 0.000 0.976 0.016 0.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.0951 0.8637 0.004 0.000 0.968 0.008 0.000 0.020
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0458 0.8594 0.000 0.000 0.016 0.984 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0000 0.9158 0.000 0.000 0.000 0.000 1.000 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 5 0.3536 0.6195 0.004 0.000 0.008 0.252 0.736 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0146 0.8772 0.004 0.000 0.996 0.000 0.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0146 0.9641 0.000 0.996 0.000 0.000 0.000 0.004
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.3791 0.6954 0.732 0.000 0.000 0.236 0.000 0.032
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 6 0.1434 0.8762 0.048 0.012 0.000 0.000 0.000 0.940
#> 692C65BB-BF32-4846-806B-01A285BED1B9 3 0.5516 0.1751 0.116 0.004 0.492 0.388 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 5 0.5226 0.5587 0.172 0.004 0.000 0.196 0.628 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.0000 0.8784 0.000 0.000 1.000 0.000 0.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0000 0.8784 0.000 0.000 1.000 0.000 0.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0000 0.9158 0.000 0.000 0.000 0.000 1.000 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0260 0.9625 0.008 0.992 0.000 0.000 0.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 6 0.3697 0.5097 0.004 0.000 0.000 0.016 0.248 0.732
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 6 0.5408 0.2449 0.116 0.000 0.000 0.408 0.000 0.476
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.0000 0.8784 0.000 0.000 1.000 0.000 0.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 6 0.2445 0.8300 0.108 0.020 0.000 0.000 0.000 0.872
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0000 0.8784 0.000 0.000 1.000 0.000 0.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0000 0.8784 0.000 0.000 1.000 0.000 0.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.1168 0.8237 0.956 0.016 0.000 0.000 0.000 0.028
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.0000 0.8784 0.000 0.000 1.000 0.000 0.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0909 0.8251 0.968 0.012 0.000 0.000 0.000 0.020
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 6 0.0972 0.8759 0.028 0.008 0.000 0.000 0.000 0.964
#> A533C39D-CE42-42AD-92AD-549157A43139 3 0.5578 0.0365 0.120 0.004 0.448 0.428 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.3868 -0.1330 0.000 0.000 0.496 0.504 0.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.2058 0.8245 0.004 0.000 0.916 0.016 0.008 0.056
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0000 0.8784 0.000 0.000 1.000 0.000 0.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0146 0.9641 0.000 0.996 0.000 0.000 0.000 0.004
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 6 0.1500 0.8752 0.052 0.012 0.000 0.000 0.000 0.936
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0000 0.8784 0.000 0.000 1.000 0.000 0.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0146 0.8772 0.004 0.000 0.996 0.000 0.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.0146 0.8772 0.004 0.000 0.996 0.000 0.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0551 0.8499 0.008 0.004 0.000 0.984 0.000 0.004
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0363 0.8582 0.000 0.000 0.012 0.988 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.0000 0.8784 0.000 0.000 1.000 0.000 0.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.2003 0.7743 0.884 0.000 0.000 0.116 0.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 6 0.2122 0.8567 0.076 0.024 0.000 0.000 0.000 0.900
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 6 0.0858 0.8748 0.028 0.004 0.000 0.000 0.000 0.968
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 1 0.3641 0.6294 0.732 0.020 0.000 0.000 0.000 0.248
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 6 0.1074 0.8750 0.028 0.012 0.000 0.000 0.000 0.960
#> D891BCA1-0323-4277-BAF7-6F505377EA45 6 0.4427 0.2569 0.412 0.016 0.000 0.008 0.000 0.564
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 5 0.4812 0.6185 0.008 0.080 0.000 0.000 0.660 0.252
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.3930 0.4069 0.576 0.000 0.000 0.420 0.000 0.004
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 3 0.4076 0.4546 0.012 0.004 0.636 0.348 0.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.2925 0.7652 0.148 0.004 0.016 0.832 0.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.0993 0.8241 0.964 0.024 0.000 0.000 0.000 0.012
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 3 0.5121 0.4231 0.096 0.004 0.596 0.304 0.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.1168 0.8528 0.028 0.000 0.016 0.956 0.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.3688 0.6208 0.724 0.020 0.000 0.000 0.000 0.256
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0458 0.9586 0.016 0.984 0.000 0.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 6 0.0972 0.8759 0.028 0.008 0.000 0.000 0.000 0.964
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.2597 0.7916 0.176 0.824 0.000 0.000 0.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.4199 0.5040 0.600 0.000 0.000 0.380 0.000 0.020
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0458 0.8594 0.000 0.000 0.016 0.984 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 6 0.1257 0.8652 0.020 0.028 0.000 0.000 0.000 0.952
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0547 0.9568 0.020 0.980 0.000 0.000 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.4394 0.4173 0.008 0.000 0.608 0.364 0.000 0.020
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0653 0.8216 0.980 0.004 0.000 0.012 0.000 0.004
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0146 0.9631 0.004 0.996 0.000 0.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.1074 0.8094 0.960 0.012 0.000 0.028 0.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.0000 0.9158 0.000 0.000 0.000 0.000 1.000 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.1003 0.8249 0.964 0.016 0.000 0.000 0.000 0.020
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 3 0.0937 0.8514 0.000 0.000 0.960 0.040 0.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.1003 0.8249 0.964 0.016 0.000 0.000 0.000 0.020
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0146 0.9641 0.000 0.996 0.000 0.000 0.000 0.004
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0260 0.9622 0.000 0.992 0.000 0.000 0.000 0.008
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.0000 0.8784 0.000 0.000 1.000 0.000 0.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 6 0.1850 0.8708 0.052 0.008 0.000 0.016 0.000 0.924
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.1926 0.8059 0.912 0.020 0.000 0.000 0.000 0.068
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 6 0.0725 0.8641 0.012 0.012 0.000 0.000 0.000 0.976
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.2544 0.7687 0.140 0.004 0.004 0.852 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 5 0.2748 0.8650 0.004 0.020 0.000 0.012 0.872 0.092
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0291 0.8218 0.992 0.000 0.000 0.004 0.000 0.004
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.1078 0.8126 0.964 0.008 0.000 0.012 0.016 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0458 0.8594 0.000 0.000 0.016 0.984 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0000 0.8784 0.000 0.000 1.000 0.000 0.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0146 0.9641 0.000 0.996 0.000 0.000 0.000 0.004
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.4131 0.4989 0.600 0.000 0.000 0.384 0.000 0.016
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0692 0.8500 0.020 0.004 0.000 0.976 0.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 3 0.3869 0.0393 0.000 0.000 0.500 0.500 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.1657 0.8134 0.928 0.016 0.000 0.000 0.000 0.056
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.0000 0.9158 0.000 0.000 0.000 0.000 1.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.3351 0.5361 0.712 0.288 0.000 0.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 6 0.1625 0.8724 0.060 0.012 0.000 0.000 0.000 0.928
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0603 0.8505 0.016 0.004 0.000 0.980 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0260 0.9622 0.000 0.992 0.000 0.000 0.000 0.008
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.3489 0.5737 0.004 0.000 0.708 0.288 0.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0000 0.9158 0.000 0.000 0.000 0.000 1.000 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["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 17548 rows and 122 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 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 0.592 0.818 0.899 0.4570 0.498 0.498
#> 3 3 0.641 0.716 0.852 0.3847 0.741 0.525
#> 4 4 0.744 0.796 0.872 0.1186 0.911 0.747
#> 5 5 0.757 0.780 0.865 0.0386 0.976 0.913
#> 6 6 0.740 0.740 0.850 0.0355 0.989 0.958
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.1184 0.964 0.984 0.016
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.9896 0.483 0.440 0.560
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.8207 0.549 0.744 0.256
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.1184 0.964 0.984 0.016
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 2 0.9881 0.522 0.436 0.564
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0376 0.788 0.004 0.996
#> 9264567D-4524-46AF-A851-C091C3CD76CF 2 0.9881 0.522 0.436 0.564
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.1184 0.964 0.984 0.016
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.3733 0.909 0.928 0.072
#> 806616FE-1855-4284-9265-42842104CB21 2 0.9881 0.522 0.436 0.564
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.787 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0376 0.788 0.004 0.996
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.1184 0.964 0.984 0.016
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.7950 0.705 0.240 0.760
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 2 0.9909 0.507 0.444 0.556
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 2 0.9881 0.522 0.436 0.564
#> 853120F0-857B-4108-9EC8-727189630C5F 2 0.9909 0.507 0.444 0.556
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.966 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.787 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.787 0.000 1.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.966 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.787 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 2 0.9909 0.507 0.444 0.556
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.966 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0376 0.788 0.004 0.996
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.966 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.966 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.966 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 2 0.9909 0.507 0.444 0.556
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.7950 0.705 0.240 0.760
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.787 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.1184 0.964 0.984 0.016
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.966 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.1414 0.957 0.980 0.020
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.966 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 2 0.9909 0.507 0.444 0.556
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.1633 0.954 0.976 0.024
#> 50D620F3-5C52-42FB-89A1-6840A7444647 2 0.9881 0.522 0.436 0.564
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.787 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.1184 0.964 0.984 0.016
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0376 0.788 0.004 0.996
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.966 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.966 1.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.1843 0.951 0.972 0.028
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.9881 0.522 0.436 0.564
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.9909 0.507 0.444 0.556
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.8955 0.654 0.312 0.688
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.5519 0.757 0.128 0.872
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.1184 0.964 0.984 0.016
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.1843 0.951 0.972 0.028
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.3274 0.778 0.060 0.940
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.1843 0.951 0.972 0.028
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 2 0.9881 0.522 0.436 0.564
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.1184 0.964 0.984 0.016
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.1843 0.951 0.972 0.028
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.3879 0.902 0.924 0.076
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0376 0.788 0.004 0.996
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.966 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.966 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.5519 0.757 0.128 0.872
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.1843 0.951 0.972 0.028
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.787 0.000 1.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0376 0.788 0.004 0.996
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.8207 0.549 0.744 0.256
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.9881 0.522 0.436 0.564
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.1843 0.951 0.972 0.028
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.966 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.966 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0672 0.964 0.992 0.008
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.1184 0.964 0.984 0.016
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0376 0.788 0.004 0.996
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0376 0.788 0.004 0.996
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.3274 0.778 0.060 0.940
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.787 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.1184 0.964 0.984 0.016
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.787 0.000 1.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.1184 0.964 0.984 0.016
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.966 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.966 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.9000 0.650 0.316 0.684
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.966 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.966 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.1184 0.964 0.984 0.016
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.787 0.000 1.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0376 0.788 0.004 0.996
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.8955 0.654 0.312 0.688
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.1184 0.964 0.984 0.016
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.966 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0376 0.788 0.004 0.996
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.3274 0.778 0.060 0.940
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.1633 0.954 0.976 0.024
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.966 1.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.787 0.000 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.966 1.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 2 0.9909 0.507 0.444 0.556
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.9983 0.383 0.476 0.524
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.966 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.1184 0.964 0.984 0.016
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.787 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.787 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.8207 0.549 0.744 0.256
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.1184 0.964 0.984 0.016
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.1184 0.964 0.984 0.016
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.787 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.966 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0938 0.786 0.012 0.988
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.966 1.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.966 1.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.966 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.9881 0.522 0.436 0.564
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.787 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.1184 0.964 0.984 0.016
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.966 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.966 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.9000 0.650 0.316 0.684
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.6438 0.746 0.836 0.164
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.1184 0.964 0.984 0.016
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.1184 0.964 0.984 0.016
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.966 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.787 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.1633 0.954 0.976 0.024
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.9909 0.507 0.444 0.556
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.1182 0.9398 0.976 0.012 0.012
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 3 0.6274 0.0811 0.000 0.456 0.544
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.2318 0.5362 0.028 0.028 0.944
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 3 0.6688 0.2451 0.408 0.012 0.580
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.6603 0.4395 0.020 0.332 0.648
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0475 0.8943 0.004 0.992 0.004
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.6603 0.4395 0.020 0.332 0.648
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.1015 0.9398 0.980 0.012 0.008
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.2680 0.8918 0.924 0.068 0.008
#> 806616FE-1855-4284-9265-42842104CB21 3 0.6603 0.4395 0.020 0.332 0.648
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0424 0.8971 0.000 0.992 0.008
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0475 0.8943 0.004 0.992 0.004
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.1182 0.9398 0.976 0.012 0.012
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.6357 0.5074 0.020 0.684 0.296
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.6702 0.4453 0.024 0.328 0.648
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.6603 0.4395 0.020 0.332 0.648
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.6702 0.4453 0.024 0.328 0.648
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.9417 1.000 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0424 0.8971 0.000 0.992 0.008
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0424 0.8971 0.000 0.992 0.008
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.9417 1.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0424 0.8971 0.000 0.992 0.008
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.6702 0.4453 0.024 0.328 0.648
#> F5A814F6-E824-4DB2-8497-4B99E151D450 3 0.6168 0.2773 0.412 0.000 0.588
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0475 0.8943 0.004 0.992 0.004
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.9417 1.000 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.9417 1.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.9417 1.000 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.6702 0.4453 0.024 0.328 0.648
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.6357 0.5074 0.020 0.684 0.296
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0424 0.8971 0.000 0.992 0.008
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.1182 0.9398 0.976 0.012 0.012
#> F5940915-4123-49B3-95EE-4A0412BE8C30 3 0.6244 0.2048 0.440 0.000 0.560
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.2356 0.9071 0.928 0.000 0.072
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.9417 1.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.6702 0.4453 0.024 0.328 0.648
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.2860 0.8945 0.912 0.004 0.084
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.6603 0.4395 0.020 0.332 0.648
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0424 0.8971 0.000 0.992 0.008
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.2651 0.9156 0.928 0.012 0.060
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0475 0.8943 0.004 0.992 0.004
#> 692C65BB-BF32-4846-806B-01A285BED1B9 3 0.6168 0.2773 0.412 0.000 0.588
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.4931 0.4669 0.232 0.000 0.768
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.2774 0.9020 0.920 0.008 0.072
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.6603 0.4395 0.020 0.332 0.648
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.6702 0.4453 0.024 0.328 0.648
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.6467 0.2756 0.008 0.604 0.388
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.4342 0.7703 0.024 0.856 0.120
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.1015 0.9398 0.980 0.012 0.008
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.2774 0.9020 0.920 0.008 0.072
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.2448 0.8488 0.000 0.924 0.076
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.2774 0.9020 0.920 0.008 0.072
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.6603 0.4395 0.020 0.332 0.648
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.3989 0.8554 0.864 0.012 0.124
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.2774 0.9020 0.920 0.008 0.072
#> B5474EEB-D585-4668-959C-38F240F55BC2 3 0.7600 0.3427 0.344 0.056 0.600
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0475 0.8943 0.004 0.992 0.004
#> A533C39D-CE42-42AD-92AD-549157A43139 3 0.6154 0.2840 0.408 0.000 0.592
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0237 0.9418 0.996 0.000 0.004
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.4342 0.7703 0.024 0.856 0.120
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.2774 0.9020 0.920 0.008 0.072
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0424 0.8971 0.000 0.992 0.008
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0475 0.8943 0.004 0.992 0.004
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.2318 0.5362 0.028 0.028 0.944
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.6603 0.4395 0.020 0.332 0.648
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.2774 0.9020 0.920 0.008 0.072
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.9417 1.000 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.9417 1.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.4235 0.7955 0.824 0.000 0.176
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.3120 0.9006 0.908 0.012 0.080
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0475 0.8943 0.004 0.992 0.004
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0475 0.8943 0.004 0.992 0.004
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.2448 0.8488 0.000 0.924 0.076
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0424 0.8971 0.000 0.992 0.008
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.1015 0.9398 0.980 0.012 0.008
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0424 0.8971 0.000 0.992 0.008
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.2651 0.9156 0.928 0.012 0.060
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0237 0.9418 0.996 0.000 0.004
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 3 0.6154 0.2840 0.408 0.000 0.592
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.6483 0.2627 0.008 0.600 0.392
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 3 0.6154 0.2840 0.408 0.000 0.592
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.5397 0.5568 0.720 0.000 0.280
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.1182 0.9398 0.976 0.012 0.012
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0424 0.8971 0.000 0.992 0.008
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0475 0.8943 0.004 0.992 0.004
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.6467 0.2756 0.008 0.604 0.388
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.2651 0.9156 0.928 0.012 0.060
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.9417 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0475 0.8943 0.004 0.992 0.004
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.2356 0.8516 0.000 0.928 0.072
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.2860 0.8945 0.912 0.004 0.084
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 3 0.6154 0.2787 0.408 0.000 0.592
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0424 0.8971 0.000 0.992 0.008
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 3 0.6095 0.3048 0.392 0.000 0.608
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.6702 0.4453 0.024 0.328 0.648
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 3 0.7979 0.1473 0.060 0.440 0.500
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0424 0.9404 0.992 0.000 0.008
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.3989 0.8554 0.864 0.012 0.124
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0424 0.8971 0.000 0.992 0.008
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0424 0.8971 0.000 0.992 0.008
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.2318 0.5362 0.028 0.028 0.944
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.1015 0.9398 0.980 0.012 0.008
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.1182 0.9398 0.976 0.012 0.012
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0424 0.8971 0.000 0.992 0.008
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 3 0.6168 0.2773 0.412 0.000 0.588
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.1015 0.8891 0.008 0.980 0.012
#> F900E9BE-2400-4451-9434-EE8BC513BA94 3 0.6095 0.3048 0.392 0.000 0.608
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 3 0.6095 0.3048 0.392 0.000 0.608
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.9417 1.000 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.6603 0.4395 0.020 0.332 0.648
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0424 0.8971 0.000 0.992 0.008
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.1182 0.9398 0.976 0.012 0.012
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0424 0.9413 0.992 0.000 0.008
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.9417 1.000 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.6483 0.2627 0.008 0.600 0.392
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.2261 0.5383 0.068 0.000 0.932
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.2939 0.9065 0.916 0.012 0.072
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.1015 0.9398 0.980 0.012 0.008
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.9417 1.000 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0424 0.8971 0.000 0.992 0.008
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.2860 0.8945 0.912 0.004 0.084
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.6702 0.4453 0.024 0.328 0.648
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.2197 0.876 0.080 0.000 0.004 0.916
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 3 0.6534 0.626 0.148 0.220 0.632 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.5536 0.259 0.384 0.000 0.592 0.024
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.4608 0.830 0.692 0.000 0.004 0.304
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.801 0.000 0.000 1.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.1022 0.905 0.032 0.968 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0000 0.801 0.000 0.000 1.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.2011 0.877 0.080 0.000 0.000 0.920
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.3239 0.836 0.068 0.052 0.000 0.880
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0000 0.801 0.000 0.000 1.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.1022 0.905 0.032 0.968 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.2197 0.876 0.080 0.000 0.004 0.916
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 3 0.4746 0.421 0.000 0.368 0.632 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.3172 0.790 0.160 0.000 0.840 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0000 0.801 0.000 0.000 1.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.3172 0.790 0.160 0.000 0.840 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.891 0.000 0.000 0.000 1.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.891 0.000 0.000 0.000 1.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.3172 0.790 0.160 0.000 0.840 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.4697 0.879 0.644 0.000 0.000 0.356
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.1022 0.905 0.032 0.968 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.891 0.000 0.000 0.000 1.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.891 0.000 0.000 0.000 1.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.891 0.000 0.000 0.000 1.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.3172 0.790 0.160 0.000 0.840 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 3 0.5024 0.424 0.008 0.360 0.632 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.2197 0.876 0.080 0.000 0.004 0.916
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.4830 0.835 0.608 0.000 0.000 0.392
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.1867 0.852 0.000 0.000 0.072 0.928
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.891 0.000 0.000 0.000 1.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.3172 0.790 0.160 0.000 0.840 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.2149 0.838 0.000 0.000 0.088 0.912
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0000 0.801 0.000 0.000 1.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.2944 0.842 0.128 0.000 0.004 0.868
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.1022 0.905 0.032 0.968 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.4697 0.879 0.644 0.000 0.000 0.356
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.4004 0.725 0.812 0.000 0.024 0.164
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.2011 0.846 0.000 0.000 0.080 0.920
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0000 0.801 0.000 0.000 1.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.3172 0.790 0.160 0.000 0.840 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 3 0.5256 0.607 0.040 0.260 0.700 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.5821 0.165 0.032 0.536 0.432 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 4 0.2011 0.877 0.080 0.000 0.000 0.920
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.2011 0.846 0.000 0.000 0.080 0.920
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.4643 0.430 0.000 0.656 0.344 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.2011 0.846 0.000 0.000 0.080 0.920
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0000 0.801 0.000 0.000 1.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 4 0.3710 0.756 0.192 0.000 0.004 0.804
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.2011 0.846 0.000 0.000 0.080 0.920
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.5562 0.819 0.712 0.032 0.020 0.236
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.1022 0.905 0.032 0.968 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.4679 0.881 0.648 0.000 0.000 0.352
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0188 0.890 0.004 0.000 0.000 0.996
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.5821 0.165 0.032 0.536 0.432 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.2011 0.846 0.000 0.000 0.080 0.920
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.1022 0.905 0.032 0.968 0.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.5536 0.259 0.384 0.000 0.592 0.024
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0000 0.801 0.000 0.000 1.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.2011 0.846 0.000 0.000 0.080 0.920
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0000 0.891 0.000 0.000 0.000 1.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.891 0.000 0.000 0.000 1.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.4370 0.667 0.156 0.000 0.044 0.800
#> 352471DC-A881-4EA8-B646-EB1200291893 4 0.3208 0.822 0.148 0.000 0.004 0.848
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.1022 0.905 0.032 0.968 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.1022 0.905 0.032 0.968 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.4643 0.430 0.000 0.656 0.344 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 4 0.2011 0.877 0.080 0.000 0.000 0.920
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.2944 0.842 0.128 0.000 0.004 0.868
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0188 0.890 0.004 0.000 0.000 0.996
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.4679 0.881 0.648 0.000 0.000 0.352
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 3 0.5227 0.612 0.040 0.256 0.704 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.4679 0.881 0.648 0.000 0.000 0.352
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.4477 0.163 0.312 0.000 0.000 0.688
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.2197 0.876 0.080 0.000 0.004 0.916
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.1022 0.905 0.032 0.968 0.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 3 0.5256 0.607 0.040 0.260 0.700 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.2944 0.842 0.128 0.000 0.004 0.868
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.891 0.000 0.000 0.000 1.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.1022 0.905 0.032 0.968 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.4624 0.439 0.000 0.660 0.340 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.2149 0.838 0.000 0.000 0.088 0.912
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.4382 0.871 0.704 0.000 0.000 0.296
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.4277 0.875 0.720 0.000 0.000 0.280
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.3172 0.790 0.160 0.000 0.840 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 3 0.7010 0.596 0.240 0.184 0.576 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0336 0.889 0.000 0.000 0.008 0.992
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 4 0.3710 0.756 0.192 0.000 0.004 0.804
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.5536 0.259 0.384 0.000 0.592 0.024
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 4 0.2011 0.877 0.080 0.000 0.000 0.920
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.2197 0.876 0.080 0.000 0.004 0.916
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.4697 0.879 0.644 0.000 0.000 0.356
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.2499 0.873 0.032 0.920 0.044 0.004
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.4277 0.875 0.720 0.000 0.000 0.280
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.4277 0.875 0.720 0.000 0.000 0.280
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.891 0.000 0.000 0.000 1.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0000 0.801 0.000 0.000 1.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.2197 0.876 0.080 0.000 0.004 0.916
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0336 0.889 0.008 0.000 0.000 0.992
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0000 0.891 0.000 0.000 0.000 1.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 3 0.5227 0.612 0.040 0.256 0.704 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.2149 0.430 0.912 0.000 0.088 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 4 0.3105 0.829 0.140 0.000 0.004 0.856
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 4 0.2011 0.877 0.080 0.000 0.000 0.920
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.891 0.000 0.000 0.000 1.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.2149 0.838 0.000 0.000 0.088 0.912
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.3172 0.790 0.160 0.000 0.840 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.1892 0.886 0.080 0.000 0.000 0.916 0.004
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 3 0.6018 0.534 0.172 0.208 0.612 0.000 0.008
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.5920 0.271 0.384 0.000 0.536 0.024 0.056
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.3928 0.772 0.700 0.000 0.000 0.296 0.004
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.1270 0.672 0.000 0.000 0.948 0.000 0.052
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.1845 0.883 0.056 0.928 0.000 0.000 0.016
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0162 0.689 0.000 0.000 0.996 0.000 0.004
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.1732 0.887 0.080 0.000 0.000 0.920 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.2949 0.854 0.076 0.028 0.000 0.880 0.016
#> 806616FE-1855-4284-9265-42842104CB21 3 0.1270 0.672 0.000 0.000 0.948 0.000 0.052
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.897 0.000 1.000 0.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.1740 0.884 0.056 0.932 0.000 0.000 0.012
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.1892 0.886 0.080 0.000 0.000 0.916 0.004
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 5 0.7046 0.184 0.024 0.356 0.188 0.000 0.432
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.2561 0.806 0.000 0.000 0.144 0.000 0.856
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.1270 0.672 0.000 0.000 0.948 0.000 0.052
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.2561 0.806 0.000 0.000 0.144 0.000 0.856
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.902 0.000 0.000 0.000 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.897 0.000 1.000 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.897 0.000 1.000 0.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.902 0.000 0.000 0.000 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.897 0.000 1.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.0703 0.717 0.000 0.000 0.024 0.000 0.976
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.3707 0.878 0.716 0.000 0.000 0.284 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.1845 0.883 0.056 0.928 0.000 0.000 0.016
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.902 0.000 0.000 0.000 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.902 0.000 0.000 0.000 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.902 0.000 0.000 0.000 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.2561 0.806 0.000 0.000 0.144 0.000 0.856
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 5 0.7172 0.187 0.032 0.348 0.188 0.000 0.432
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.897 0.000 1.000 0.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.1892 0.886 0.080 0.000 0.000 0.916 0.004
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.4161 0.746 0.608 0.000 0.000 0.392 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.1845 0.869 0.000 0.000 0.016 0.928 0.056
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.902 0.000 0.000 0.000 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.2561 0.806 0.000 0.000 0.144 0.000 0.856
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.2193 0.857 0.000 0.000 0.028 0.912 0.060
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0162 0.689 0.000 0.000 0.996 0.000 0.004
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.897 0.000 1.000 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.2536 0.855 0.128 0.000 0.000 0.868 0.004
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.1740 0.884 0.056 0.932 0.000 0.000 0.012
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.3707 0.878 0.716 0.000 0.000 0.284 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.2927 0.665 0.868 0.000 0.000 0.092 0.040
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.2012 0.864 0.000 0.000 0.020 0.920 0.060
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0162 0.689 0.000 0.000 0.996 0.000 0.004
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.2561 0.806 0.000 0.000 0.144 0.000 0.856
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 3 0.5441 0.534 0.080 0.220 0.680 0.000 0.020
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.5541 0.211 0.056 0.496 0.004 0.000 0.444
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 4 0.1732 0.887 0.080 0.000 0.000 0.920 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.2012 0.864 0.000 0.000 0.020 0.920 0.060
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.3999 0.439 0.000 0.656 0.344 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.2012 0.864 0.000 0.000 0.020 0.920 0.060
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.1270 0.672 0.000 0.000 0.948 0.000 0.052
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 4 0.3196 0.777 0.192 0.000 0.000 0.804 0.004
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.2012 0.864 0.000 0.000 0.020 0.920 0.060
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.3912 0.790 0.752 0.000 0.000 0.228 0.020
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.1845 0.883 0.056 0.928 0.000 0.000 0.016
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.3684 0.878 0.720 0.000 0.000 0.280 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0162 0.901 0.004 0.000 0.000 0.996 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.5541 0.211 0.056 0.496 0.004 0.000 0.444
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.2012 0.864 0.000 0.000 0.020 0.920 0.060
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0162 0.897 0.004 0.996 0.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.1845 0.883 0.056 0.928 0.000 0.000 0.016
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.5920 0.271 0.384 0.000 0.536 0.024 0.056
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0162 0.689 0.000 0.000 0.996 0.000 0.004
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.2012 0.864 0.000 0.000 0.020 0.920 0.060
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0000 0.902 0.000 0.000 0.000 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.902 0.000 0.000 0.000 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.3847 0.698 0.156 0.000 0.004 0.800 0.040
#> 352471DC-A881-4EA8-B646-EB1200291893 4 0.2763 0.836 0.148 0.000 0.000 0.848 0.004
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.1740 0.884 0.056 0.932 0.000 0.000 0.012
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.1845 0.883 0.056 0.928 0.000 0.000 0.016
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.3999 0.439 0.000 0.656 0.344 0.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.897 0.000 1.000 0.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 4 0.1732 0.887 0.080 0.000 0.000 0.920 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.897 0.000 1.000 0.000 0.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.2536 0.855 0.128 0.000 0.000 0.868 0.004
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0162 0.901 0.004 0.000 0.000 0.996 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.3684 0.878 0.720 0.000 0.000 0.280 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 3 0.5412 0.538 0.080 0.216 0.684 0.000 0.020
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.3684 0.878 0.720 0.000 0.000 0.280 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.3857 0.266 0.312 0.000 0.000 0.688 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.1892 0.886 0.080 0.000 0.000 0.916 0.004
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0162 0.897 0.004 0.996 0.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.1845 0.883 0.056 0.928 0.000 0.000 0.016
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 3 0.5441 0.534 0.080 0.220 0.680 0.000 0.020
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.2536 0.855 0.128 0.000 0.000 0.868 0.004
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.902 0.000 0.000 0.000 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.1845 0.883 0.056 0.928 0.000 0.000 0.016
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.3983 0.447 0.000 0.660 0.340 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.2193 0.857 0.000 0.000 0.028 0.912 0.060
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.3305 0.865 0.776 0.000 0.000 0.224 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.897 0.000 1.000 0.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.3177 0.864 0.792 0.000 0.000 0.208 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.2561 0.806 0.000 0.000 0.144 0.000 0.856
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 3 0.6463 0.509 0.280 0.144 0.556 0.000 0.020
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0290 0.900 0.000 0.000 0.000 0.992 0.008
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 4 0.3196 0.777 0.192 0.000 0.000 0.804 0.004
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.897 0.000 1.000 0.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.897 0.000 1.000 0.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.5920 0.271 0.384 0.000 0.536 0.024 0.056
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 4 0.1732 0.887 0.080 0.000 0.000 0.920 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.1892 0.886 0.080 0.000 0.000 0.916 0.004
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.897 0.000 1.000 0.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.3707 0.878 0.716 0.000 0.000 0.284 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.2888 0.853 0.056 0.880 0.000 0.004 0.060
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.3177 0.864 0.792 0.000 0.000 0.208 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.3177 0.864 0.792 0.000 0.000 0.208 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.902 0.000 0.000 0.000 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0162 0.689 0.000 0.000 0.996 0.000 0.004
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.897 0.000 1.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.1892 0.886 0.080 0.000 0.000 0.916 0.004
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0290 0.900 0.008 0.000 0.000 0.992 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0000 0.902 0.000 0.000 0.000 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 3 0.5412 0.538 0.080 0.216 0.684 0.000 0.020
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.3143 0.437 0.796 0.000 0.000 0.000 0.204
#> 12F54761-4F68-4181-8421-88EA858902FC 4 0.2674 0.844 0.140 0.000 0.000 0.856 0.004
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 4 0.1732 0.887 0.080 0.000 0.000 0.920 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.902 0.000 0.000 0.000 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.897 0.000 1.000 0.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.2193 0.857 0.000 0.000 0.028 0.912 0.060
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.2561 0.806 0.000 0.000 0.144 0.000 0.856
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.2231 0.884 0.900 0.000 0.004 0.068 0.000 0.028
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 3 0.6451 0.381 0.000 0.056 0.496 0.152 0.000 0.296
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.4101 0.237 0.000 0.000 0.580 0.408 0.000 0.012
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 4 0.4023 0.726 0.264 0.000 0.004 0.704 0.000 0.028
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0520 0.676 0.000 0.000 0.984 0.000 0.008 0.008
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.3482 0.551 0.000 0.684 0.000 0.000 0.000 0.316
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.1779 0.681 0.000 0.000 0.920 0.000 0.016 0.064
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.1845 0.889 0.920 0.000 0.000 0.052 0.000 0.028
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.2457 0.864 0.880 0.000 0.000 0.036 0.000 0.084
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0520 0.676 0.000 0.000 0.984 0.000 0.008 0.008
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0146 0.751 0.000 0.996 0.000 0.000 0.000 0.004
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.3464 0.559 0.000 0.688 0.000 0.000 0.000 0.312
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.2231 0.884 0.900 0.000 0.004 0.068 0.000 0.028
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 6 0.4004 0.733 0.000 0.092 0.096 0.004 0.016 0.792
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.0000 0.944 0.000 0.000 0.000 0.000 1.000 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0520 0.676 0.000 0.000 0.984 0.000 0.008 0.008
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0000 0.944 0.000 0.000 0.000 0.000 1.000 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.751 0.000 1.000 0.000 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0146 0.751 0.000 0.996 0.000 0.000 0.000 0.004
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.751 0.000 1.000 0.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.3789 0.457 0.000 0.000 0.000 0.000 0.584 0.416
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.2762 0.868 0.196 0.000 0.000 0.804 0.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.3482 0.551 0.000 0.684 0.000 0.000 0.000 0.316
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.0000 0.944 0.000 0.000 0.000 0.000 1.000 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 6 0.3906 0.733 0.000 0.084 0.096 0.004 0.016 0.800
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0146 0.751 0.000 0.996 0.000 0.000 0.000 0.004
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.2231 0.884 0.900 0.000 0.004 0.068 0.000 0.028
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.3620 0.710 0.352 0.000 0.000 0.648 0.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.1882 0.872 0.920 0.000 0.060 0.008 0.000 0.012
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0000 0.944 0.000 0.000 0.000 0.000 1.000 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.2252 0.862 0.900 0.000 0.072 0.012 0.000 0.016
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.1779 0.681 0.000 0.000 0.920 0.000 0.016 0.064
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.751 0.000 1.000 0.000 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.2838 0.851 0.852 0.000 0.004 0.116 0.000 0.028
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.3464 0.559 0.000 0.688 0.000 0.000 0.000 0.312
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.2762 0.868 0.196 0.000 0.000 0.804 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.2322 0.655 0.036 0.000 0.000 0.896 0.004 0.064
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.2119 0.868 0.912 0.000 0.060 0.008 0.004 0.016
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.1779 0.681 0.000 0.000 0.920 0.000 0.016 0.064
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0000 0.944 0.000 0.000 0.000 0.000 1.000 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 3 0.5014 0.378 0.000 0.060 0.564 0.008 0.000 0.368
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 6 0.3582 0.732 0.000 0.252 0.000 0.000 0.016 0.732
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.1845 0.889 0.920 0.000 0.000 0.052 0.000 0.028
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.2119 0.868 0.912 0.000 0.060 0.008 0.004 0.016
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.4210 0.218 0.000 0.636 0.336 0.000 0.000 0.028
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.2220 0.867 0.908 0.000 0.060 0.012 0.004 0.016
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0520 0.676 0.000 0.000 0.984 0.000 0.008 0.008
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.3453 0.778 0.788 0.000 0.004 0.180 0.000 0.028
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.2119 0.868 0.912 0.000 0.060 0.008 0.004 0.016
#> B5474EEB-D585-4668-959C-38F240F55BC2 4 0.4449 0.764 0.196 0.000 0.004 0.712 0.000 0.088
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.3482 0.551 0.000 0.684 0.000 0.000 0.000 0.316
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.2730 0.868 0.192 0.000 0.000 0.808 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0146 0.902 0.996 0.000 0.000 0.004 0.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 6 0.3582 0.732 0.000 0.252 0.000 0.000 0.016 0.732
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.2119 0.868 0.912 0.000 0.060 0.008 0.004 0.016
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0777 0.745 0.000 0.972 0.000 0.004 0.000 0.024
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.3482 0.551 0.000 0.684 0.000 0.000 0.000 0.316
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.4101 0.237 0.000 0.000 0.580 0.408 0.000 0.012
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.1779 0.681 0.000 0.000 0.920 0.000 0.016 0.064
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.2220 0.867 0.908 0.000 0.060 0.012 0.004 0.016
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.3562 0.716 0.788 0.000 0.040 0.168 0.000 0.004
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.3206 0.824 0.816 0.000 0.004 0.152 0.000 0.028
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.3464 0.559 0.000 0.688 0.000 0.000 0.000 0.312
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.3482 0.551 0.000 0.684 0.000 0.000 0.000 0.316
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.4210 0.218 0.000 0.636 0.336 0.000 0.000 0.028
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0146 0.751 0.000 0.996 0.000 0.000 0.000 0.004
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.1845 0.889 0.920 0.000 0.000 0.052 0.000 0.028
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.751 0.000 1.000 0.000 0.000 0.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.2838 0.851 0.852 0.000 0.004 0.116 0.000 0.028
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0146 0.902 0.996 0.000 0.000 0.004 0.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.2730 0.868 0.192 0.000 0.000 0.808 0.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 3 0.4963 0.383 0.000 0.056 0.568 0.008 0.000 0.368
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.2730 0.868 0.192 0.000 0.000 0.808 0.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.3531 0.329 0.672 0.000 0.000 0.328 0.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.2231 0.884 0.900 0.000 0.004 0.068 0.000 0.028
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0777 0.745 0.000 0.972 0.000 0.004 0.000 0.024
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.3482 0.551 0.000 0.684 0.000 0.000 0.000 0.316
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 3 0.5014 0.378 0.000 0.060 0.564 0.008 0.000 0.368
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.2838 0.851 0.852 0.000 0.004 0.116 0.000 0.028
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.3482 0.551 0.000 0.684 0.000 0.000 0.000 0.316
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.3883 0.246 0.000 0.656 0.332 0.000 0.000 0.012
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.2252 0.862 0.900 0.000 0.072 0.012 0.000 0.016
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 4 0.2553 0.854 0.144 0.000 0.000 0.848 0.000 0.008
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0146 0.751 0.000 0.996 0.000 0.000 0.000 0.004
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 4 0.2389 0.853 0.128 0.000 0.000 0.864 0.000 0.008
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.0000 0.944 0.000 0.000 0.000 0.000 1.000 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 3 0.6231 0.328 0.000 0.016 0.440 0.200 0.000 0.344
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0520 0.899 0.984 0.000 0.000 0.008 0.000 0.008
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.3453 0.778 0.788 0.000 0.004 0.180 0.000 0.028
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0146 0.751 0.000 0.996 0.000 0.000 0.000 0.004
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0146 0.751 0.000 0.996 0.000 0.000 0.000 0.004
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.4101 0.237 0.000 0.000 0.580 0.408 0.000 0.012
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.1845 0.889 0.920 0.000 0.000 0.052 0.000 0.028
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.2231 0.884 0.900 0.000 0.004 0.068 0.000 0.028
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0146 0.751 0.000 0.996 0.000 0.000 0.000 0.004
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.2762 0.868 0.196 0.000 0.000 0.804 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.3892 0.469 0.004 0.640 0.000 0.000 0.004 0.352
#> F900E9BE-2400-4451-9434-EE8BC513BA94 4 0.2389 0.853 0.128 0.000 0.000 0.864 0.000 0.008
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 4 0.2389 0.853 0.128 0.000 0.000 0.864 0.000 0.008
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.1779 0.681 0.000 0.000 0.920 0.000 0.016 0.064
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.751 0.000 1.000 0.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.2231 0.884 0.900 0.000 0.004 0.068 0.000 0.028
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0632 0.898 0.976 0.000 0.000 0.024 0.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 3 0.4963 0.383 0.000 0.056 0.568 0.008 0.000 0.368
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 4 0.3786 0.492 0.000 0.000 0.000 0.768 0.168 0.064
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.2968 0.840 0.840 0.000 0.004 0.128 0.000 0.028
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.1845 0.889 0.920 0.000 0.000 0.052 0.000 0.028
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0146 0.751 0.000 0.996 0.000 0.000 0.000 0.004
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.2252 0.862 0.900 0.000 0.072 0.012 0.000 0.016
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0000 0.944 0.000 0.000 0.000 0.000 1.000 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["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 17548 rows and 122 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 0.949 0.963 0.983 0.4975 0.501 0.501
#> 3 3 0.541 0.722 0.828 0.2981 0.846 0.700
#> 4 4 0.613 0.366 0.615 0.1321 0.833 0.599
#> 5 5 0.651 0.470 0.656 0.0654 0.770 0.397
#> 6 6 0.718 0.664 0.732 0.0477 0.846 0.464
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.000 0.989 1.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.000 0.974 0.000 1.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.000 0.989 1.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.000 0.989 1.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 2 0.760 0.746 0.220 0.780
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.000 0.974 0.000 1.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 2 0.000 0.974 0.000 1.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.000 0.989 1.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.000 0.989 1.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 1 0.000 0.989 1.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.000 0.974 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.000 0.974 0.000 1.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.000 0.989 1.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.000 0.974 0.000 1.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 2 0.605 0.843 0.148 0.852
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 2 0.529 0.873 0.120 0.880
#> 853120F0-857B-4108-9EC8-727189630C5F 2 0.615 0.838 0.152 0.848
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.000 0.989 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.000 0.974 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.000 0.974 0.000 1.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.000 0.989 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.000 0.974 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 2 0.634 0.829 0.160 0.840
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.000 0.989 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.000 0.974 0.000 1.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.000 0.989 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.000 0.989 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.000 0.989 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 2 0.552 0.865 0.128 0.872
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.000 0.974 0.000 1.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.000 0.974 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.000 0.989 1.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.000 0.989 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.000 0.989 1.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.000 0.989 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 2 0.615 0.838 0.152 0.848
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.000 0.989 1.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 2 0.000 0.974 0.000 1.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.000 0.974 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.000 0.989 1.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.000 0.974 0.000 1.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.000 0.989 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.000 0.989 1.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.000 0.989 1.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.000 0.974 0.000 1.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.000 0.974 0.000 1.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.000 0.974 0.000 1.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.000 0.974 0.000 1.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.000 0.989 1.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.000 0.989 1.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.000 0.974 0.000 1.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.000 0.989 1.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 2 0.430 0.903 0.088 0.912
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.000 0.989 1.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.697 0.755 0.812 0.188
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.000 0.989 1.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.000 0.974 0.000 1.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.000 0.989 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.000 0.989 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.000 0.974 0.000 1.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.000 0.989 1.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.000 0.974 0.000 1.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.000 0.974 0.000 1.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.000 0.989 1.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.000 0.974 0.000 1.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.000 0.989 1.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.000 0.989 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.000 0.989 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.000 0.989 1.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.000 0.989 1.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.000 0.974 0.000 1.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.000 0.974 0.000 1.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.000 0.974 0.000 1.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.000 0.974 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.000 0.989 1.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.000 0.974 0.000 1.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.000 0.989 1.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.000 0.989 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.000 0.989 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.000 0.974 0.000 1.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.000 0.989 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.000 0.989 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.000 0.989 1.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.000 0.974 0.000 1.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.000 0.974 0.000 1.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.000 0.974 0.000 1.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.000 0.989 1.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.000 0.989 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.000 0.974 0.000 1.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.000 0.974 0.000 1.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.000 0.989 1.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.000 0.989 1.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.000 0.974 0.000 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.000 0.989 1.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.991 0.149 0.556 0.444
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.730 0.752 0.204 0.796
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.000 0.989 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.000 0.989 1.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.000 0.974 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.000 0.974 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.000 0.989 1.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.000 0.989 1.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.000 0.989 1.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.000 0.974 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.000 0.989 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.000 0.974 0.000 1.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.000 0.989 1.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.000 0.989 1.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.000 0.989 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.000 0.974 0.000 1.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.000 0.974 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.000 0.989 1.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.000 0.989 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.000 0.989 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.000 0.974 0.000 1.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.278 0.939 0.952 0.048
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.000 0.989 1.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.000 0.989 1.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.000 0.989 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.000 0.974 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.000 0.989 1.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.000 0.974 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.4452 0.7760 0.808 0.000 0.192
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.5988 0.1969 0.000 0.632 0.368
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.5016 0.5899 0.240 0.000 0.760
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.5529 0.7461 0.704 0.000 0.296
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.8753 0.6889 0.188 0.224 0.588
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.3482 0.8264 0.000 0.872 0.128
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.6204 0.5677 0.000 0.424 0.576
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.4452 0.7760 0.808 0.000 0.192
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.4605 0.7701 0.796 0.000 0.204
#> 806616FE-1855-4284-9265-42842104CB21 3 0.5678 0.5324 0.316 0.000 0.684
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.8764 0.000 1.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.3482 0.8264 0.000 0.872 0.128
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.4452 0.7760 0.808 0.000 0.192
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0892 0.8764 0.000 0.980 0.020
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.6546 0.7391 0.096 0.148 0.756
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.8464 0.6869 0.132 0.272 0.596
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.6546 0.7391 0.096 0.148 0.756
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.8093 1.000 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0892 0.8764 0.000 0.980 0.020
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0892 0.8764 0.000 0.980 0.020
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.8093 1.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0892 0.8764 0.000 0.980 0.020
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.7664 0.7178 0.104 0.228 0.668
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.4750 0.7062 0.784 0.000 0.216
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.3482 0.8264 0.000 0.872 0.128
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.8093 1.000 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.8093 1.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.8093 1.000 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.6546 0.7391 0.096 0.148 0.756
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 3 0.6260 0.0714 0.000 0.448 0.552
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0892 0.8764 0.000 0.980 0.020
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.4452 0.7760 0.808 0.000 0.192
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.4750 0.7062 0.784 0.000 0.216
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.0892 0.8065 0.980 0.000 0.020
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.8093 1.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.6546 0.7391 0.096 0.148 0.756
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0592 0.8079 0.988 0.000 0.012
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.5803 0.6976 0.016 0.248 0.736
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0892 0.8764 0.000 0.980 0.020
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.4452 0.7760 0.808 0.000 0.192
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.1529 0.8670 0.000 0.960 0.040
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.4750 0.7062 0.784 0.000 0.216
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.5678 0.5802 0.684 0.000 0.316
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.8093 1.000 0.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.6204 0.5677 0.000 0.424 0.576
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.7222 0.6078 0.032 0.388 0.580
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.3879 0.8075 0.000 0.848 0.152
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.2959 0.8423 0.000 0.900 0.100
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.4504 0.7741 0.804 0.000 0.196
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.5926 0.2902 0.644 0.000 0.356
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.8764 0.000 1.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.5882 0.2951 0.652 0.000 0.348
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.7508 0.7308 0.148 0.156 0.696
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.5431 0.7534 0.716 0.000 0.284
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.5948 0.2888 0.640 0.000 0.360
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.6140 0.6710 0.596 0.000 0.404
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.3941 0.8040 0.000 0.844 0.156
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.4750 0.7062 0.784 0.000 0.216
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.8093 1.000 0.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.6291 0.4828 0.000 0.468 0.532
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.5905 0.2977 0.648 0.000 0.352
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0892 0.8764 0.000 0.980 0.020
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.4346 0.7737 0.000 0.816 0.184
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.5016 0.5899 0.240 0.000 0.760
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.6204 0.5677 0.000 0.424 0.576
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.0424 0.8083 0.992 0.000 0.008
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.8093 1.000 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.8093 1.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.3941 0.7477 0.844 0.000 0.156
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.3619 0.8012 0.864 0.000 0.136
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.1529 0.8670 0.000 0.960 0.040
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.3482 0.8264 0.000 0.872 0.128
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.8764 0.000 1.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.8764 0.000 1.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.4605 0.7701 0.796 0.000 0.204
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0892 0.8764 0.000 0.980 0.020
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.3879 0.7885 0.848 0.000 0.152
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.8093 1.000 0.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.4750 0.7062 0.784 0.000 0.216
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.6309 0.2136 0.000 0.504 0.496
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.4750 0.7062 0.784 0.000 0.216
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.1163 0.8047 0.972 0.000 0.028
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.4452 0.7760 0.808 0.000 0.192
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0892 0.8764 0.000 0.980 0.020
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.4555 0.7539 0.000 0.800 0.200
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.2625 0.8181 0.000 0.916 0.084
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.4452 0.7760 0.808 0.000 0.192
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.8093 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.3941 0.8040 0.000 0.844 0.156
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0892 0.8764 0.000 0.980 0.020
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.5905 0.3065 0.648 0.000 0.352
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.5785 0.7076 0.668 0.000 0.332
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0892 0.8764 0.000 0.980 0.020
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.5621 0.6983 0.692 0.000 0.308
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.5492 0.7137 0.104 0.080 0.816
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 3 0.6359 -0.0260 0.004 0.404 0.592
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0237 0.8089 0.996 0.000 0.004
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.5431 0.7534 0.716 0.000 0.284
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0892 0.8764 0.000 0.980 0.020
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0892 0.8764 0.000 0.980 0.020
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.4931 0.5963 0.232 0.000 0.768
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.5455 0.7561 0.776 0.020 0.204
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.4452 0.7760 0.808 0.000 0.192
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.8764 0.000 1.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.4750 0.7062 0.784 0.000 0.216
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.4235 0.7830 0.000 0.824 0.176
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.5948 0.6927 0.640 0.000 0.360
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.5529 0.7089 0.704 0.000 0.296
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.8093 1.000 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.6204 0.5677 0.000 0.424 0.576
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0892 0.8764 0.000 0.980 0.020
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.4452 0.7760 0.808 0.000 0.192
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.8093 1.000 0.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.8093 1.000 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.5327 0.6873 0.000 0.728 0.272
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.5526 0.6525 0.172 0.036 0.792
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.5397 0.7553 0.720 0.000 0.280
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.5455 0.7561 0.776 0.020 0.204
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.8093 1.000 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0892 0.8764 0.000 0.980 0.020
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.6008 0.2491 0.628 0.000 0.372
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.6192 0.5713 0.000 0.420 0.580
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0657 0.5932 0.984 0.000 0.012 0.004
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.7398 0.0185 0.000 0.492 0.184 0.324
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.4543 0.2016 0.000 0.000 0.676 0.324
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.5036 0.4426 0.696 0.000 0.280 0.024
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 4 0.4837 -0.2629 0.000 0.004 0.348 0.648
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.5751 0.7348 0.124 0.712 0.164 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 4 0.6179 -0.3202 0.000 0.056 0.392 0.552
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0657 0.5941 0.984 0.000 0.012 0.004
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.3217 0.5071 0.860 0.000 0.128 0.012
#> 806616FE-1855-4284-9265-42842104CB21 4 0.5057 -0.2490 0.012 0.000 0.340 0.648
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.8229 0.000 1.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.5647 0.7389 0.116 0.720 0.164 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0524 0.5943 0.988 0.000 0.008 0.004
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.2053 0.8137 0.000 0.924 0.072 0.004
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 4 0.5336 -0.4180 0.004 0.004 0.496 0.496
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 4 0.5112 -0.2998 0.000 0.008 0.384 0.608
#> 853120F0-857B-4108-9EC8-727189630C5F 4 0.5336 -0.4180 0.004 0.004 0.496 0.496
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.4916 0.5313 0.576 0.000 0.000 0.424
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0804 0.8230 0.000 0.980 0.012 0.008
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0804 0.8230 0.000 0.980 0.012 0.008
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.4916 0.5313 0.576 0.000 0.000 0.424
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0804 0.8230 0.000 0.980 0.012 0.008
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 4 0.5464 -0.4122 0.004 0.008 0.492 0.496
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.7754 -0.1751 0.244 0.000 0.336 0.420
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.5751 0.7348 0.124 0.712 0.164 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.4925 0.5307 0.572 0.000 0.000 0.428
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.4925 0.5307 0.572 0.000 0.000 0.428
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.5097 0.5282 0.568 0.000 0.004 0.428
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.5336 0.3241 0.004 0.004 0.496 0.496
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 4 0.9222 -0.1738 0.300 0.080 0.252 0.368
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0804 0.8230 0.000 0.980 0.012 0.008
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0657 0.5932 0.984 0.000 0.012 0.004
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.7704 -0.1665 0.232 0.000 0.336 0.432
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.5657 0.5045 0.540 0.000 0.024 0.436
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.4925 0.5307 0.572 0.000 0.000 0.428
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.5336 0.3241 0.004 0.004 0.496 0.496
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.4925 0.5303 0.572 0.000 0.000 0.428
#> 50D620F3-5C52-42FB-89A1-6840A7444647 4 0.5999 -0.3307 0.000 0.044 0.404 0.552
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0804 0.8230 0.000 0.980 0.012 0.008
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.5961 1.000 0.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.2814 0.7947 0.000 0.868 0.132 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.7754 -0.1751 0.244 0.000 0.336 0.420
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.7602 -0.3277 0.200 0.000 0.420 0.380
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.4933 0.5235 0.568 0.000 0.000 0.432
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 4 0.6179 -0.3202 0.000 0.056 0.392 0.552
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.6147 0.3249 0.000 0.048 0.488 0.464
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.7092 0.6591 0.216 0.608 0.164 0.012
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.6850 0.6786 0.132 0.628 0.228 0.012
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0895 0.5907 0.976 0.000 0.020 0.004
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.4589 0.1918 0.168 0.000 0.048 0.784
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.8229 0.000 1.000 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.4544 0.1896 0.164 0.000 0.048 0.788
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 4 0.4855 -0.2662 0.000 0.004 0.352 0.644
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.3448 0.5265 0.828 0.000 0.168 0.004
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.5572 0.1977 0.196 0.000 0.088 0.716
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.4819 0.3849 0.652 0.000 0.344 0.004
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.6537 0.6797 0.200 0.636 0.164 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.7754 -0.1751 0.244 0.000 0.336 0.420
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.5097 0.5282 0.568 0.000 0.004 0.428
#> 84E18629-1B13-4696-8E54-121ABE469CD2 4 0.5957 -0.3157 0.000 0.040 0.420 0.540
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.5035 0.2026 0.196 0.000 0.056 0.748
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0804 0.8230 0.000 0.980 0.012 0.008
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.7586 0.5673 0.304 0.520 0.164 0.012
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 4 0.4761 -0.2752 0.000 0.000 0.372 0.628
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 4 0.6179 -0.3202 0.000 0.056 0.392 0.552
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.5000 -0.4640 0.500 0.000 0.000 0.500
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.4916 0.5313 0.576 0.000 0.000 0.424
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.4925 0.5307 0.572 0.000 0.000 0.428
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.7582 -0.3110 0.336 0.000 0.208 0.456
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.5902 0.5252 0.700 0.000 0.160 0.140
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.2814 0.7947 0.000 0.868 0.132 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.5751 0.7348 0.124 0.712 0.164 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0188 0.8227 0.000 0.996 0.004 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0188 0.8226 0.000 0.996 0.004 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.3142 0.5053 0.860 0.000 0.132 0.008
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0804 0.8230 0.000 0.980 0.012 0.008
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.2149 0.5933 0.912 0.000 0.000 0.088
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.5097 0.5282 0.568 0.000 0.004 0.428
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.7754 -0.1751 0.244 0.000 0.336 0.420
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.9417 -0.3300 0.384 0.304 0.180 0.132
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.7722 -0.1663 0.236 0.000 0.336 0.428
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.7541 -0.3727 0.388 0.000 0.188 0.424
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0657 0.5932 0.984 0.000 0.012 0.004
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0804 0.8230 0.000 0.980 0.012 0.008
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.7745 0.4982 0.360 0.464 0.164 0.012
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.5007 0.7640 0.008 0.776 0.156 0.060
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.5961 1.000 0.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.4916 0.5313 0.576 0.000 0.000 0.424
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.6869 0.6608 0.220 0.612 0.164 0.004
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0804 0.8230 0.000 0.980 0.012 0.008
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.5620 0.1997 0.208 0.000 0.084 0.708
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.6868 0.3788 0.544 0.000 0.336 0.120
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0804 0.8230 0.000 0.980 0.012 0.008
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.7006 0.3702 0.528 0.000 0.340 0.132
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.5137 0.3328 0.004 0.000 0.544 0.452
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 3 0.8159 -0.1339 0.388 0.156 0.424 0.032
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.4916 0.5313 0.576 0.000 0.000 0.424
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.3448 0.5265 0.828 0.000 0.168 0.004
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0804 0.8230 0.000 0.980 0.012 0.008
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0804 0.8230 0.000 0.980 0.012 0.008
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 4 0.4907 -0.2964 0.000 0.000 0.420 0.580
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.3271 0.5055 0.856 0.000 0.132 0.012
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0524 0.5943 0.988 0.000 0.008 0.004
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0188 0.8226 0.000 0.996 0.004 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.7754 -0.1751 0.244 0.000 0.336 0.420
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.7586 0.5673 0.304 0.520 0.164 0.012
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.6478 0.3856 0.576 0.000 0.336 0.088
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.6993 0.3739 0.532 0.000 0.336 0.132
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.4925 0.5307 0.572 0.000 0.000 0.428
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 4 0.6158 -0.3154 0.000 0.056 0.384 0.560
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0804 0.8230 0.000 0.980 0.012 0.008
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0804 0.5934 0.980 0.000 0.012 0.008
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.4925 0.5307 0.572 0.000 0.000 0.428
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.5097 0.5282 0.568 0.000 0.004 0.428
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.8567 0.4195 0.380 0.400 0.168 0.052
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.3583 0.2583 0.004 0.000 0.816 0.180
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.3545 0.5272 0.828 0.000 0.164 0.008
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.3142 0.5053 0.860 0.000 0.132 0.008
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.4925 0.5307 0.572 0.000 0.000 0.428
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0804 0.8230 0.000 0.980 0.012 0.008
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.4130 0.1349 0.108 0.000 0.064 0.828
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.6209 0.3255 0.000 0.052 0.492 0.456
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.4470 0.5481 0.616 0.000 0.012 0.372 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.7512 0.2166 0.100 0.472 0.128 0.000 0.300
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.5775 0.1143 0.104 0.000 0.672 0.032 0.192
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.5659 0.4647 0.632 0.000 0.204 0.164 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 5 0.5614 0.3654 0.004 0.008 0.432 0.044 0.512
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.6650 0.4107 0.348 0.476 0.164 0.000 0.012
#> 9264567D-4524-46AF-A851-C091C3CD76CF 5 0.5199 0.4430 0.004 0.036 0.412 0.000 0.548
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.4161 0.5338 0.608 0.000 0.000 0.392 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.3707 0.5682 0.716 0.000 0.000 0.284 0.000
#> 806616FE-1855-4284-9265-42842104CB21 5 0.5531 0.3480 0.004 0.000 0.432 0.056 0.508
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0162 0.8351 0.000 0.996 0.000 0.000 0.004
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.6436 0.4259 0.344 0.488 0.164 0.000 0.004
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.4482 0.5463 0.612 0.000 0.012 0.376 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.5126 0.6862 0.172 0.720 0.092 0.000 0.016
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.0290 0.5378 0.000 0.008 0.000 0.000 0.992
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 5 0.5387 0.4280 0.008 0.016 0.416 0.016 0.544
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0162 0.5369 0.000 0.004 0.000 0.000 0.996
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.7096 0.000 0.000 0.000 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0290 0.8362 0.000 0.992 0.000 0.000 0.008
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0290 0.8362 0.000 0.992 0.000 0.000 0.008
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.7096 0.000 0.000 0.000 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0290 0.8362 0.000 0.992 0.000 0.000 0.008
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.0290 0.5325 0.000 0.000 0.008 0.000 0.992
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.6286 0.2326 0.120 0.000 0.388 0.484 0.008
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.6650 0.4107 0.348 0.476 0.164 0.000 0.012
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.7096 0.000 0.000 0.000 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0162 0.7073 0.004 0.000 0.000 0.996 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0290 0.7101 0.008 0.000 0.000 0.992 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.0162 0.5369 0.000 0.004 0.000 0.000 0.996
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.6269 0.2949 0.588 0.036 0.284 0.000 0.092
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0290 0.8362 0.000 0.992 0.000 0.000 0.008
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.4482 0.5463 0.612 0.000 0.012 0.376 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.6138 0.3141 0.120 0.000 0.324 0.548 0.008
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.0992 0.7013 0.008 0.000 0.024 0.968 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0162 0.7073 0.004 0.000 0.000 0.996 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0162 0.5369 0.000 0.004 0.000 0.000 0.996
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.0798 0.7053 0.008 0.000 0.016 0.976 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 5 0.4735 0.4442 0.008 0.008 0.412 0.000 0.572
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0290 0.8362 0.000 0.992 0.000 0.000 0.008
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.4494 0.5429 0.608 0.000 0.012 0.380 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.5966 0.5843 0.228 0.604 0.164 0.000 0.004
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.6286 0.2326 0.120 0.000 0.388 0.484 0.008
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.7727 -0.2356 0.120 0.000 0.396 0.364 0.120
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.0510 0.7089 0.000 0.000 0.016 0.984 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 5 0.5199 0.4430 0.004 0.036 0.412 0.000 0.548
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0404 0.5372 0.000 0.012 0.000 0.000 0.988
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.6718 -0.0534 0.500 0.308 0.176 0.000 0.016
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 1 0.7251 -0.2926 0.388 0.380 0.200 0.000 0.032
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.4256 0.4842 0.564 0.000 0.000 0.436 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.6225 0.0604 0.004 0.000 0.400 0.472 0.124
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0451 0.8334 0.008 0.988 0.000 0.000 0.004
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.6257 0.0511 0.004 0.000 0.400 0.468 0.128
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 5 0.5323 0.3772 0.008 0.008 0.448 0.020 0.516
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.4904 0.5564 0.688 0.000 0.072 0.240 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.6261 0.0434 0.004 0.000 0.404 0.464 0.128
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.5919 0.3658 0.592 0.000 0.288 0.112 0.008
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 1 0.6703 -0.2432 0.436 0.388 0.164 0.000 0.012
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.6286 0.2326 0.120 0.000 0.388 0.484 0.008
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0451 0.7103 0.008 0.000 0.004 0.988 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.5633 -0.3879 0.056 0.008 0.512 0.000 0.424
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.6225 0.0604 0.004 0.000 0.400 0.472 0.124
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0290 0.8362 0.000 0.992 0.000 0.000 0.008
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 1 0.5913 0.2331 0.636 0.188 0.164 0.000 0.012
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 5 0.5190 0.3122 0.004 0.000 0.468 0.032 0.496
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 5 0.5199 0.4430 0.004 0.036 0.412 0.000 0.548
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.3231 0.5608 0.004 0.000 0.196 0.800 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0162 0.7073 0.004 0.000 0.000 0.996 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.7096 0.000 0.000 0.000 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.4351 0.5661 0.100 0.000 0.132 0.768 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.6080 0.3960 0.520 0.000 0.136 0.344 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.5990 0.5803 0.232 0.600 0.164 0.000 0.004
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.6650 0.4107 0.348 0.476 0.164 0.000 0.012
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.2299 0.8022 0.052 0.912 0.032 0.000 0.004
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0579 0.8288 0.008 0.984 0.008 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.3586 0.5701 0.736 0.000 0.000 0.264 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0290 0.8362 0.000 0.992 0.000 0.000 0.008
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.4659 0.3873 0.500 0.000 0.012 0.488 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0451 0.7103 0.008 0.000 0.004 0.988 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.6296 0.2188 0.120 0.000 0.396 0.476 0.008
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.5087 0.3838 0.700 0.068 0.220 0.000 0.012
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.6291 0.2279 0.120 0.000 0.392 0.480 0.008
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.3749 0.6022 0.080 0.000 0.104 0.816 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.4470 0.5481 0.616 0.000 0.012 0.372 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0290 0.8362 0.000 0.992 0.000 0.000 0.008
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 1 0.5843 0.3059 0.668 0.148 0.164 0.008 0.012
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.6815 0.4639 0.300 0.488 0.196 0.000 0.016
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.4494 0.5429 0.608 0.000 0.012 0.380 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0162 0.7073 0.004 0.000 0.000 0.996 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 1 0.6683 -0.1946 0.456 0.368 0.164 0.000 0.012
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0290 0.8362 0.000 0.992 0.000 0.000 0.008
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.6257 0.0525 0.004 0.000 0.400 0.468 0.128
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.6701 0.1581 0.424 0.000 0.388 0.180 0.008
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0290 0.8362 0.000 0.992 0.000 0.000 0.008
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.6703 0.1468 0.420 0.000 0.392 0.180 0.008
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.1205 0.4909 0.004 0.000 0.040 0.000 0.956
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.5152 0.4048 0.672 0.036 0.272 0.004 0.016
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0000 0.7096 0.000 0.000 0.000 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.4904 0.5564 0.688 0.000 0.072 0.240 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0290 0.8362 0.000 0.992 0.000 0.000 0.008
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0290 0.8362 0.000 0.992 0.000 0.000 0.008
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.5642 -0.4198 0.024 0.000 0.484 0.032 0.460
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.3491 0.5732 0.768 0.000 0.004 0.228 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.4482 0.5463 0.612 0.000 0.012 0.376 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0579 0.8288 0.008 0.984 0.008 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.6286 0.2326 0.120 0.000 0.388 0.484 0.008
#> B3561356-5A80-4C79-B23A-D518425565FE 1 0.6461 0.0241 0.540 0.284 0.164 0.000 0.012
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.6476 0.2046 0.460 0.000 0.388 0.144 0.008
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.6701 0.1581 0.424 0.000 0.388 0.180 0.008
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0162 0.7073 0.004 0.000 0.000 0.996 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 5 0.5318 0.4362 0.008 0.036 0.416 0.000 0.540
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0290 0.8362 0.000 0.992 0.000 0.000 0.008
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.4150 0.5379 0.612 0.000 0.000 0.388 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0162 0.7073 0.004 0.000 0.000 0.996 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0290 0.7101 0.008 0.000 0.000 0.992 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.5453 0.3382 0.680 0.108 0.200 0.000 0.012
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.5622 -0.0709 0.076 0.000 0.348 0.004 0.572
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.5039 0.5508 0.676 0.000 0.080 0.244 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.3336 0.5740 0.772 0.000 0.000 0.228 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.7096 0.000 0.000 0.000 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0290 0.8362 0.000 0.992 0.000 0.000 0.008
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.6350 0.0208 0.004 0.000 0.400 0.456 0.140
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0404 0.5372 0.000 0.012 0.000 0.000 0.988
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0146 0.8748 0.996 0.000 0.000 0.004 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.8101 0.1419 0.012 0.436 0.060 0.100 0.180 0.212
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.4522 0.2824 0.000 0.000 0.548 0.424 0.020 0.008
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.3342 0.7025 0.760 0.000 0.000 0.228 0.000 0.012
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#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.3695 0.6454 0.000 0.004 0.772 0.000 0.184 0.040
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.1088 0.8569 0.960 0.000 0.000 0.016 0.000 0.024
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#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0146 0.8748 0.996 0.000 0.000 0.004 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.5076 -0.0415 0.000 0.528 0.000 0.020 0.040 0.412
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.1610 0.9281 0.000 0.000 0.084 0.000 0.916 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.3460 0.6598 0.000 0.000 0.796 0.004 0.164 0.036
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.1610 0.9281 0.000 0.000 0.084 0.000 0.916 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.8029 0.6529 0.212 0.000 0.168 0.352 0.028 0.240
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#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.3691 0.6442 0.000 0.000 0.768 0.004 0.192 0.036
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.9172 0.000 1.000 0.000 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0520 0.8725 0.984 0.000 0.000 0.008 0.000 0.008
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 6 0.3659 0.5725 0.000 0.364 0.000 0.000 0.000 0.636
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.0865 0.3943 0.036 0.000 0.000 0.964 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.3246 0.2316 0.036 0.000 0.004 0.840 0.108 0.012
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.8182 0.6028 0.208 0.000 0.220 0.296 0.028 0.248
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.3695 0.6454 0.000 0.004 0.772 0.000 0.184 0.040
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.1753 0.9272 0.000 0.000 0.084 0.000 0.912 0.004
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 6 0.6136 0.7714 0.180 0.156 0.004 0.020 0.028 0.612
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 6 0.5212 0.7720 0.140 0.212 0.004 0.000 0.004 0.640
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.2529 0.7917 0.900 0.000 0.024 0.012 0.020 0.044
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#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.4127 0.5575 0.020 0.000 0.784 0.108 0.004 0.084
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.2982 0.6643 0.000 0.000 0.820 0.004 0.164 0.012
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.2312 0.8119 0.876 0.000 0.000 0.112 0.000 0.012
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.3946 0.5623 0.020 0.000 0.792 0.100 0.000 0.088
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.4141 0.5106 0.596 0.000 0.000 0.388 0.000 0.016
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 6 0.5008 0.7729 0.148 0.212 0.000 0.000 0.000 0.640
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.0865 0.3943 0.036 0.000 0.000 0.964 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.8029 0.6547 0.212 0.000 0.168 0.352 0.028 0.240
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.4403 0.5448 0.000 0.000 0.708 0.000 0.096 0.196
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.4037 0.5521 0.020 0.000 0.792 0.100 0.004 0.084
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0405 0.9158 0.000 0.988 0.000 0.000 0.008 0.004
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 6 0.4435 0.6952 0.308 0.040 0.004 0.000 0.000 0.648
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.3453 0.6553 0.000 0.000 0.808 0.040 0.144 0.008
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.3695 0.6454 0.000 0.004 0.772 0.000 0.184 0.040
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.7508 -0.3341 0.092 0.000 0.432 0.264 0.028 0.184
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.8052 0.6541 0.224 0.000 0.168 0.344 0.028 0.236
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.8042 0.6563 0.220 0.000 0.168 0.348 0.028 0.236
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.6705 0.4792 0.048 0.000 0.236 0.520 0.016 0.180
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.3468 0.6759 0.728 0.000 0.000 0.264 0.000 0.008
#> F779417A-9E29-4B27-BEA3-B23273A66021 6 0.3659 0.5725 0.000 0.364 0.000 0.000 0.000 0.636
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 6 0.4812 0.7411 0.096 0.264 0.000 0.000 0.000 0.640
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.4007 0.6383 0.000 0.760 0.000 0.020 0.036 0.184
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.1124 0.8920 0.000 0.956 0.000 0.000 0.008 0.036
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.1204 0.8365 0.944 0.000 0.000 0.000 0.000 0.056
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0146 0.9156 0.000 0.996 0.000 0.000 0.000 0.004
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.1572 0.8393 0.936 0.000 0.000 0.028 0.000 0.036
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.8011 0.6534 0.212 0.000 0.168 0.360 0.028 0.232
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.1382 0.3804 0.036 0.000 0.000 0.948 0.008 0.008
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 6 0.5697 0.5559 0.380 0.016 0.012 0.024 0.032 0.536
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.1155 0.3912 0.036 0.000 0.004 0.956 0.000 0.004
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.7090 0.5881 0.076 0.000 0.160 0.508 0.028 0.228
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0146 0.8748 0.996 0.000 0.000 0.004 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0405 0.9158 0.000 0.988 0.000 0.000 0.008 0.004
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 6 0.4317 0.6725 0.328 0.028 0.004 0.000 0.000 0.640
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 6 0.6375 0.5425 0.068 0.324 0.012 0.020 0.040 0.536
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0520 0.8725 0.984 0.000 0.000 0.008 0.000 0.008
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.8042 0.6523 0.220 0.000 0.168 0.348 0.028 0.236
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 6 0.5147 0.7790 0.180 0.176 0.004 0.000 0.000 0.640
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0146 0.9161 0.000 0.996 0.000 0.000 0.000 0.004
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.3946 0.5623 0.020 0.000 0.792 0.100 0.000 0.088
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 4 0.4266 -0.1363 0.348 0.000 0.000 0.628 0.008 0.016
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0146 0.9176 0.000 0.996 0.000 0.000 0.000 0.004
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 4 0.4303 -0.1121 0.332 0.000 0.000 0.640 0.012 0.016
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.1863 0.9042 0.000 0.000 0.060 0.016 0.920 0.004
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 6 0.6469 0.3656 0.384 0.008 0.000 0.136 0.036 0.436
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.8171 0.6120 0.212 0.000 0.208 0.300 0.028 0.252
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.2312 0.8119 0.876 0.000 0.000 0.112 0.000 0.012
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0146 0.9176 0.000 0.996 0.000 0.000 0.000 0.004
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0146 0.9176 0.000 0.996 0.000 0.000 0.000 0.004
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.3875 0.6375 0.000 0.000 0.780 0.068 0.144 0.008
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.2191 0.7615 0.876 0.000 0.004 0.000 0.000 0.120
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0260 0.8745 0.992 0.000 0.000 0.008 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.1411 0.8704 0.000 0.936 0.000 0.000 0.004 0.060
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.0865 0.3943 0.036 0.000 0.000 0.964 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 6 0.4905 0.7566 0.240 0.104 0.004 0.000 0.000 0.652
#> F900E9BE-2400-4451-9434-EE8BC513BA94 4 0.4303 -0.1650 0.360 0.000 0.000 0.616 0.008 0.016
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 4 0.4210 -0.1117 0.332 0.000 0.000 0.644 0.008 0.016
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.8042 0.6563 0.220 0.000 0.168 0.348 0.028 0.236
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.3562 0.6559 0.000 0.004 0.788 0.000 0.168 0.040
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.9172 0.000 1.000 0.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0363 0.8714 0.988 0.000 0.000 0.012 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.8042 0.6563 0.220 0.000 0.168 0.348 0.028 0.236
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.8033 0.6558 0.220 0.000 0.168 0.352 0.028 0.232
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 6 0.5187 0.6338 0.336 0.016 0.000 0.020 0.032 0.596
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.4589 0.5032 0.000 0.000 0.028 0.384 0.580 0.008
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.2389 0.8057 0.864 0.000 0.000 0.128 0.000 0.008
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.1957 0.7742 0.888 0.000 0.000 0.000 0.000 0.112
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.8042 0.6563 0.220 0.000 0.168 0.348 0.028 0.236
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0146 0.9176 0.000 0.996 0.000 0.000 0.000 0.004
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.3560 0.5759 0.004 0.000 0.808 0.104 0.000 0.084
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.1753 0.9272 0.000 0.000 0.084 0.000 0.912 0.004
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 17548 rows and 122 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 0.979 0.991 0.5019 0.499 0.499
#> 3 3 0.557 0.773 0.823 0.3032 0.807 0.632
#> 4 4 0.801 0.781 0.871 0.1374 0.857 0.622
#> 5 5 0.751 0.545 0.721 0.0435 0.878 0.636
#> 6 6 0.823 0.694 0.800 0.0466 0.839 0.491
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.000 0.987 1.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.000 0.996 0.000 1.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.000 0.987 1.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.000 0.987 1.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 2 0.224 0.959 0.036 0.964
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.000 0.996 0.000 1.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 2 0.000 0.996 0.000 1.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.000 0.987 1.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.000 0.987 1.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 1 0.000 0.987 1.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.000 0.996 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.000 0.996 0.000 1.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.000 0.987 1.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.000 0.996 0.000 1.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 2 0.000 0.996 0.000 1.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 2 0.000 0.996 0.000 1.000
#> 853120F0-857B-4108-9EC8-727189630C5F 2 0.000 0.996 0.000 1.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.000 0.987 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.000 0.996 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.000 0.996 0.000 1.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.000 0.987 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.000 0.996 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 2 0.000 0.996 0.000 1.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.000 0.987 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.000 0.996 0.000 1.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.000 0.987 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.000 0.987 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.000 0.987 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 2 0.000 0.996 0.000 1.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.000 0.996 0.000 1.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.000 0.996 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.000 0.987 1.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.000 0.987 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.000 0.987 1.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.000 0.987 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 2 0.000 0.996 0.000 1.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.000 0.987 1.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 2 0.000 0.996 0.000 1.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.000 0.996 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.000 0.987 1.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.000 0.996 0.000 1.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.000 0.987 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.000 0.987 1.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.000 0.987 1.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.000 0.996 0.000 1.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.000 0.996 0.000 1.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.000 0.996 0.000 1.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.000 0.996 0.000 1.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.000 0.987 1.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.000 0.987 1.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.000 0.996 0.000 1.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.000 0.987 1.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 2 0.000 0.996 0.000 1.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.000 0.987 1.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.730 0.742 0.796 0.204
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.000 0.987 1.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.000 0.996 0.000 1.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.000 0.987 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.000 0.987 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.000 0.996 0.000 1.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.000 0.987 1.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.000 0.996 0.000 1.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.000 0.996 0.000 1.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.000 0.987 1.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.000 0.996 0.000 1.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.000 0.987 1.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.000 0.987 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.000 0.987 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.000 0.987 1.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.000 0.987 1.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.000 0.996 0.000 1.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.000 0.996 0.000 1.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.000 0.996 0.000 1.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.000 0.996 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.000 0.987 1.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.000 0.996 0.000 1.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.000 0.987 1.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.000 0.987 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.000 0.987 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.000 0.996 0.000 1.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.000 0.987 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.000 0.987 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.000 0.987 1.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.000 0.996 0.000 1.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.000 0.996 0.000 1.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.000 0.996 0.000 1.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.000 0.987 1.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.000 0.987 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.000 0.996 0.000 1.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.000 0.996 0.000 1.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.000 0.987 1.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.000 0.987 1.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.000 0.996 0.000 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.000 0.987 1.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 2 0.722 0.745 0.200 0.800
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.000 0.996 0.000 1.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.000 0.987 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.000 0.987 1.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.000 0.996 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.000 0.996 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.981 0.279 0.580 0.420
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.000 0.987 1.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.000 0.987 1.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.000 0.996 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.000 0.987 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.000 0.996 0.000 1.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.000 0.987 1.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.000 0.987 1.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.000 0.987 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.000 0.996 0.000 1.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.000 0.996 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.000 0.987 1.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.000 0.987 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.000 0.987 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.000 0.996 0.000 1.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.706 0.761 0.808 0.192
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.000 0.987 1.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.000 0.987 1.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.000 0.987 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.000 0.996 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.000 0.987 1.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.000 0.996 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.5634 0.787 0.800 0.144 0.056
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.5835 0.663 0.000 0.660 0.340
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.4555 0.663 0.200 0.000 0.800
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.8505 0.715 0.600 0.144 0.256
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.8802 0.684 0.200 0.216 0.584
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.825 0.000 1.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.5327 0.573 0.000 0.272 0.728
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.5634 0.787 0.800 0.144 0.056
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.5634 0.787 0.800 0.144 0.056
#> 806616FE-1855-4284-9265-42842104CB21 3 0.4555 0.663 0.200 0.000 0.800
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.3752 0.908 0.000 0.856 0.144
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.825 0.000 1.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.5634 0.787 0.800 0.144 0.056
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.3752 0.908 0.000 0.856 0.144
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.5743 0.677 0.044 0.172 0.784
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.6678 0.642 0.060 0.216 0.724
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.2229 0.735 0.044 0.012 0.944
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.814 1.000 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.3752 0.908 0.000 0.856 0.144
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.3752 0.908 0.000 0.856 0.144
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.814 1.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.3752 0.908 0.000 0.856 0.144
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.6423 0.628 0.044 0.228 0.728
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.4750 0.735 0.784 0.000 0.216
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.825 0.000 1.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.814 1.000 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.814 1.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.814 1.000 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.2229 0.735 0.044 0.012 0.944
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.3752 0.908 0.000 0.856 0.144
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.3752 0.908 0.000 0.856 0.144
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.5634 0.787 0.800 0.144 0.056
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.4750 0.735 0.784 0.000 0.216
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.0000 0.814 1.000 0.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.814 1.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.2918 0.735 0.044 0.032 0.924
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.4702 0.738 0.788 0.000 0.212
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.1964 0.710 0.000 0.056 0.944
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.3752 0.908 0.000 0.856 0.144
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.5634 0.787 0.800 0.144 0.056
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.3752 0.908 0.000 0.856 0.144
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.4750 0.735 0.784 0.000 0.216
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.4750 0.735 0.784 0.000 0.216
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.814 1.000 0.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.5327 0.573 0.000 0.272 0.728
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.5327 0.573 0.000 0.272 0.728
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.3752 0.908 0.000 0.856 0.144
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.3752 0.908 0.000 0.856 0.144
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.5634 0.787 0.800 0.144 0.056
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.6168 0.583 0.412 0.000 0.588
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.3752 0.908 0.000 0.856 0.144
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.6126 0.593 0.400 0.000 0.600
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.2550 0.717 0.012 0.056 0.932
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.8505 0.715 0.600 0.144 0.256
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.6180 0.579 0.416 0.000 0.584
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.8623 0.704 0.584 0.144 0.272
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0592 0.815 0.000 0.988 0.012
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.4750 0.735 0.784 0.000 0.216
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.814 1.000 0.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.5431 0.722 0.000 0.716 0.284
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.6180 0.579 0.416 0.000 0.584
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.3752 0.908 0.000 0.856 0.144
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0592 0.815 0.000 0.988 0.012
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.4555 0.663 0.200 0.000 0.800
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.5327 0.573 0.000 0.272 0.728
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.0000 0.814 1.000 0.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.814 1.000 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.814 1.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.4750 0.735 0.784 0.000 0.216
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.5138 0.757 0.748 0.000 0.252
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.3752 0.908 0.000 0.856 0.144
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.825 0.000 1.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.3752 0.908 0.000 0.856 0.144
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.3752 0.908 0.000 0.856 0.144
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.5634 0.787 0.800 0.144 0.056
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.3752 0.908 0.000 0.856 0.144
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.5634 0.787 0.800 0.144 0.056
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.814 1.000 0.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.4750 0.735 0.784 0.000 0.216
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.4605 0.577 0.000 0.796 0.204
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.4750 0.735 0.784 0.000 0.216
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.814 1.000 0.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.5634 0.787 0.800 0.144 0.056
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.3752 0.908 0.000 0.856 0.144
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.1964 0.768 0.000 0.944 0.056
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.3752 0.908 0.000 0.856 0.144
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.5634 0.787 0.800 0.144 0.056
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.814 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0592 0.815 0.000 0.988 0.012
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.3752 0.908 0.000 0.856 0.144
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.6192 0.573 0.420 0.000 0.580
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.8571 0.706 0.588 0.140 0.272
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.3752 0.908 0.000 0.856 0.144
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.5216 0.743 0.740 0.000 0.260
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.2229 0.735 0.044 0.012 0.944
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.4750 0.551 0.000 0.784 0.216
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.814 1.000 0.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.8505 0.715 0.600 0.144 0.256
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.3752 0.908 0.000 0.856 0.144
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.3752 0.908 0.000 0.856 0.144
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.4555 0.663 0.200 0.000 0.800
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.5634 0.787 0.800 0.144 0.056
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.5634 0.787 0.800 0.144 0.056
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.3752 0.908 0.000 0.856 0.144
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.4750 0.735 0.784 0.000 0.216
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0592 0.815 0.000 0.988 0.012
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.8623 0.704 0.584 0.144 0.272
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.5327 0.743 0.728 0.000 0.272
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.814 1.000 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.5327 0.573 0.000 0.272 0.728
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.3752 0.908 0.000 0.856 0.144
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.5634 0.787 0.800 0.144 0.056
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.814 1.000 0.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.814 1.000 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0592 0.815 0.000 0.988 0.012
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.2229 0.735 0.044 0.012 0.944
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.8505 0.715 0.600 0.144 0.256
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.5634 0.787 0.800 0.144 0.056
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.814 1.000 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.3752 0.908 0.000 0.856 0.144
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.6126 0.593 0.400 0.000 0.600
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.5327 0.573 0.000 0.272 0.728
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0188 0.755 0.996 0.000 0.000 0.004
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.4801 0.720 0.000 0.764 0.048 0.188
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.4817 0.540 0.000 0.000 0.612 0.388
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.4804 0.659 0.616 0.000 0.000 0.384
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0336 0.909 0.000 0.000 0.992 0.008
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0188 0.913 0.000 0.004 0.996 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0336 0.748 0.992 0.000 0.000 0.008
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0336 0.748 0.992 0.000 0.000 0.008
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0336 0.909 0.000 0.000 0.992 0.008
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0188 0.755 0.996 0.000 0.000 0.004
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0188 0.960 0.000 0.996 0.004 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.0188 0.914 0.000 0.000 0.996 0.004
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0188 0.913 0.000 0.004 0.996 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.0188 0.914 0.000 0.000 0.996 0.004
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.4817 0.722 0.388 0.000 0.000 0.612
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0188 0.960 0.000 0.996 0.004 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0188 0.960 0.000 0.996 0.004 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.4817 0.722 0.388 0.000 0.000 0.612
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0188 0.960 0.000 0.996 0.004 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.0376 0.913 0.000 0.004 0.992 0.004
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.0657 0.584 0.012 0.000 0.004 0.984
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.4817 0.722 0.388 0.000 0.000 0.612
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.4817 0.722 0.388 0.000 0.000 0.612
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.4817 0.722 0.388 0.000 0.000 0.612
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.0188 0.914 0.000 0.000 0.996 0.004
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0188 0.960 0.000 0.996 0.004 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.754 1.000 0.000 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.0657 0.584 0.012 0.000 0.004 0.984
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.4817 0.722 0.388 0.000 0.000 0.612
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.4817 0.722 0.388 0.000 0.000 0.612
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.0188 0.914 0.000 0.000 0.996 0.004
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.0188 0.590 0.004 0.000 0.000 0.996
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0000 0.913 0.000 0.000 1.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0188 0.960 0.000 0.996 0.004 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0188 0.755 0.996 0.000 0.000 0.004
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.0657 0.584 0.012 0.000 0.004 0.984
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.2611 0.500 0.008 0.000 0.096 0.896
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.4817 0.722 0.388 0.000 0.000 0.612
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0188 0.913 0.000 0.004 0.996 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.0376 0.913 0.000 0.004 0.992 0.004
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0336 0.748 0.992 0.000 0.000 0.008
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.4978 0.472 0.004 0.000 0.384 0.612
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.4978 0.472 0.004 0.000 0.384 0.612
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0000 0.913 0.000 0.000 1.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.4804 0.659 0.616 0.000 0.000 0.384
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.4978 0.472 0.004 0.000 0.384 0.612
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.4978 0.657 0.612 0.000 0.004 0.384
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.0657 0.584 0.012 0.000 0.004 0.984
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.4817 0.722 0.388 0.000 0.000 0.612
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.4961 0.211 0.000 0.552 0.448 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.4978 0.472 0.004 0.000 0.384 0.612
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0188 0.960 0.000 0.996 0.004 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.3356 0.786 0.000 0.000 0.824 0.176
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0188 0.913 0.000 0.004 0.996 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.4978 0.721 0.384 0.000 0.004 0.612
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.4817 0.722 0.388 0.000 0.000 0.612
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.4817 0.722 0.388 0.000 0.000 0.612
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.0188 0.590 0.000 0.000 0.004 0.996
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.4804 0.659 0.616 0.000 0.000 0.384
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.754 1.000 0.000 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0188 0.960 0.000 0.996 0.004 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0188 0.755 0.996 0.000 0.000 0.004
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.4817 0.722 0.388 0.000 0.000 0.612
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.0657 0.584 0.012 0.000 0.004 0.984
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.2944 0.834 0.000 0.868 0.004 0.128
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.0657 0.584 0.012 0.000 0.004 0.984
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.2011 0.619 0.080 0.000 0.000 0.920
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0188 0.755 0.996 0.000 0.000 0.004
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0188 0.960 0.000 0.996 0.004 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.3610 0.730 0.200 0.800 0.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.754 1.000 0.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.4817 0.722 0.388 0.000 0.000 0.612
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0188 0.960 0.000 0.996 0.004 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.4978 0.472 0.004 0.000 0.384 0.612
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.4978 0.657 0.612 0.000 0.004 0.384
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0188 0.960 0.000 0.996 0.004 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.4978 0.657 0.612 0.000 0.004 0.384
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.3649 0.761 0.000 0.000 0.796 0.204
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.5887 0.447 0.036 0.600 0.004 0.360
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.4817 0.722 0.388 0.000 0.000 0.612
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.4804 0.659 0.616 0.000 0.000 0.384
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0188 0.960 0.000 0.996 0.004 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0188 0.960 0.000 0.996 0.004 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.4500 0.636 0.000 0.000 0.684 0.316
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0336 0.748 0.992 0.000 0.000 0.008
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0188 0.755 0.996 0.000 0.000 0.004
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.0657 0.584 0.012 0.000 0.004 0.984
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.4978 0.657 0.612 0.000 0.004 0.384
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.4978 0.657 0.612 0.000 0.004 0.384
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.4817 0.722 0.388 0.000 0.000 0.612
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0188 0.913 0.000 0.004 0.996 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0188 0.960 0.000 0.996 0.004 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0336 0.748 0.992 0.000 0.000 0.008
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.4817 0.722 0.388 0.000 0.000 0.612
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.4817 0.722 0.388 0.000 0.000 0.612
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0000 0.961 0.000 1.000 0.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.4804 0.547 0.000 0.000 0.616 0.384
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.4804 0.659 0.616 0.000 0.000 0.384
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.0188 0.751 0.996 0.000 0.000 0.004
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.4817 0.722 0.388 0.000 0.000 0.612
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0188 0.960 0.000 0.996 0.004 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.4978 0.472 0.004 0.000 0.384 0.612
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.0376 0.913 0.000 0.004 0.992 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.4291 0.5890 0.536 0.000 NA 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.5524 0.4763 0.276 0.628 NA 0.000 0.092
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.7853 -0.1983 0.448 0.000 NA 0.260 0.112
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.3143 0.5334 0.796 0.000 NA 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 4 0.6439 -0.2406 0.000 0.000 NA 0.448 0.372
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.2127 0.8929 0.000 0.892 NA 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 4 0.6450 -0.2512 0.000 0.000 NA 0.436 0.384
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.4291 0.5890 0.536 0.000 NA 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.4291 0.5890 0.536 0.000 NA 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 4 0.6439 -0.2406 0.000 0.000 NA 0.448 0.372
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.9208 0.000 1.000 NA 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.2127 0.8929 0.000 0.892 NA 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.4291 0.5890 0.536 0.000 NA 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0162 0.9196 0.000 0.996 NA 0.000 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.0000 0.9438 0.000 0.000 NA 0.000 1.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 4 0.6439 -0.2406 0.000 0.000 NA 0.448 0.372
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0000 0.9438 0.000 0.000 NA 0.000 1.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.4273 0.5623 0.000 0.000 NA 0.552 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.9208 0.000 1.000 NA 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.9208 0.000 1.000 NA 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.4273 0.5623 0.000 0.000 NA 0.552 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.9208 0.000 1.000 NA 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.0000 0.9438 0.000 0.000 NA 0.000 1.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.6586 -0.1288 0.464 0.000 NA 0.292 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.2127 0.8929 0.000 0.892 NA 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.4273 0.5623 0.000 0.000 NA 0.552 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.4273 0.5623 0.000 0.000 NA 0.552 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.4273 0.5623 0.000 0.000 NA 0.552 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.0000 0.9438 0.000 0.000 NA 0.000 1.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.0451 0.9188 0.000 0.988 NA 0.000 0.004
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.9208 0.000 1.000 NA 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.4291 0.5890 0.536 0.000 NA 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.6586 -0.1288 0.464 0.000 NA 0.292 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.4273 0.5623 0.000 0.000 NA 0.552 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.4273 0.5623 0.000 0.000 NA 0.552 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0000 0.9438 0.000 0.000 NA 0.000 1.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.6191 0.4266 0.204 0.000 NA 0.552 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 4 0.6453 -0.2555 0.000 0.000 NA 0.432 0.388
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.9208 0.000 1.000 NA 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.4291 0.5890 0.536 0.000 NA 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.1965 0.8971 0.000 0.904 NA 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.6586 -0.1288 0.464 0.000 NA 0.292 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.6849 -0.2234 0.464 0.000 NA 0.096 0.388
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.4249 0.5613 0.000 0.000 NA 0.568 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 4 0.6450 -0.2512 0.000 0.000 NA 0.436 0.384
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0000 0.9438 0.000 0.000 NA 0.000 1.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.9208 0.000 1.000 NA 0.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.2127 0.8929 0.000 0.892 NA 0.000 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.4291 0.5890 0.536 0.000 NA 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.0000 0.4372 0.000 0.000 NA 1.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.9208 0.000 1.000 NA 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.0000 0.4372 0.000 0.000 NA 1.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 4 0.6439 -0.2406 0.000 0.000 NA 0.448 0.372
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.3452 0.5407 0.756 0.000 NA 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.0162 0.4340 0.000 0.000 NA 0.996 0.004
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0162 0.4600 0.996 0.000 NA 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.2127 0.8929 0.000 0.892 NA 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.6586 -0.1288 0.464 0.000 NA 0.292 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.4781 0.5547 0.020 0.000 NA 0.552 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.7986 0.0524 0.000 0.452 NA 0.148 0.204
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.0000 0.4372 0.000 0.000 NA 1.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.9208 0.000 1.000 NA 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.2127 0.8929 0.000 0.892 NA 0.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 4 0.7985 -0.1887 0.236 0.000 NA 0.444 0.140
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 4 0.6453 -0.2555 0.000 0.000 NA 0.432 0.388
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.3424 0.5219 0.000 0.000 NA 0.760 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.4273 0.5623 0.000 0.000 NA 0.552 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.4273 0.5623 0.000 0.000 NA 0.552 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.6569 0.3297 0.292 0.000 NA 0.468 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.3003 0.5284 0.812 0.000 NA 0.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.1908 0.8984 0.000 0.908 NA 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.2127 0.8929 0.000 0.892 NA 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0162 0.9196 0.000 0.996 NA 0.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0404 0.9190 0.000 0.988 NA 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.4291 0.5890 0.536 0.000 NA 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0162 0.9195 0.000 0.996 NA 0.000 0.004
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.4291 0.5890 0.536 0.000 NA 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.4781 0.5547 0.020 0.000 NA 0.552 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.6586 -0.1288 0.464 0.000 NA 0.292 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.3731 0.7617 0.160 0.800 NA 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.6586 -0.1288 0.464 0.000 NA 0.292 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.6191 0.4266 0.204 0.000 NA 0.552 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.4291 0.5890 0.536 0.000 NA 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.9208 0.000 1.000 NA 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.5074 0.6821 0.168 0.700 NA 0.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0162 0.9196 0.000 0.996 NA 0.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.4291 0.5890 0.536 0.000 NA 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.4273 0.5623 0.000 0.000 NA 0.552 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.2127 0.8929 0.000 0.892 NA 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.9208 0.000 1.000 NA 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.1041 0.4526 0.000 0.000 NA 0.964 0.004
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.4581 1.000 0.000 NA 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.9208 0.000 1.000 NA 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0794 0.4471 0.972 0.000 NA 0.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.0404 0.9340 0.012 0.000 NA 0.000 0.988
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.4497 0.3672 0.424 0.568 NA 0.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.4273 0.5623 0.000 0.000 NA 0.552 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.3452 0.5407 0.756 0.000 NA 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.9208 0.000 1.000 NA 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.9208 0.000 1.000 NA 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.8107 -0.2462 0.368 0.000 NA 0.324 0.128
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.4302 0.5822 0.520 0.000 NA 0.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.4291 0.5890 0.536 0.000 NA 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0609 0.9174 0.000 0.980 NA 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.6586 -0.1288 0.464 0.000 NA 0.292 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.2127 0.8929 0.000 0.892 NA 0.000 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.4581 1.000 0.000 NA 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0162 0.4570 0.996 0.000 NA 0.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.4273 0.5623 0.000 0.000 NA 0.552 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 4 0.6443 -0.2438 0.000 0.000 NA 0.444 0.376
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.9208 0.000 1.000 NA 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.4291 0.5890 0.536 0.000 NA 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.4273 0.5623 0.000 0.000 NA 0.552 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.4273 0.5623 0.000 0.000 NA 0.552 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.1851 0.9004 0.000 0.912 NA 0.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.4126 0.4841 0.380 0.000 NA 0.000 0.620
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.3274 0.5366 0.780 0.000 NA 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.4302 0.5822 0.520 0.000 NA 0.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.4273 0.5623 0.000 0.000 NA 0.552 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.9208 0.000 1.000 NA 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.0162 0.4340 0.000 0.000 NA 0.996 0.004
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0000 0.9438 0.000 0.000 NA 0.000 1.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.1327 0.7824 0.936 0.000 0.000 0.064 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.3930 0.4899 0.000 0.764 0.000 0.000 0.092 0.144
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.5956 0.2839 0.056 0.000 0.528 0.000 0.080 0.336
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.4020 0.6233 0.692 0.000 0.000 0.000 0.032 0.276
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0146 0.7229 0.000 0.004 0.996 0.000 0.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 6 0.3851 0.9340 0.000 0.460 0.000 0.000 0.000 0.540
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0291 0.7217 0.000 0.004 0.992 0.000 0.004 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.1327 0.7824 0.936 0.000 0.000 0.064 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.1327 0.7824 0.936 0.000 0.000 0.064 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0146 0.7224 0.000 0.000 0.996 0.004 0.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0146 0.8362 0.000 0.996 0.000 0.000 0.004 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 6 0.3851 0.9340 0.000 0.460 0.000 0.000 0.000 0.540
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.1327 0.7824 0.936 0.000 0.000 0.064 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0260 0.8342 0.000 0.992 0.000 0.000 0.008 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.1663 0.8790 0.000 0.000 0.088 0.000 0.912 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0146 0.7229 0.000 0.004 0.996 0.000 0.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.1663 0.8790 0.000 0.000 0.088 0.000 0.912 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0146 0.8014 0.000 0.000 0.000 0.996 0.000 0.004
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.8369 0.000 1.000 0.000 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0146 0.8362 0.000 0.996 0.000 0.000 0.004 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.8032 0.000 0.000 0.000 1.000 0.000 0.000
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#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.1663 0.8790 0.000 0.000 0.088 0.000 0.912 0.000
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#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.0260 0.7992 0.000 0.000 0.000 0.992 0.000 0.008
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#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.1663 0.8790 0.000 0.000 0.088 0.000 0.912 0.000
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#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0405 0.7197 0.000 0.004 0.988 0.000 0.008 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.8369 0.000 1.000 0.000 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.1686 0.7818 0.924 0.000 0.000 0.064 0.000 0.012
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 6 0.3868 0.8590 0.000 0.496 0.000 0.000 0.000 0.504
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.6282 0.4460 0.064 0.000 0.004 0.452 0.080 0.400
#> CB925BF0-1249-4350-A175-9A4129C43B8D 5 0.5785 0.2873 0.064 0.000 0.004 0.036 0.464 0.432
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.0260 0.7992 0.000 0.000 0.000 0.992 0.000 0.008
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0291 0.7217 0.000 0.004 0.992 0.000 0.004 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.1663 0.8790 0.000 0.000 0.088 0.000 0.912 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0291 0.8348 0.000 0.992 0.000 0.000 0.004 0.004
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 6 0.3851 0.9340 0.000 0.460 0.000 0.000 0.000 0.540
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.1327 0.7824 0.936 0.000 0.000 0.064 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.3819 0.5543 0.000 0.000 0.652 0.340 0.000 0.008
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0260 0.8342 0.000 0.992 0.000 0.000 0.008 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.3833 0.5475 0.000 0.000 0.648 0.344 0.000 0.008
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0146 0.7229 0.000 0.004 0.996 0.000 0.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.2513 0.7118 0.852 0.000 0.000 0.000 0.008 0.140
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.3804 0.5597 0.000 0.000 0.656 0.336 0.000 0.008
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.5188 0.4687 0.496 0.000 0.004 0.000 0.076 0.424
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 6 0.4238 0.9315 0.016 0.444 0.000 0.000 0.000 0.540
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.6282 0.4460 0.064 0.000 0.004 0.452 0.080 0.400
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0146 0.8022 0.004 0.000 0.000 0.996 0.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.5147 0.0368 0.000 0.316 0.576 0.000 0.000 0.108
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.3819 0.5543 0.000 0.000 0.652 0.340 0.000 0.008
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.8369 0.000 1.000 0.000 0.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 6 0.4238 0.9315 0.016 0.444 0.000 0.000 0.000 0.540
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.2448 0.6713 0.000 0.000 0.884 0.000 0.064 0.052
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0405 0.7197 0.000 0.004 0.988 0.000 0.008 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.1757 0.7327 0.000 0.000 0.076 0.916 0.000 0.008
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0000 0.8032 0.000 0.000 0.000 1.000 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.8032 0.000 0.000 0.000 1.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.5748 0.5114 0.064 0.000 0.004 0.552 0.044 0.336
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.4803 0.5236 0.556 0.000 0.000 0.008 0.040 0.396
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.3817 -0.6914 0.000 0.568 0.000 0.000 0.000 0.432
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 6 0.3851 0.9340 0.000 0.460 0.000 0.000 0.000 0.540
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0405 0.8307 0.000 0.988 0.000 0.000 0.008 0.004
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.2491 0.5321 0.000 0.836 0.000 0.000 0.000 0.164
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.1500 0.7783 0.936 0.000 0.000 0.052 0.000 0.012
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0865 0.7940 0.000 0.964 0.000 0.000 0.000 0.036
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.1779 0.7809 0.920 0.000 0.000 0.064 0.000 0.016
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0260 0.8005 0.008 0.000 0.000 0.992 0.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.6288 0.4307 0.064 0.000 0.004 0.440 0.080 0.412
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.3634 0.3202 0.000 0.696 0.000 0.000 0.008 0.296
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.6282 0.4460 0.064 0.000 0.004 0.452 0.080 0.400
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.1972 0.7599 0.056 0.000 0.000 0.916 0.024 0.004
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.1327 0.7824 0.936 0.000 0.000 0.064 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0146 0.8362 0.000 0.996 0.000 0.000 0.004 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 6 0.5523 0.6559 0.164 0.296 0.000 0.000 0.000 0.540
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0405 0.8307 0.000 0.988 0.000 0.000 0.008 0.004
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.1686 0.7818 0.924 0.000 0.000 0.064 0.000 0.012
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0146 0.8014 0.000 0.000 0.000 0.996 0.000 0.004
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 6 0.4238 0.9315 0.016 0.444 0.000 0.000 0.000 0.540
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0260 0.8342 0.000 0.992 0.000 0.000 0.008 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.4039 0.3858 0.000 0.000 0.568 0.424 0.000 0.008
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.5370 0.4414 0.464 0.000 0.004 0.004 0.080 0.448
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.8369 0.000 1.000 0.000 0.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.5631 0.4278 0.452 0.000 0.004 0.016 0.080 0.448
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.1610 0.8761 0.000 0.000 0.084 0.000 0.916 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.5779 0.1725 0.044 0.540 0.004 0.000 0.064 0.348
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0260 0.7992 0.000 0.000 0.000 0.992 0.000 0.008
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.2513 0.7118 0.852 0.000 0.000 0.000 0.008 0.140
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.8369 0.000 1.000 0.000 0.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.8369 0.000 1.000 0.000 0.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.4954 0.4839 0.032 0.000 0.688 0.000 0.076 0.204
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.2197 0.7533 0.900 0.000 0.000 0.044 0.000 0.056
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.1686 0.7818 0.924 0.000 0.000 0.064 0.000 0.012
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.2762 0.4309 0.000 0.804 0.000 0.000 0.000 0.196
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.6282 0.4460 0.064 0.000 0.004 0.452 0.080 0.400
#> B3561356-5A80-4C79-B23A-D518425565FE 6 0.4238 0.9315 0.016 0.444 0.000 0.000 0.000 0.540
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.5370 0.4414 0.464 0.000 0.004 0.004 0.080 0.448
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.5468 0.4372 0.460 0.000 0.004 0.008 0.080 0.448
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.8032 0.000 0.000 0.000 1.000 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0146 0.7229 0.000 0.004 0.996 0.000 0.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.8369 0.000 1.000 0.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.1327 0.7824 0.936 0.000 0.000 0.064 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0000 0.8032 0.000 0.000 0.000 1.000 0.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0000 0.8032 0.000 0.000 0.000 1.000 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.3874 -0.4021 0.000 0.636 0.000 0.000 0.008 0.356
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.3770 0.5903 0.032 0.000 0.004 0.000 0.752 0.212
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.3695 0.6484 0.732 0.000 0.000 0.000 0.024 0.244
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.2258 0.7504 0.896 0.000 0.000 0.044 0.000 0.060
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.8032 0.000 0.000 0.000 1.000 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.8369 0.000 1.000 0.000 0.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.3774 0.5664 0.000 0.000 0.664 0.328 0.000 0.008
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.1663 0.8790 0.000 0.000 0.088 0.000 0.912 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["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 17548 rows and 122 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 0.848 0.945 0.970 0.4726 0.519 0.519
#> 3 3 0.508 0.650 0.788 0.3672 0.690 0.470
#> 4 4 0.651 0.643 0.817 0.1323 0.821 0.536
#> 5 5 0.692 0.572 0.746 0.0673 0.873 0.583
#> 6 6 0.846 0.719 0.831 0.0442 0.883 0.557
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.1184 0.9737 0.984 0.016
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.4939 0.8812 0.108 0.892
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.9839 1.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.9839 1.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 2 0.7528 0.7985 0.216 0.784
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.4562 0.9096 0.096 0.904
#> 9264567D-4524-46AF-A851-C091C3CD76CF 2 0.0000 0.9451 0.000 1.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.9839 1.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.1184 0.9737 0.984 0.016
#> 806616FE-1855-4284-9265-42842104CB21 1 0.0000 0.9839 1.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.9451 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.4431 0.9117 0.092 0.908
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.1184 0.9737 0.984 0.016
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.9451 0.000 1.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 1 0.0672 0.9782 0.992 0.008
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 2 0.6887 0.8399 0.184 0.816
#> 853120F0-857B-4108-9EC8-727189630C5F 1 0.0000 0.9839 1.000 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.9839 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.9451 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.9451 0.000 1.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.9839 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.9451 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 1 0.0000 0.9839 1.000 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.9839 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.4562 0.9096 0.096 0.904
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.9839 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.9839 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.9839 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.3584 0.9163 0.932 0.068
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.8144 0.6367 0.748 0.252
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.9451 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.1184 0.9737 0.984 0.016
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.9839 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.0000 0.9839 1.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.9839 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 1 0.0000 0.9839 1.000 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0000 0.9839 1.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 2 0.1184 0.9398 0.016 0.984
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.9451 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.9839 1.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.9451 0.000 1.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.9839 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.9839 1.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.9839 1.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.1184 0.9398 0.016 0.984
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.3584 0.9209 0.068 0.932
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.4939 0.9023 0.108 0.892
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.6343 0.8612 0.160 0.840
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.9839 1.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.0000 0.9839 1.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.9451 0.000 1.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.0000 0.9839 1.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 2 0.6343 0.8651 0.160 0.840
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.1184 0.9737 0.984 0.016
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.3431 0.9193 0.936 0.064
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.1184 0.9737 0.984 0.016
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.4939 0.9023 0.108 0.892
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.9839 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.9839 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.0672 0.9437 0.008 0.992
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.0000 0.9839 1.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.9451 0.000 1.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.6343 0.8612 0.160 0.840
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.0000 0.9839 1.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.0000 0.9451 0.000 1.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.0000 0.9839 1.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.9839 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.9839 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0000 0.9839 1.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.9839 1.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.9451 0.000 1.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.4562 0.9096 0.096 0.904
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.9451 0.000 1.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.9451 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.1184 0.9737 0.984 0.016
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.9451 0.000 1.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.9839 1.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.9839 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.9839 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.7139 0.8155 0.196 0.804
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.9839 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.9839 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.1184 0.9737 0.984 0.016
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.9451 0.000 1.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.6973 0.8296 0.188 0.812
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0000 0.9451 0.000 1.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.9839 1.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.9839 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.6343 0.8612 0.160 0.840
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.9451 0.000 1.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.0000 0.9839 1.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.9839 1.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.9451 0.000 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.9839 1.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.0376 0.9812 0.996 0.004
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.9996 -0.0477 0.512 0.488
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.9839 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.1184 0.9737 0.984 0.016
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.9451 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.9451 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0000 0.9839 1.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.1184 0.9737 0.984 0.016
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0938 0.9764 0.988 0.012
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.9451 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.9839 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.6343 0.8612 0.160 0.840
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.9839 1.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.9839 1.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.9839 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.1184 0.9398 0.016 0.984
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.9451 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.1184 0.9737 0.984 0.016
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.9839 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.9839 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.4815 0.9049 0.104 0.896
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0000 0.9839 1.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.9839 1.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.1184 0.9737 0.984 0.016
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.9839 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.9451 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.0000 0.9839 1.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.1633 0.9391 0.024 0.976
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.4452 0.6778 0.808 0.000 0.192
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.3551 0.7512 0.132 0.868 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.4452 0.6665 0.192 0.000 0.808
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.5138 0.6039 0.748 0.000 0.252
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.9276 0.5007 0.264 0.212 0.524
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 1 0.6192 0.4085 0.580 0.420 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 2 0.4452 0.7465 0.192 0.808 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.4452 0.6778 0.808 0.000 0.192
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.4452 0.6778 0.808 0.000 0.192
#> 806616FE-1855-4284-9265-42842104CB21 3 0.5254 0.6533 0.264 0.000 0.736
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.8759 0.000 1.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 1 0.6215 0.3917 0.572 0.428 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.4452 0.6778 0.808 0.000 0.192
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.2066 0.8236 0.060 0.940 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.6299 0.6700 0.476 0.000 0.524
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.9276 0.5007 0.264 0.212 0.524
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.6192 0.6921 0.420 0.000 0.580
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 3 0.5138 0.7182 0.252 0.000 0.748
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.8759 0.000 1.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.8759 0.000 1.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 3 0.4750 0.7377 0.216 0.000 0.784
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.8759 0.000 1.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.5529 0.6974 0.296 0.000 0.704
#> F5A814F6-E824-4DB2-8497-4B99E151D450 3 0.0000 0.7334 0.000 0.000 1.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 1 0.6168 0.4239 0.588 0.412 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 3 0.4702 0.7393 0.212 0.000 0.788
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 3 0.4702 0.7393 0.212 0.000 0.788
#> 4496EE84-2C36-413B-A328-A5B598A6C387 3 0.4702 0.7393 0.212 0.000 0.788
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.8515 -0.5984 0.476 0.092 0.432
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.5667 0.6671 0.800 0.140 0.060
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.8759 0.000 1.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.4452 0.6778 0.808 0.000 0.192
#> F5940915-4123-49B3-95EE-4A0412BE8C30 3 0.2448 0.7223 0.076 0.000 0.924
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.5948 0.7014 0.360 0.000 0.640
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 3 0.4702 0.7393 0.212 0.000 0.788
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.6192 0.6921 0.420 0.000 0.580
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 3 0.5431 0.6930 0.284 0.000 0.716
#> 50D620F3-5C52-42FB-89A1-6840A7444647 2 0.8758 0.5113 0.192 0.588 0.220
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.8759 0.000 1.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.4452 0.6778 0.808 0.000 0.192
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.6302 -0.1927 0.480 0.520 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 3 0.0000 0.7334 0.000 0.000 1.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.0000 0.7334 0.000 0.000 1.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.6252 0.6916 0.444 0.000 0.556
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.4452 0.7465 0.192 0.808 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.7778 0.5835 0.264 0.644 0.092
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.6168 0.4239 0.588 0.412 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 1 0.6168 0.4239 0.588 0.412 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.4452 0.6778 0.808 0.000 0.192
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.6299 0.6700 0.476 0.000 0.524
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.8759 0.000 1.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.6299 0.6700 0.476 0.000 0.524
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.9340 0.4891 0.264 0.220 0.516
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.4452 0.6778 0.808 0.000 0.192
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.7112 -0.5388 0.552 0.024 0.424
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.4842 0.6412 0.776 0.000 0.224
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 1 0.6168 0.4239 0.588 0.412 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 3 0.0000 0.7334 0.000 0.000 1.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 3 0.4555 0.7421 0.200 0.000 0.800
#> 84E18629-1B13-4696-8E54-121ABE469CD2 1 0.6180 0.4167 0.584 0.416 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.6299 0.6700 0.476 0.000 0.524
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.8759 0.000 1.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 1 0.6168 0.4239 0.588 0.412 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.4452 0.6665 0.192 0.000 0.808
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.4452 0.7465 0.192 0.808 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.6299 0.6700 0.476 0.000 0.524
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 3 0.5138 0.7182 0.252 0.000 0.748
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 3 0.4702 0.7393 0.212 0.000 0.788
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.4452 0.6665 0.192 0.000 0.808
#> 352471DC-A881-4EA8-B646-EB1200291893 3 0.6079 0.4693 0.388 0.000 0.612
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.6180 0.0271 0.416 0.584 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 1 0.6180 0.4165 0.584 0.416 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.8759 0.000 1.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.8759 0.000 1.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.4452 0.6778 0.808 0.000 0.192
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.8759 0.000 1.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.4504 0.6734 0.804 0.000 0.196
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 3 0.2261 0.7460 0.068 0.000 0.932
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 3 0.0000 0.7334 0.000 0.000 1.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.5678 0.5325 0.684 0.316 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 3 0.0237 0.7333 0.004 0.000 0.996
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 3 0.0000 0.7334 0.000 0.000 1.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.4452 0.6778 0.808 0.000 0.192
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.8759 0.000 1.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 1 0.6140 0.4316 0.596 0.404 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.2356 0.8121 0.072 0.928 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.4452 0.6778 0.808 0.000 0.192
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 3 0.5178 0.7160 0.256 0.000 0.744
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 1 0.6168 0.4239 0.588 0.412 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.8759 0.000 1.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.1860 0.4945 0.948 0.000 0.052
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 3 0.4504 0.5218 0.196 0.000 0.804
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.8759 0.000 1.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 3 0.0000 0.7334 0.000 0.000 1.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.3412 0.7180 0.124 0.000 0.876
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.5667 0.6671 0.800 0.140 0.060
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 3 0.5988 0.7004 0.368 0.000 0.632
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.4452 0.6778 0.808 0.000 0.192
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.8759 0.000 1.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.8759 0.000 1.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.4842 0.6623 0.224 0.000 0.776
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.4452 0.6778 0.808 0.000 0.192
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.4452 0.6778 0.808 0.000 0.192
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.8759 0.000 1.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 3 0.0000 0.7334 0.000 0.000 1.000
#> B3561356-5A80-4C79-B23A-D518425565FE 1 0.6168 0.4239 0.588 0.412 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 3 0.5098 0.3776 0.248 0.000 0.752
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 3 0.0747 0.7267 0.016 0.000 0.984
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 3 0.4702 0.7393 0.212 0.000 0.788
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.7040 0.6416 0.252 0.688 0.060
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.8759 0.000 1.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.4452 0.6778 0.808 0.000 0.192
#> 472B75A2-A8C0-4503-B212-CADB781802EB 3 0.4702 0.7393 0.212 0.000 0.788
#> F205F9FC-F2D5-4164-9A40-1279647F900B 3 0.4702 0.7393 0.212 0.000 0.788
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.6168 0.4239 0.588 0.412 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.0000 0.7334 0.000 0.000 1.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.4452 0.6778 0.808 0.000 0.192
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.4452 0.6778 0.808 0.000 0.192
#> FA716037-886B-4DD0-8016-686C2D24550A 3 0.4702 0.7393 0.212 0.000 0.788
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.8759 0.000 1.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.6299 0.6700 0.476 0.000 0.524
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.4291 0.7609 0.180 0.820 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0469 0.7234 0.988 0.000 0.000 0.012
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.2593 0.8293 0.080 0.904 0.016 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.4967 0.5355 0.000 0.000 0.548 0.452
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0469 0.7234 0.988 0.000 0.000 0.012
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.3837 0.7700 0.000 0.000 0.776 0.224
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 1 0.5408 0.3706 0.576 0.408 0.016 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.3837 0.6844 0.000 0.224 0.776 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0469 0.7234 0.988 0.000 0.000 0.012
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0469 0.7234 0.988 0.000 0.000 0.012
#> 806616FE-1855-4284-9265-42842104CB21 3 0.3837 0.7700 0.000 0.000 0.776 0.224
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.9158 0.000 1.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 1 0.5444 0.3327 0.560 0.424 0.016 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0469 0.7234 0.988 0.000 0.000 0.012
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.1059 0.9028 0.012 0.972 0.016 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.0592 0.7257 0.000 0.000 0.984 0.016
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.3837 0.7700 0.000 0.000 0.776 0.224
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.3852 0.5983 0.180 0.000 0.808 0.012
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.4916 0.6424 0.424 0.000 0.000 0.576
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.9158 0.000 1.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.9158 0.000 1.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.4916 0.6424 0.424 0.000 0.000 0.576
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.9158 0.000 1.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 1 0.4040 0.4945 0.752 0.000 0.248 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.0000 0.6659 0.000 0.000 0.000 1.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 1 0.5408 0.3706 0.576 0.408 0.016 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.4898 0.6464 0.416 0.000 0.000 0.584
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.4916 0.6424 0.424 0.000 0.000 0.576
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.1022 0.6746 0.032 0.000 0.000 0.968
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.1059 0.7246 0.016 0.000 0.972 0.012
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.2342 0.6984 0.912 0.080 0.008 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.9158 0.000 1.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0469 0.7234 0.988 0.000 0.000 0.012
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.0000 0.6659 0.000 0.000 0.000 1.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.6042 -0.2257 0.580 0.000 0.052 0.368
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.4916 0.6424 0.424 0.000 0.000 0.576
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.4103 0.5147 0.256 0.000 0.744 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.4948 0.6188 0.440 0.000 0.000 0.560
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.2149 0.7304 0.000 0.088 0.912 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.9158 0.000 1.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0469 0.7234 0.988 0.000 0.000 0.012
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.5512 -0.1883 0.492 0.492 0.016 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.0000 0.6659 0.000 0.000 0.000 1.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.0000 0.6659 0.000 0.000 0.000 1.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.5212 0.0391 0.008 0.000 0.420 0.572
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.3837 0.6844 0.000 0.224 0.776 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.0817 0.7230 0.000 0.024 0.976 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.5408 0.3706 0.576 0.408 0.016 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 1 0.5408 0.3706 0.576 0.408 0.016 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0469 0.7234 0.988 0.000 0.000 0.012
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.3837 0.7700 0.000 0.000 0.776 0.224
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.1059 0.9028 0.012 0.972 0.016 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.3837 0.7700 0.000 0.000 0.776 0.224
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.3688 0.7692 0.000 0.000 0.792 0.208
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0469 0.7234 0.988 0.000 0.000 0.012
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.4253 0.7654 0.016 0.000 0.776 0.208
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0469 0.7234 0.988 0.000 0.000 0.012
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 1 0.5408 0.3706 0.576 0.408 0.016 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.0000 0.6659 0.000 0.000 0.000 1.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.3610 0.6931 0.200 0.000 0.000 0.800
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.7697 0.1593 0.220 0.404 0.376 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.4086 0.7685 0.008 0.000 0.776 0.216
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.9158 0.000 1.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 1 0.5408 0.3706 0.576 0.408 0.016 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.4072 0.7581 0.000 0.000 0.748 0.252
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.3649 0.6900 0.000 0.204 0.796 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.4436 0.7588 0.020 0.000 0.764 0.216
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.4916 0.6424 0.424 0.000 0.000 0.576
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.4916 0.6424 0.424 0.000 0.000 0.576
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.4477 0.6805 0.000 0.000 0.688 0.312
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.4898 -0.3194 0.584 0.000 0.000 0.416
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.5427 0.0831 0.416 0.568 0.016 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 1 0.5408 0.3706 0.576 0.408 0.016 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.1059 0.9028 0.012 0.972 0.016 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.1059 0.9028 0.012 0.972 0.016 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0469 0.7234 0.988 0.000 0.000 0.012
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.9158 0.000 1.000 0.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0592 0.7190 0.984 0.000 0.000 0.016
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.3400 0.6923 0.180 0.000 0.000 0.820
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.0000 0.6659 0.000 0.000 0.000 1.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.5026 0.4970 0.672 0.312 0.016 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.0188 0.6613 0.000 0.000 0.004 0.996
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0000 0.6659 0.000 0.000 0.000 1.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0469 0.7234 0.988 0.000 0.000 0.012
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.9158 0.000 1.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 1 0.5408 0.3706 0.576 0.408 0.016 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0000 0.9158 0.000 1.000 0.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0469 0.7234 0.988 0.000 0.000 0.012
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.4916 0.6424 0.424 0.000 0.000 0.576
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 1 0.5408 0.3706 0.576 0.408 0.016 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.9158 0.000 1.000 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.6532 0.3818 0.368 0.000 0.548 0.084
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 4 0.3649 0.5507 0.204 0.000 0.000 0.796
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.9158 0.000 1.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 4 0.0188 0.6655 0.004 0.000 0.000 0.996
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.4955 0.2743 0.000 0.000 0.556 0.444
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.1610 0.7102 0.952 0.032 0.016 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.7001 0.5362 0.244 0.000 0.180 0.576
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0469 0.7234 0.988 0.000 0.000 0.012
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.9158 0.000 1.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.9158 0.000 1.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.4103 0.7557 0.000 0.000 0.744 0.256
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0469 0.7234 0.988 0.000 0.000 0.012
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0469 0.7234 0.988 0.000 0.000 0.012
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.1059 0.9028 0.012 0.972 0.016 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.0000 0.6659 0.000 0.000 0.000 1.000
#> B3561356-5A80-4C79-B23A-D518425565FE 1 0.5408 0.3706 0.576 0.408 0.016 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 4 0.3649 0.5319 0.204 0.000 0.000 0.796
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 4 0.0817 0.6598 0.024 0.000 0.000 0.976
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.4916 0.6424 0.424 0.000 0.000 0.576
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.4332 0.7216 0.000 0.176 0.792 0.032
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.9158 0.000 1.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0469 0.7234 0.988 0.000 0.000 0.012
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.4916 0.6424 0.424 0.000 0.000 0.576
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.4134 0.6901 0.260 0.000 0.000 0.740
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.5408 0.3706 0.576 0.408 0.016 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 4 0.3688 0.4870 0.000 0.000 0.208 0.792
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0469 0.7234 0.988 0.000 0.000 0.012
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.0469 0.7234 0.988 0.000 0.000 0.012
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.4916 0.6424 0.424 0.000 0.000 0.576
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.9158 0.000 1.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.3837 0.7700 0.000 0.000 0.776 0.224
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.4994 -0.1032 0.000 0.480 0.520 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.85143 1.000 0.000 0.000 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.2806 0.69608 0.000 0.844 0.004 0.000 0.152
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.5493 0.45759 0.000 0.000 0.632 0.112 0.256
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.85143 1.000 0.000 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.2127 0.72165 0.000 0.000 0.892 0.108 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.6717 0.46263 0.336 0.408 0.000 0.000 0.256
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.2127 0.68122 0.000 0.108 0.892 0.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.85143 1.000 0.000 0.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0000 0.85143 1.000 0.000 0.000 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.2127 0.72165 0.000 0.000 0.892 0.108 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.70459 0.000 1.000 0.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.6700 0.47634 0.324 0.420 0.000 0.000 0.256
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.85143 1.000 0.000 0.000 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.3366 0.67861 0.000 0.768 0.000 0.000 0.232
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.3999 0.39577 0.000 0.000 0.656 0.000 0.344
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.2127 0.72165 0.000 0.000 0.892 0.108 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.4900 0.18431 0.024 0.000 0.512 0.000 0.464
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.1043 0.66831 0.040 0.000 0.000 0.960 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.70459 0.000 1.000 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.70459 0.000 1.000 0.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.1043 0.66831 0.040 0.000 0.000 0.960 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.70459 0.000 1.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.6567 0.18424 0.024 0.000 0.112 0.396 0.468
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.4182 0.43886 0.000 0.000 0.000 0.600 0.400
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.6717 0.46263 0.336 0.408 0.000 0.000 0.256
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0162 0.66537 0.004 0.000 0.000 0.996 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.1043 0.66755 0.040 0.000 0.000 0.960 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.66463 0.000 0.000 0.000 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.3999 0.39577 0.000 0.000 0.656 0.000 0.344
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.6570 0.39248 0.388 0.408 0.000 0.000 0.204
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.70459 0.000 1.000 0.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.85143 1.000 0.000 0.000 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.4182 0.43886 0.000 0.000 0.000 0.600 0.400
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.3607 0.37938 0.244 0.000 0.004 0.752 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.1043 0.66831 0.040 0.000 0.000 0.960 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.5922 0.21927 0.112 0.000 0.520 0.000 0.368
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.4383 0.01135 0.572 0.000 0.004 0.424 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.1197 0.68752 0.000 0.048 0.952 0.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.70459 0.000 1.000 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.85143 1.000 0.000 0.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.6620 0.51343 0.288 0.456 0.000 0.000 0.256
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.4182 0.43886 0.000 0.000 0.000 0.600 0.400
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.4182 0.43886 0.000 0.000 0.000 0.600 0.400
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.1197 0.64177 0.000 0.000 0.048 0.952 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.2127 0.68122 0.000 0.108 0.892 0.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.4151 0.39231 0.000 0.004 0.652 0.000 0.344
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.6395 -0.36562 0.424 0.408 0.000 0.000 0.168
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.6717 0.46263 0.336 0.408 0.000 0.000 0.256
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.85143 1.000 0.000 0.000 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.2127 0.72165 0.000 0.000 0.892 0.108 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.2732 0.69629 0.000 0.840 0.000 0.000 0.160
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.2127 0.72165 0.000 0.000 0.892 0.108 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.2233 0.72092 0.000 0.000 0.892 0.104 0.004
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.85143 1.000 0.000 0.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.2127 0.72165 0.000 0.000 0.892 0.108 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.3109 0.61927 0.800 0.000 0.000 0.000 0.200
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.6717 0.46263 0.336 0.408 0.000 0.000 0.256
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.4182 0.43886 0.000 0.000 0.000 0.600 0.400
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0000 0.66463 0.000 0.000 0.000 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.6739 -0.30328 0.000 0.372 0.372 0.000 0.256
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.2127 0.72165 0.000 0.000 0.892 0.108 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.70459 0.000 1.000 0.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.6717 0.46263 0.336 0.408 0.000 0.000 0.256
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.4364 0.63630 0.000 0.000 0.768 0.112 0.120
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.2074 0.68247 0.000 0.104 0.896 0.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.3857 0.28273 0.000 0.000 0.312 0.688 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.1043 0.66831 0.040 0.000 0.000 0.960 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.1043 0.66831 0.040 0.000 0.000 0.960 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.6239 0.06466 0.000 0.000 0.452 0.404 0.144
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.3684 0.35961 0.720 0.000 0.000 0.280 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.6596 0.52063 0.280 0.464 0.000 0.000 0.256
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.6717 0.46263 0.336 0.408 0.000 0.000 0.256
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.3210 0.68498 0.000 0.788 0.000 0.000 0.212
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.2732 0.69629 0.000 0.840 0.000 0.000 0.160
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.85143 1.000 0.000 0.000 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.70459 0.000 1.000 0.000 0.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.85143 1.000 0.000 0.000 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0000 0.66463 0.000 0.000 0.000 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.4182 0.43886 0.000 0.000 0.000 0.600 0.400
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.5335 0.40307 0.668 0.200 0.000 0.000 0.132
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.4182 0.43886 0.000 0.000 0.000 0.600 0.400
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.3983 0.48035 0.000 0.000 0.000 0.660 0.340
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.85143 1.000 0.000 0.000 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.70459 0.000 1.000 0.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 1 0.4167 0.58446 0.724 0.024 0.000 0.000 0.252
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.2179 0.69478 0.000 0.888 0.000 0.000 0.112
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.85143 1.000 0.000 0.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.1121 0.66534 0.044 0.000 0.000 0.956 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.6717 0.46263 0.336 0.408 0.000 0.000 0.256
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.70459 0.000 1.000 0.000 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.5591 -0.00705 0.496 0.000 0.432 0.072 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 4 0.6480 -0.00225 0.184 0.000 0.000 0.416 0.400
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.70459 0.000 1.000 0.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 4 0.5944 0.20911 0.108 0.000 0.000 0.488 0.404
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.4224 0.39650 0.000 0.000 0.216 0.040 0.744
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.3783 0.60884 0.740 0.008 0.000 0.000 0.252
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.4359 0.22006 0.412 0.000 0.004 0.584 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.85143 1.000 0.000 0.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.70459 0.000 1.000 0.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.70459 0.000 1.000 0.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.4364 0.63630 0.000 0.000 0.768 0.112 0.120
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0000 0.85143 1.000 0.000 0.000 0.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.85143 1.000 0.000 0.000 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.2773 0.69574 0.000 0.836 0.000 0.000 0.164
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.4182 0.43886 0.000 0.000 0.000 0.600 0.400
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.6717 0.46263 0.336 0.408 0.000 0.000 0.256
#> F900E9BE-2400-4451-9434-EE8BC513BA94 5 0.6749 -0.05398 0.272 0.000 0.000 0.328 0.400
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 4 0.5939 0.21779 0.108 0.000 0.000 0.492 0.400
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.1043 0.66831 0.040 0.000 0.000 0.960 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.2630 0.69312 0.000 0.080 0.892 0.016 0.012
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.70459 0.000 1.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.85143 1.000 0.000 0.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.1043 0.66831 0.040 0.000 0.000 0.960 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0000 0.66463 0.000 0.000 0.000 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.6717 0.46263 0.336 0.408 0.000 0.000 0.256
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.4617 0.46191 0.000 0.000 0.108 0.148 0.744
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.85143 1.000 0.000 0.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.0000 0.85143 1.000 0.000 0.000 0.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.1043 0.66831 0.040 0.000 0.000 0.960 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.70459 0.000 1.000 0.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.2127 0.72165 0.000 0.000 0.892 0.108 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.6746 0.01251 0.000 0.264 0.392 0.000 0.344
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.0 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.3634 -0.1974 0.000 0.644 0.000 0.000 0.0 0.356
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.2912 0.6973 0.000 0.000 0.784 0.216 0.0 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.0 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.8795 0.000 0.000 1.000 0.000 0.0 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.7456 0.000 1.000 0.000 0.000 0.0 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0000 0.8795 0.000 0.000 1.000 0.000 0.0 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.0 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.0 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0000 0.8795 0.000 0.000 1.000 0.000 0.0 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 6 0.3854 0.7566 0.000 0.464 0.000 0.000 0.0 0.536
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.7456 0.000 1.000 0.000 0.000 0.0 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.0 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0790 0.7212 0.000 0.968 0.000 0.000 0.0 0.032
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.0000 0.9516 0.000 0.000 0.000 0.000 1.0 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0000 0.8795 0.000 0.000 1.000 0.000 0.0 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.0000 0.9516 0.000 0.000 0.000 0.000 1.0 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.4516 0.7303 0.036 0.000 0.000 0.564 0.0 0.400
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 6 0.3756 0.8542 0.000 0.400 0.000 0.000 0.0 0.600
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 6 0.3756 0.8542 0.000 0.400 0.000 0.000 0.0 0.600
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.4516 0.7303 0.036 0.000 0.000 0.564 0.0 0.400
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 6 0.3756 0.8542 0.000 0.400 0.000 0.000 0.0 0.600
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.0000 0.9516 0.000 0.000 0.000 0.000 1.0 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.0000 0.6317 0.000 0.000 0.000 1.000 0.0 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.7456 0.000 1.000 0.000 0.000 0.0 0.000
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#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.4576 0.7129 0.000 0.000 0.040 0.560 0.0 0.400
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0000 0.8795 0.000 0.000 1.000 0.000 0.0 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0000 0.9516 0.000 0.000 0.000 0.000 1.0 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.2135 0.6201 0.128 0.872 0.000 0.000 0.0 0.000
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#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0000 0.8795 0.000 0.000 1.000 0.000 0.0 0.000
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#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.0 0.000
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#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.2793 0.7292 0.800 0.000 0.000 0.200 0.0 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.7456 0.000 1.000 0.000 0.000 0.0 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.0458 0.6252 0.016 0.000 0.000 0.984 0.0 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.3756 0.7292 0.000 0.000 0.000 0.600 0.0 0.400
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.3717 0.2495 0.000 0.616 0.384 0.000 0.0 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0000 0.8795 0.000 0.000 1.000 0.000 0.0 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 6 0.3854 0.7566 0.000 0.464 0.000 0.000 0.0 0.536
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.7456 0.000 1.000 0.000 0.000 0.0 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.1714 0.8186 0.000 0.000 0.908 0.092 0.0 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0000 0.8795 0.000 0.000 1.000 0.000 0.0 0.000
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#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.4516 0.7303 0.036 0.000 0.000 0.564 0.0 0.400
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.4516 0.7303 0.036 0.000 0.000 0.564 0.0 0.400
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.3847 0.2184 0.000 0.000 0.544 0.456 0.0 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.0 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.7456 0.000 1.000 0.000 0.000 0.0 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.7456 0.000 1.000 0.000 0.000 0.0 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.1501 0.6709 0.000 0.924 0.000 0.000 0.0 0.076
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.2730 0.4623 0.000 0.808 0.000 0.000 0.0 0.192
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.0 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 6 0.3756 0.8542 0.000 0.400 0.000 0.000 0.0 0.600
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.0 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.3756 0.7292 0.000 0.000 0.000 0.600 0.0 0.400
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.0458 0.6252 0.016 0.000 0.000 0.984 0.0 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.3747 0.3419 0.396 0.604 0.000 0.000 0.0 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.0458 0.6252 0.016 0.000 0.000 0.984 0.0 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0458 0.6374 0.000 0.000 0.000 0.984 0.0 0.016
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.0 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 6 0.3854 0.7566 0.000 0.464 0.000 0.000 0.0 0.536
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.3717 0.2838 0.384 0.616 0.000 0.000 0.0 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.3175 0.2191 0.000 0.744 0.000 0.000 0.0 0.256
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.0 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.4516 0.7303 0.036 0.000 0.000 0.564 0.0 0.400
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.7456 0.000 1.000 0.000 0.000 0.0 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 6 0.3756 0.8542 0.000 0.400 0.000 0.000 0.0 0.600
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.3747 0.3318 0.396 0.000 0.604 0.000 0.0 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 4 0.3727 0.0987 0.388 0.000 0.000 0.612 0.0 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 6 0.3756 0.8542 0.000 0.400 0.000 0.000 0.0 0.600
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 4 0.3601 0.2867 0.312 0.004 0.000 0.684 0.0 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.0000 0.9516 0.000 0.000 0.000 0.000 1.0 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.3804 0.1785 0.424 0.576 0.000 0.000 0.0 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.5234 0.2599 0.596 0.000 0.000 0.260 0.0 0.144
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.0 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 6 0.3756 0.8542 0.000 0.400 0.000 0.000 0.0 0.600
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 6 0.3756 0.8542 0.000 0.400 0.000 0.000 0.0 0.600
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.1714 0.8186 0.000 0.000 0.908 0.092 0.0 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0458 0.9475 0.984 0.016 0.000 0.000 0.0 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.0 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.2941 0.3976 0.000 0.780 0.000 0.000 0.0 0.220
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.0260 0.6289 0.008 0.000 0.000 0.992 0.0 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.7456 0.000 1.000 0.000 0.000 0.0 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 4 0.3868 -0.2237 0.496 0.000 0.000 0.504 0.0 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 4 0.3464 0.2910 0.312 0.000 0.000 0.688 0.0 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.4516 0.7303 0.036 0.000 0.000 0.564 0.0 0.400
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0000 0.8795 0.000 0.000 1.000 0.000 0.0 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 6 0.3756 0.8542 0.000 0.400 0.000 0.000 0.0 0.600
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.0 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.4516 0.7303 0.036 0.000 0.000 0.564 0.0 0.400
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.3756 0.7292 0.000 0.000 0.000 0.600 0.0 0.400
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0000 0.7456 0.000 1.000 0.000 0.000 0.0 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.3756 0.5276 0.000 0.000 0.000 0.400 0.6 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.9619 1.000 0.000 0.000 0.000 0.0 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.0458 0.9475 0.984 0.016 0.000 0.000 0.0 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.4516 0.7303 0.036 0.000 0.000 0.564 0.0 0.400
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 6 0.3756 0.8542 0.000 0.400 0.000 0.000 0.0 0.600
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.0000 0.8795 0.000 0.000 1.000 0.000 0.0 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0000 0.9516 0.000 0.000 0.000 0.000 1.0 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["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 17548 rows and 122 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 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 0.983 0.979 0.4911 0.497 0.497
#> 3 3 0.797 0.816 0.889 0.2840 0.863 0.725
#> 4 4 0.840 0.908 0.936 0.1737 0.837 0.579
#> 5 5 0.777 0.802 0.892 0.0379 0.986 0.944
#> 6 6 0.802 0.730 0.840 0.0557 0.945 0.781
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 2 0.2778 0.985 0.048 0.952
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.2778 0.985 0.048 0.952
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.994 1.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 2 0.2778 0.985 0.048 0.952
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 1 0.0000 0.994 1.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.2778 0.985 0.048 0.952
#> 9264567D-4524-46AF-A851-C091C3CD76CF 1 0.0000 0.994 1.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 2 0.2778 0.985 0.048 0.952
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 2 0.2778 0.985 0.048 0.952
#> 806616FE-1855-4284-9265-42842104CB21 1 0.0000 0.994 1.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.963 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.2778 0.985 0.048 0.952
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 2 0.2778 0.985 0.048 0.952
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.2778 0.985 0.048 0.952
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 1 0.2236 0.966 0.964 0.036
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 1 0.0000 0.994 1.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 1 0.2236 0.966 0.964 0.036
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.994 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.963 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.963 0.000 1.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.994 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.963 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 1 0.2236 0.966 0.964 0.036
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.994 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.2778 0.985 0.048 0.952
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.994 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.994 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.994 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.2236 0.966 0.964 0.036
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.2778 0.985 0.048 0.952
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.963 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 2 0.2778 0.985 0.048 0.952
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.994 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.0000 0.994 1.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.994 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 1 0.2236 0.966 0.964 0.036
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0000 0.994 1.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.0000 0.994 1.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.963 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 2 0.2778 0.985 0.048 0.952
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.2778 0.985 0.048 0.952
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.994 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.2236 0.966 0.964 0.036
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.994 1.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 1 0.0000 0.994 1.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 1 0.0000 0.994 1.000 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.2778 0.985 0.048 0.952
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.2778 0.985 0.048 0.952
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 2 0.2778 0.985 0.048 0.952
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.0000 0.994 1.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.963 0.000 1.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.0000 0.994 1.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.0000 0.994 1.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 2 0.2778 0.985 0.048 0.952
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.0000 0.994 1.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 2 0.2778 0.985 0.048 0.952
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.2778 0.985 0.048 0.952
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.994 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.994 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 1 0.0938 0.985 0.988 0.012
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.0000 0.994 1.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.963 0.000 1.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.2778 0.985 0.048 0.952
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.0000 0.994 1.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 1 0.0000 0.994 1.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.0000 0.994 1.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.994 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.994 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0000 0.994 1.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 2 0.2778 0.985 0.048 0.952
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.2778 0.985 0.048 0.952
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.2778 0.985 0.048 0.952
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.963 0.000 1.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.963 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 2 0.2778 0.985 0.048 0.952
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.4022 0.949 0.080 0.920
#> A314C4E6-B245-4F10-A555-50B9B819040D 2 0.2778 0.985 0.048 0.952
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.994 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.994 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.2778 0.985 0.048 0.952
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.994 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.994 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 2 0.2778 0.985 0.048 0.952
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.963 0.000 1.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.2778 0.985 0.048 0.952
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.2778 0.985 0.048 0.952
#> 6F7DB73C-FE46-402C-9001-DC2005278069 2 0.2778 0.985 0.048 0.952
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.994 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.2778 0.985 0.048 0.952
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.963 0.000 1.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.0000 0.994 1.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 2 0.2778 0.985 0.048 0.952
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.963 0.000 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 2 0.3114 0.978 0.056 0.944
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.2236 0.966 0.964 0.036
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.2778 0.985 0.048 0.952
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.994 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 2 0.2778 0.985 0.048 0.952
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.963 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.963 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0000 0.994 1.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 2 0.2778 0.985 0.048 0.952
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 2 0.2778 0.985 0.048 0.952
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.963 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.994 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.2778 0.985 0.048 0.952
#> F900E9BE-2400-4451-9434-EE8BC513BA94 2 0.2778 0.985 0.048 0.952
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 2 0.3114 0.978 0.056 0.944
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.994 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 1 0.0000 0.994 1.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.963 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 2 0.2778 0.985 0.048 0.952
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.994 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.994 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.2778 0.985 0.048 0.952
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.2236 0.966 0.964 0.036
#> 12F54761-4F68-4181-8421-88EA858902FC 2 0.2778 0.985 0.048 0.952
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.2778 0.985 0.048 0.952
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.994 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.963 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.0000 0.994 1.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 1 0.2236 0.966 0.964 0.036
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.2796 0.927 0.908 0.092 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.6280 0.313 0.460 0.540 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.0000 0.963 0.000 0.000 1.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.2796 0.927 0.908 0.092 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.963 0.000 0.000 1.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.6244 0.407 0.440 0.560 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0237 0.961 0.000 0.004 0.996
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.2796 0.927 0.908 0.092 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.2959 0.917 0.900 0.100 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0000 0.963 0.000 0.000 1.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.1163 0.743 0.028 0.972 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.6244 0.407 0.440 0.560 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.2796 0.927 0.908 0.092 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.1643 0.735 0.044 0.956 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.0000 0.963 0.000 0.000 1.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0000 0.963 0.000 0.000 1.000
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.0000 0.963 0.000 0.000 1.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 3 0.3030 0.945 0.092 0.004 0.904
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.740 0.000 1.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.740 0.000 1.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 3 0.3030 0.945 0.092 0.004 0.904
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.740 0.000 1.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.0000 0.963 0.000 0.000 1.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 3 0.0000 0.963 0.000 0.000 1.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.6244 0.407 0.440 0.560 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 3 0.3030 0.945 0.092 0.004 0.904
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 3 0.3030 0.945 0.092 0.004 0.904
#> 4496EE84-2C36-413B-A328-A5B598A6C387 3 0.2796 0.946 0.092 0.000 0.908
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.0000 0.963 0.000 0.000 1.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.4750 0.643 0.216 0.784 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.740 0.000 1.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.2796 0.927 0.908 0.092 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 3 0.0000 0.963 0.000 0.000 1.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.3272 0.939 0.104 0.004 0.892
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 3 0.3030 0.945 0.092 0.004 0.904
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.0000 0.963 0.000 0.000 1.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 3 0.0829 0.958 0.012 0.004 0.984
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0000 0.963 0.000 0.000 1.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.740 0.000 1.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.2796 0.927 0.908 0.092 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.6215 0.430 0.428 0.572 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 3 0.0000 0.963 0.000 0.000 1.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.0000 0.963 0.000 0.000 1.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.2796 0.946 0.092 0.000 0.908
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.1411 0.944 0.000 0.036 0.964
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.0000 0.963 0.000 0.000 1.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.6008 0.521 0.372 0.628 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.3619 0.708 0.136 0.864 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.2796 0.927 0.908 0.092 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.0237 0.963 0.004 0.000 0.996
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.3816 0.706 0.148 0.852 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0000 0.963 0.000 0.000 1.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.1643 0.941 0.000 0.044 0.956
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.2796 0.927 0.908 0.092 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.2796 0.946 0.092 0.000 0.908
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.2796 0.927 0.908 0.092 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.6244 0.407 0.440 0.560 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 3 0.0000 0.963 0.000 0.000 1.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 3 0.2796 0.946 0.092 0.000 0.908
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.4062 0.819 0.000 0.164 0.836
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0237 0.963 0.004 0.000 0.996
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.740 0.000 1.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.6244 0.407 0.440 0.560 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0000 0.963 0.000 0.000 1.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.1163 0.949 0.000 0.028 0.972
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.3030 0.945 0.092 0.004 0.904
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 3 0.3272 0.939 0.104 0.004 0.892
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 3 0.3030 0.945 0.092 0.004 0.904
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.0000 0.963 0.000 0.000 1.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.2796 0.927 0.908 0.092 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.6225 0.422 0.432 0.568 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.6244 0.407 0.440 0.560 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.2356 0.736 0.072 0.928 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.5178 0.632 0.256 0.744 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.2796 0.927 0.908 0.092 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.1289 0.734 0.032 0.968 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.2796 0.927 0.908 0.092 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 3 0.2796 0.946 0.092 0.000 0.908
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 3 0.0000 0.963 0.000 0.000 1.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.6307 -0.205 0.512 0.488 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 3 0.0000 0.963 0.000 0.000 1.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 3 0.2796 0.946 0.092 0.000 0.908
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.2796 0.927 0.908 0.092 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0592 0.742 0.012 0.988 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.6244 0.407 0.440 0.560 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.6079 0.480 0.388 0.612 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.2796 0.927 0.908 0.092 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 3 0.3030 0.945 0.092 0.004 0.904
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.6244 0.407 0.440 0.560 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0747 0.742 0.016 0.984 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.4636 0.916 0.104 0.044 0.852
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.2796 0.927 0.908 0.092 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.740 0.000 1.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.3532 0.898 0.884 0.108 0.008
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.0000 0.963 0.000 0.000 1.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.6307 -0.205 0.512 0.488 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 3 0.3193 0.941 0.100 0.004 0.896
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.2796 0.927 0.908 0.092 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.740 0.000 1.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0592 0.742 0.012 0.988 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.0000 0.963 0.000 0.000 1.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.2796 0.927 0.908 0.092 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.2796 0.927 0.908 0.092 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.2711 0.730 0.088 0.912 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 3 0.0000 0.963 0.000 0.000 1.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.6111 0.486 0.396 0.604 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.2796 0.927 0.908 0.092 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.2796 0.927 0.908 0.092 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 3 0.3030 0.945 0.092 0.004 0.904
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.1289 0.947 0.000 0.032 0.968
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.740 0.000 1.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.2796 0.927 0.908 0.092 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 3 0.3193 0.941 0.100 0.004 0.896
#> F205F9FC-F2D5-4164-9A40-1279647F900B 3 0.2796 0.946 0.092 0.000 0.908
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.6309 -0.236 0.504 0.496 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.0000 0.963 0.000 0.000 1.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.2796 0.927 0.908 0.092 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.2796 0.927 0.908 0.092 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 3 0.3272 0.939 0.104 0.004 0.892
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.740 0.000 1.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.0237 0.963 0.004 0.000 0.996
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.0000 0.963 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0188 0.9708 0.996 0.000 0.004 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.1452 0.8842 0.008 0.956 0.036 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.3975 0.8217 0.000 0.000 0.760 0.240
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.9719 1.000 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.4790 0.6115 0.000 0.000 0.620 0.380
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.3791 0.8173 0.200 0.796 0.004 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.2593 0.8899 0.000 0.004 0.892 0.104
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.9719 1.000 0.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0469 0.9653 0.988 0.012 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.4898 0.5332 0.000 0.000 0.584 0.416
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0469 0.8919 0.000 0.988 0.012 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.3791 0.8173 0.200 0.796 0.004 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0188 0.9708 0.996 0.000 0.004 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0927 0.8874 0.016 0.976 0.008 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.1211 0.8882 0.000 0.000 0.960 0.040
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.3975 0.8238 0.000 0.000 0.760 0.240
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.1211 0.8882 0.000 0.000 0.960 0.040
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.9939 0.000 0.000 0.000 1.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0469 0.8919 0.000 0.988 0.012 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0469 0.8919 0.000 0.988 0.012 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.9939 0.000 0.000 0.000 1.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0469 0.8919 0.000 0.988 0.012 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.1389 0.8885 0.000 0.000 0.952 0.048
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.0336 0.9915 0.000 0.000 0.008 0.992
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.3791 0.8173 0.200 0.796 0.004 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.9939 0.000 0.000 0.000 1.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.9939 0.000 0.000 0.000 1.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0188 0.9926 0.000 0.000 0.004 0.996
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.1211 0.8882 0.000 0.000 0.960 0.040
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.3893 0.7679 0.796 0.196 0.008 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0469 0.8919 0.000 0.988 0.012 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0188 0.9708 0.996 0.000 0.004 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.0336 0.9915 0.000 0.000 0.008 0.992
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.0000 0.9939 0.000 0.000 0.000 1.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.9939 0.000 0.000 0.000 1.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.1302 0.8884 0.000 0.000 0.956 0.044
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.0657 0.9814 0.004 0.000 0.012 0.984
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.2466 0.8904 0.000 0.004 0.900 0.096
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0469 0.8919 0.000 0.988 0.012 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.9719 1.000 0.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.3448 0.8350 0.168 0.828 0.004 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.0336 0.9915 0.000 0.000 0.008 0.992
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.1022 0.8825 0.000 0.000 0.968 0.032
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.0000 0.9939 0.000 0.000 0.000 1.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.2593 0.8899 0.000 0.004 0.892 0.104
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.1389 0.8897 0.000 0.000 0.952 0.048
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.5292 0.0491 0.480 0.512 0.008 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 1 0.3893 0.7679 0.796 0.196 0.008 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.9719 1.000 0.000 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.0000 0.9939 0.000 0.000 0.000 1.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.8909 0.000 1.000 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.0000 0.9939 0.000 0.000 0.000 1.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.4262 0.8277 0.000 0.008 0.756 0.236
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.9719 1.000 0.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.0000 0.9939 0.000 0.000 0.000 1.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.9719 1.000 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.3791 0.8173 0.200 0.796 0.004 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.0336 0.9915 0.000 0.000 0.008 0.992
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0336 0.9915 0.000 0.000 0.008 0.992
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.4964 0.8420 0.000 0.068 0.764 0.168
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.0188 0.9926 0.000 0.000 0.004 0.996
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0469 0.8919 0.000 0.988 0.012 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.3791 0.8173 0.200 0.796 0.004 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.3975 0.8217 0.000 0.000 0.760 0.240
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.2654 0.8894 0.000 0.004 0.888 0.108
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.0000 0.9939 0.000 0.000 0.000 1.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0000 0.9939 0.000 0.000 0.000 1.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.9939 0.000 0.000 0.000 1.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.0336 0.9915 0.000 0.000 0.008 0.992
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0188 0.9704 0.996 0.000 0.004 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.3583 0.8295 0.180 0.816 0.004 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.3791 0.8173 0.200 0.796 0.004 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0469 0.8893 0.000 0.988 0.012 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0804 0.8922 0.008 0.980 0.012 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.9719 1.000 0.000 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.4137 0.7043 0.208 0.780 0.012 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.9719 1.000 0.000 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0188 0.9925 0.000 0.000 0.004 0.996
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.0336 0.9915 0.000 0.000 0.008 0.992
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.4136 0.8165 0.196 0.788 0.016 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.0336 0.9915 0.000 0.000 0.008 0.992
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0336 0.9915 0.000 0.000 0.008 0.992
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0188 0.9708 0.996 0.000 0.004 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.8909 0.000 1.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 1 0.1489 0.9353 0.952 0.044 0.004 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.1798 0.8832 0.040 0.944 0.016 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.9719 1.000 0.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.9939 0.000 0.000 0.000 1.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.4509 0.7078 0.288 0.708 0.004 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0188 0.8914 0.000 0.996 0.004 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.0336 0.9879 0.000 0.000 0.008 0.992
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0707 0.9615 0.980 0.000 0.020 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0469 0.8919 0.000 0.988 0.012 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.1716 0.9319 0.936 0.000 0.064 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.1211 0.8882 0.000 0.000 0.960 0.040
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.4012 0.8246 0.184 0.800 0.016 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0000 0.9939 0.000 0.000 0.000 1.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.9719 1.000 0.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0469 0.8919 0.000 0.988 0.012 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0469 0.8919 0.000 0.988 0.012 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.3942 0.8276 0.000 0.000 0.764 0.236
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0188 0.9698 0.996 0.004 0.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.9719 1.000 0.000 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0469 0.8919 0.000 0.988 0.012 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.0336 0.9915 0.000 0.000 0.008 0.992
#> B3561356-5A80-4C79-B23A-D518425565FE 1 0.3032 0.8590 0.868 0.124 0.008 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0707 0.9615 0.980 0.000 0.020 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.1211 0.9479 0.960 0.000 0.040 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.9939 0.000 0.000 0.000 1.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.3688 0.8480 0.000 0.000 0.792 0.208
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0469 0.8919 0.000 0.988 0.012 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.9719 1.000 0.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0000 0.9939 0.000 0.000 0.000 1.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0000 0.9939 0.000 0.000 0.000 1.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.4175 0.8133 0.200 0.784 0.016 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.1022 0.8825 0.000 0.000 0.968 0.032
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.9719 1.000 0.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.0000 0.9719 1.000 0.000 0.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.9939 0.000 0.000 0.000 1.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0469 0.8919 0.000 0.988 0.012 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.1716 0.9165 0.000 0.000 0.064 0.936
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.1389 0.8885 0.000 0.000 0.952 0.048
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.1965 0.876 0.904 0.000 0.000 0.000 0.096
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.3692 0.777 0.052 0.812 0.000 0.000 0.136
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.1012 0.763 0.000 0.000 0.968 0.012 0.020
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.908 1.000 0.000 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.2074 0.573 0.000 0.000 0.896 0.104 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.3912 0.750 0.228 0.752 0.000 0.000 0.020
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0000 0.788 0.000 0.000 1.000 0.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.908 1.000 0.000 0.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0000 0.908 1.000 0.000 0.000 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.2891 0.347 0.000 0.000 0.824 0.176 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0510 0.837 0.000 0.984 0.000 0.000 0.016
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.3912 0.750 0.228 0.752 0.000 0.000 0.020
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.1965 0.876 0.904 0.000 0.000 0.000 0.096
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.2964 0.770 0.120 0.856 0.000 0.000 0.024
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.3109 0.648 0.000 0.000 0.800 0.000 0.200
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0162 0.786 0.000 0.000 0.996 0.004 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.3109 0.648 0.000 0.000 0.800 0.000 0.200
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0794 0.837 0.000 0.972 0.000 0.000 0.028
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0794 0.837 0.000 0.972 0.000 0.000 0.028
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0794 0.837 0.000 0.972 0.000 0.000 0.028
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.3109 0.648 0.000 0.000 0.800 0.000 0.200
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.1704 0.876 0.000 0.000 0.004 0.928 0.068
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.3942 0.747 0.232 0.748 0.000 0.000 0.020
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.3109 0.648 0.000 0.000 0.800 0.000 0.200
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.3368 0.797 0.820 0.156 0.000 0.000 0.024
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0609 0.838 0.000 0.980 0.000 0.000 0.020
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.1965 0.876 0.904 0.000 0.000 0.000 0.096
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.0865 0.898 0.000 0.000 0.004 0.972 0.024
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.0162 0.905 0.000 0.000 0.004 0.996 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.3109 0.648 0.000 0.000 0.800 0.000 0.200
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.1892 0.858 0.000 0.000 0.080 0.916 0.004
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0290 0.788 0.000 0.000 0.992 0.000 0.008
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0794 0.837 0.000 0.972 0.000 0.000 0.028
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.908 1.000 0.000 0.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.3912 0.750 0.228 0.752 0.000 0.000 0.020
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.2439 0.840 0.000 0.000 0.004 0.876 0.120
#> CB925BF0-1249-4350-A175-9A4129C43B8D 5 0.6121 0.921 0.000 0.000 0.408 0.128 0.464
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.0404 0.903 0.000 0.000 0.012 0.988 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0162 0.786 0.000 0.000 0.996 0.004 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.2891 0.669 0.000 0.000 0.824 0.000 0.176
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.4402 0.386 0.636 0.352 0.000 0.000 0.012
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 1 0.3264 0.793 0.820 0.164 0.000 0.000 0.016
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.908 1.000 0.000 0.000 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.3684 0.600 0.000 0.000 0.280 0.720 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.2230 0.802 0.000 0.884 0.000 0.000 0.116
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.3837 0.545 0.000 0.000 0.308 0.692 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0162 0.788 0.000 0.000 0.996 0.004 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.908 1.000 0.000 0.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.3586 0.625 0.000 0.000 0.264 0.736 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.1041 0.895 0.964 0.004 0.000 0.000 0.032
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.3942 0.747 0.232 0.748 0.000 0.000 0.020
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.2970 0.797 0.000 0.000 0.004 0.828 0.168
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.0963 0.742 0.000 0.036 0.964 0.000 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.3707 0.593 0.000 0.000 0.284 0.716 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0609 0.838 0.000 0.980 0.000 0.000 0.020
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.3988 0.726 0.252 0.732 0.000 0.000 0.016
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0404 0.780 0.000 0.000 0.988 0.012 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0000 0.788 0.000 0.000 1.000 0.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.0290 0.904 0.000 0.000 0.008 0.992 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.1608 0.867 0.000 0.000 0.072 0.928 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.908 1.000 0.000 0.000 0.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.3912 0.750 0.228 0.752 0.000 0.000 0.020
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.3912 0.750 0.228 0.752 0.000 0.000 0.020
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.2280 0.800 0.000 0.880 0.000 0.000 0.120
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.1399 0.837 0.028 0.952 0.000 0.000 0.020
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.2074 0.874 0.896 0.000 0.000 0.000 0.104
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.3437 0.716 0.176 0.808 0.004 0.000 0.012
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.908 1.000 0.000 0.000 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.3123 0.781 0.000 0.000 0.004 0.812 0.184
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.5515 0.679 0.260 0.628 0.000 0.000 0.112
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.3010 0.793 0.000 0.000 0.004 0.824 0.172
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.2020 0.875 0.900 0.000 0.000 0.000 0.100
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0404 0.837 0.000 0.988 0.000 0.000 0.012
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 1 0.2825 0.819 0.860 0.124 0.000 0.000 0.016
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.3569 0.805 0.068 0.828 0.000 0.000 0.104
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.908 1.000 0.000 0.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.4366 0.620 0.320 0.664 0.000 0.000 0.016
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.2127 0.802 0.000 0.892 0.000 0.000 0.108
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.2011 0.851 0.000 0.000 0.088 0.908 0.004
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.3274 0.780 0.780 0.000 0.000 0.000 0.220
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0794 0.837 0.000 0.972 0.000 0.000 0.028
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.3741 0.740 0.732 0.004 0.000 0.000 0.264
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.3242 0.620 0.000 0.000 0.784 0.000 0.216
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.5510 0.713 0.208 0.648 0.000 0.000 0.144
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0162 0.905 0.000 0.000 0.004 0.996 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0290 0.906 0.992 0.000 0.000 0.000 0.008
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0703 0.837 0.000 0.976 0.000 0.000 0.024
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0609 0.838 0.000 0.980 0.000 0.000 0.020
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.0000 0.788 0.000 0.000 1.000 0.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0000 0.908 1.000 0.000 0.000 0.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.908 1.000 0.000 0.000 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0703 0.838 0.000 0.976 0.000 0.000 0.024
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.2179 0.855 0.000 0.000 0.004 0.896 0.100
#> B3561356-5A80-4C79-B23A-D518425565FE 1 0.2920 0.816 0.852 0.132 0.000 0.000 0.016
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.3395 0.767 0.764 0.000 0.000 0.000 0.236
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.3550 0.765 0.760 0.004 0.000 0.000 0.236
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0000 0.788 0.000 0.000 1.000 0.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0794 0.837 0.000 0.972 0.000 0.000 0.028
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.908 1.000 0.000 0.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.5373 0.708 0.236 0.652 0.000 0.000 0.112
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.5929 0.916 0.000 0.000 0.432 0.104 0.464
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.908 1.000 0.000 0.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.2074 0.874 0.896 0.000 0.000 0.000 0.104
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.907 0.000 0.000 0.000 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0609 0.838 0.000 0.980 0.000 0.000 0.020
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.4249 0.236 0.000 0.000 0.432 0.568 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.3109 0.648 0.000 0.000 0.800 0.000 0.200
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0146 0.834 0.996 0.000 0.000 0.000 0.000 0.004
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.6823 0.242 0.044 0.456 0.020 0.000 0.148 0.332
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.3737 0.645 0.000 0.000 0.780 0.008 0.168 0.044
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.2092 0.730 0.876 0.000 0.000 0.000 0.000 0.124
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.1745 0.774 0.000 0.000 0.920 0.068 0.000 0.012
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 5 0.2697 0.787 0.000 0.188 0.000 0.000 0.812 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0146 0.823 0.000 0.000 0.996 0.004 0.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.835 1.000 0.000 0.000 0.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.1578 0.783 0.936 0.004 0.000 0.000 0.012 0.048
#> 806616FE-1855-4284-9265-42842104CB21 3 0.1913 0.762 0.000 0.000 0.908 0.080 0.000 0.012
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.1219 0.873 0.000 0.948 0.000 0.000 0.004 0.048
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 5 0.2697 0.787 0.000 0.188 0.000 0.000 0.812 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0146 0.834 0.996 0.000 0.000 0.000 0.000 0.004
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.6480 0.428 0.160 0.592 0.020 0.000 0.072 0.156
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.2730 0.780 0.000 0.000 0.808 0.000 0.000 0.192
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.1075 0.796 0.000 0.000 0.952 0.048 0.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.2793 0.779 0.000 0.000 0.800 0.000 0.000 0.200
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0146 0.865 0.000 0.000 0.004 0.996 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.879 0.000 1.000 0.000 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.879 0.000 1.000 0.000 0.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.865 0.000 0.000 0.000 1.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.879 0.000 1.000 0.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.2902 0.779 0.000 0.000 0.800 0.004 0.000 0.196
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.4265 0.727 0.000 0.000 0.000 0.728 0.172 0.100
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 5 0.2697 0.787 0.000 0.188 0.000 0.000 0.812 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.865 0.000 0.000 0.000 1.000 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.865 0.000 0.000 0.000 1.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0363 0.864 0.000 0.000 0.000 0.988 0.000 0.012
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.2793 0.779 0.000 0.000 0.800 0.000 0.000 0.200
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.5962 0.395 0.608 0.020 0.020 0.000 0.156 0.196
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0790 0.876 0.000 0.968 0.000 0.000 0.032 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0146 0.834 0.996 0.000 0.000 0.000 0.000 0.004
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.3671 0.767 0.000 0.000 0.008 0.784 0.168 0.040
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.0777 0.861 0.000 0.000 0.004 0.972 0.000 0.024
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.865 0.000 0.000 0.000 1.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.2762 0.779 0.000 0.000 0.804 0.000 0.000 0.196
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.2478 0.826 0.012 0.000 0.024 0.888 0.000 0.076
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0146 0.824 0.000 0.000 0.996 0.000 0.000 0.004
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.879 0.000 1.000 0.000 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.835 1.000 0.000 0.000 0.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 5 0.3288 0.694 0.000 0.276 0.000 0.000 0.724 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.4265 0.727 0.000 0.000 0.000 0.728 0.172 0.100
#> CB925BF0-1249-4350-A175-9A4129C43B8D 6 0.7033 -0.298 0.000 0.000 0.292 0.104 0.172 0.432
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.1682 0.840 0.000 0.000 0.052 0.928 0.000 0.020
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0146 0.823 0.000 0.000 0.996 0.004 0.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.2278 0.806 0.000 0.000 0.868 0.004 0.000 0.128
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 5 0.5739 0.499 0.228 0.012 0.000 0.000 0.568 0.192
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 1 0.6483 0.282 0.540 0.028 0.020 0.000 0.216 0.196
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.835 1.000 0.000 0.000 0.000 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.3665 0.673 0.000 0.000 0.252 0.728 0.000 0.020
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.1780 0.867 0.000 0.924 0.000 0.000 0.028 0.048
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.4356 0.508 0.000 0.000 0.360 0.608 0.000 0.032
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0547 0.824 0.000 0.000 0.980 0.000 0.000 0.020
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.835 1.000 0.000 0.000 0.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.3284 0.722 0.000 0.000 0.196 0.784 0.000 0.020
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.3383 0.604 0.728 0.004 0.000 0.000 0.000 0.268
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 5 0.2697 0.787 0.000 0.188 0.000 0.000 0.812 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.4265 0.727 0.000 0.000 0.000 0.728 0.172 0.100
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0363 0.864 0.000 0.000 0.000 0.988 0.000 0.012
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.1370 0.811 0.000 0.036 0.948 0.004 0.000 0.012
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.3713 0.699 0.000 0.000 0.224 0.744 0.000 0.032
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.1219 0.868 0.000 0.948 0.000 0.000 0.048 0.004
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 5 0.2979 0.787 0.004 0.188 0.000 0.000 0.804 0.004
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.1750 0.804 0.000 0.000 0.932 0.016 0.040 0.012
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0146 0.824 0.000 0.000 0.996 0.000 0.000 0.004
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.1745 0.838 0.000 0.000 0.056 0.924 0.000 0.020
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0146 0.864 0.000 0.000 0.000 0.996 0.000 0.004
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.865 0.000 0.000 0.000 1.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.4066 0.793 0.000 0.000 0.064 0.788 0.112 0.036
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.2854 0.671 0.792 0.000 0.000 0.000 0.000 0.208
#> F779417A-9E29-4B27-BEA3-B23273A66021 5 0.3076 0.740 0.000 0.240 0.000 0.000 0.760 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 5 0.2697 0.787 0.000 0.188 0.000 0.000 0.812 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.1856 0.866 0.000 0.920 0.000 0.000 0.032 0.048
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.1528 0.869 0.000 0.936 0.000 0.000 0.016 0.048
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0146 0.834 0.996 0.000 0.000 0.000 0.000 0.004
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.6461 0.429 0.160 0.600 0.024 0.000 0.072 0.144
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.835 1.000 0.000 0.000 0.000 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0146 0.864 0.000 0.000 0.000 0.996 0.000 0.004
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.4265 0.727 0.000 0.000 0.000 0.728 0.172 0.100
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 5 0.6212 0.579 0.160 0.064 0.000 0.000 0.572 0.204
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.4265 0.727 0.000 0.000 0.000 0.728 0.172 0.100
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.1480 0.852 0.000 0.000 0.000 0.940 0.040 0.020
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0146 0.834 0.996 0.000 0.000 0.000 0.000 0.004
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.1616 0.870 0.000 0.932 0.000 0.000 0.048 0.020
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 5 0.6254 0.489 0.256 0.056 0.000 0.000 0.544 0.144
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 5 0.6831 0.415 0.052 0.336 0.004 0.000 0.416 0.192
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.835 1.000 0.000 0.000 0.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.865 0.000 0.000 0.000 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 5 0.3354 0.786 0.008 0.184 0.000 0.000 0.792 0.016
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.1528 0.868 0.000 0.936 0.000 0.000 0.016 0.048
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.3698 0.770 0.000 0.000 0.116 0.788 0.000 0.096
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.3979 0.455 0.628 0.000 0.012 0.000 0.000 0.360
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.879 0.000 1.000 0.000 0.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 6 0.4241 -0.286 0.348 0.000 0.020 0.000 0.004 0.628
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.5253 0.549 0.000 0.000 0.604 0.000 0.168 0.228
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 5 0.5641 0.523 0.088 0.024 0.004 0.000 0.568 0.316
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0777 0.861 0.000 0.000 0.004 0.972 0.000 0.024
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0363 0.830 0.988 0.000 0.000 0.000 0.000 0.012
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0790 0.876 0.000 0.968 0.000 0.000 0.032 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.2001 0.864 0.000 0.912 0.000 0.000 0.040 0.048
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.3453 0.671 0.000 0.000 0.792 0.000 0.164 0.044
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0363 0.829 0.988 0.000 0.000 0.000 0.012 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.835 1.000 0.000 0.000 0.000 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.1856 0.868 0.000 0.920 0.000 0.000 0.032 0.048
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.4265 0.727 0.000 0.000 0.000 0.728 0.172 0.100
#> B3561356-5A80-4C79-B23A-D518425565FE 1 0.5933 0.286 0.540 0.016 0.000 0.000 0.252 0.192
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.4091 0.215 0.520 0.000 0.008 0.000 0.000 0.472
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.4338 0.134 0.492 0.000 0.020 0.000 0.000 0.488
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0146 0.865 0.000 0.000 0.004 0.996 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0146 0.823 0.000 0.000 0.996 0.004 0.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.879 0.000 1.000 0.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.835 1.000 0.000 0.000 0.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.1327 0.841 0.000 0.000 0.000 0.936 0.000 0.064
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0000 0.865 0.000 0.000 0.000 1.000 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 5 0.6238 0.663 0.080 0.148 0.000 0.000 0.580 0.192
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.5765 0.155 0.000 0.000 0.420 0.000 0.172 0.408
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.1007 0.807 0.956 0.000 0.000 0.000 0.000 0.044
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.0146 0.834 0.996 0.000 0.000 0.000 0.000 0.004
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0260 0.864 0.000 0.000 0.000 0.992 0.000 0.008
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0790 0.876 0.000 0.968 0.000 0.000 0.032 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.4385 0.320 0.000 0.000 0.444 0.532 0.000 0.024
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.2697 0.780 0.000 0.000 0.812 0.000 0.000 0.188
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 17548 rows and 122 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 1.000 0.976 0.990 0.5009 0.499 0.499
#> 3 3 0.682 0.751 0.878 0.3241 0.765 0.562
#> 4 4 0.824 0.855 0.913 0.1072 0.849 0.592
#> 5 5 0.854 0.824 0.920 0.0545 0.919 0.709
#> 6 6 0.754 0.558 0.779 0.0454 0.956 0.816
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.991 1.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.0000 0.987 0.000 1.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.991 1.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.991 1.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 2 0.8955 0.548 0.312 0.688
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.987 0.000 1.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 2 0.0000 0.987 0.000 1.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.991 1.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0000 0.991 1.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 1 0.0000 0.991 1.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.987 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.987 0.000 1.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.991 1.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.987 0.000 1.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 2 0.0000 0.987 0.000 1.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 2 0.1633 0.965 0.024 0.976
#> 853120F0-857B-4108-9EC8-727189630C5F 2 0.0000 0.987 0.000 1.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.991 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.987 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.987 0.000 1.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.991 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.987 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 2 0.0000 0.987 0.000 1.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.991 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.987 0.000 1.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.991 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.991 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.991 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 2 0.0000 0.987 0.000 1.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.0000 0.987 0.000 1.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.987 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.991 1.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.991 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.0000 0.991 1.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.991 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 2 0.0000 0.987 0.000 1.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0000 0.991 1.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 2 0.0000 0.987 0.000 1.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.987 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.991 1.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.987 0.000 1.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.991 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.991 1.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.991 1.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.0000 0.987 0.000 1.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.0000 0.987 0.000 1.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.987 0.000 1.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0000 0.987 0.000 1.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.991 1.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.0000 0.991 1.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.987 0.000 1.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.0000 0.991 1.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 2 0.1184 0.972 0.016 0.984
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.991 1.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.6973 0.768 0.812 0.188
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.1633 0.970 0.976 0.024
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.987 0.000 1.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.991 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.991 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.0000 0.987 0.000 1.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.0000 0.991 1.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.987 0.000 1.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.987 0.000 1.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.0000 0.991 1.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.0000 0.987 0.000 1.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.0000 0.991 1.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.991 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.991 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0000 0.991 1.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.991 1.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.987 0.000 1.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.987 0.000 1.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.987 0.000 1.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.987 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.991 1.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.987 0.000 1.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.991 1.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.991 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.991 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.0000 0.987 0.000 1.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.991 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.991 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.991 1.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.987 0.000 1.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0000 0.987 0.000 1.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0000 0.987 0.000 1.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.991 1.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.991 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.987 0.000 1.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.987 0.000 1.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.0376 0.988 0.996 0.004
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.991 1.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.987 0.000 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.991 1.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 2 0.9323 0.466 0.348 0.652
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.0672 0.980 0.008 0.992
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.991 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.991 1.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.987 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.987 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.2423 0.954 0.960 0.040
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.3584 0.925 0.932 0.068
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.991 1.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.987 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.991 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.987 0.000 1.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.991 1.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.991 1.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.991 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.0000 0.987 0.000 1.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.987 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.991 1.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.991 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.991 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0000 0.987 0.000 1.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.7453 0.732 0.788 0.212
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.991 1.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.1184 0.978 0.984 0.016
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.991 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.987 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.0000 0.991 1.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.0000 0.987 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0747 0.830 0.984 0.016 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.6307 0.126 0.000 0.512 0.488
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.0000 0.718 0.000 0.000 1.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.4555 0.734 0.800 0.000 0.200
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.5835 0.584 0.000 0.340 0.660
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.965 0.000 1.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.5810 0.588 0.000 0.336 0.664
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.838 1.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0424 0.834 0.992 0.008 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0000 0.718 0.000 0.000 1.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.965 0.000 1.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.965 0.000 1.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.838 1.000 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0237 0.961 0.000 0.996 0.004
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.5178 0.656 0.000 0.256 0.744
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.4887 0.674 0.000 0.228 0.772
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.0237 0.719 0.000 0.004 0.996
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0592 0.832 0.988 0.000 0.012
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.965 0.000 1.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.965 0.000 1.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.838 1.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.965 0.000 1.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.6026 0.537 0.000 0.376 0.624
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.5968 0.580 0.636 0.000 0.364
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.965 0.000 1.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.838 1.000 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.838 1.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.838 1.000 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.0237 0.719 0.000 0.004 0.996
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.0237 0.961 0.000 0.996 0.004
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.965 0.000 1.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.838 1.000 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 3 0.2796 0.641 0.092 0.000 0.908
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.2796 0.765 0.908 0.000 0.092
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.838 1.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.4555 0.688 0.000 0.200 0.800
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.4235 0.754 0.824 0.000 0.176
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0237 0.719 0.000 0.004 0.996
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.965 0.000 1.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.838 1.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.965 0.000 1.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.6045 0.561 0.620 0.000 0.380
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.6215 -0.167 0.428 0.000 0.572
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.6235 0.362 0.436 0.000 0.564
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.5988 0.549 0.000 0.368 0.632
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.5988 0.549 0.000 0.368 0.632
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.965 0.000 1.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0000 0.965 0.000 1.000 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.838 1.000 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.5678 0.546 0.316 0.000 0.684
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.965 0.000 1.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.4452 0.648 0.192 0.000 0.808
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0000 0.718 0.000 0.000 1.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.5269 0.729 0.784 0.016 0.200
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.6298 0.450 0.388 0.004 0.608
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.9213 0.396 0.484 0.160 0.356
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.965 0.000 1.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.6244 0.465 0.560 0.000 0.440
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0424 0.834 0.992 0.000 0.008
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.6154 0.480 0.000 0.408 0.592
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.5988 0.482 0.368 0.000 0.632
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.965 0.000 1.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.965 0.000 1.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0000 0.718 0.000 0.000 1.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.5948 0.560 0.000 0.360 0.640
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.6192 0.396 0.420 0.000 0.580
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.838 1.000 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.838 1.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.0000 0.718 0.000 0.000 1.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.4178 0.753 0.828 0.000 0.172
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.965 0.000 1.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.965 0.000 1.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.965 0.000 1.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.965 0.000 1.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.3752 0.713 0.856 0.144 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.965 0.000 1.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.838 1.000 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.1964 0.812 0.944 0.000 0.056
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.6026 0.565 0.624 0.000 0.376
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.4235 0.729 0.000 0.824 0.176
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 3 0.4504 0.493 0.196 0.000 0.804
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0237 0.837 0.996 0.000 0.004
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0892 0.827 0.980 0.020 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.965 0.000 1.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.1529 0.915 0.040 0.960 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0000 0.965 0.000 1.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.838 1.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.838 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.965 0.000 1.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.965 0.000 1.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.5785 0.323 0.668 0.000 0.332
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.5835 0.606 0.660 0.000 0.340
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.965 0.000 1.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.6215 0.482 0.572 0.000 0.428
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.0000 0.718 0.000 0.000 1.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.5968 0.411 0.000 0.636 0.364
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0237 0.836 0.996 0.000 0.004
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.7276 0.670 0.704 0.104 0.192
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.965 0.000 1.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.965 0.000 1.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.0000 0.718 0.000 0.000 1.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.6126 0.317 0.600 0.400 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.838 1.000 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.965 0.000 1.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.5968 0.580 0.636 0.000 0.364
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.965 0.000 1.000 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.5988 0.575 0.632 0.000 0.368
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.5988 0.575 0.632 0.000 0.368
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.838 1.000 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.5905 0.570 0.000 0.352 0.648
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.965 0.000 1.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.838 1.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.838 1.000 0.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0424 0.834 0.992 0.000 0.008
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0000 0.965 0.000 1.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.0000 0.718 0.000 0.000 1.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.4452 0.739 0.808 0.000 0.192
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.6168 0.285 0.588 0.412 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.838 1.000 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.965 0.000 1.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.5560 0.563 0.300 0.000 0.700
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.6126 0.496 0.000 0.400 0.600
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.2149 0.831 0.000 0.088 0.000 0.912
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.3610 0.704 0.800 0.200 0.000 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.3649 0.668 0.796 0.000 0.204 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.4500 0.723 0.684 0.000 0.000 0.316
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0188 0.902 0.004 0.000 0.996 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0376 0.901 0.004 0.004 0.992 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.0817 0.913 0.000 0.024 0.000 0.976
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0336 0.902 0.008 0.000 0.992 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.1677 0.931 0.012 0.948 0.040 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.3942 0.815 0.236 0.000 0.764 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0336 0.902 0.008 0.000 0.992 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.4250 0.784 0.276 0.000 0.724 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0188 0.933 0.000 0.000 0.004 0.996
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0188 0.933 0.000 0.000 0.004 0.996
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.3873 0.818 0.228 0.000 0.772 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.3908 0.815 0.784 0.000 0.004 0.212
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0188 0.933 0.000 0.000 0.004 0.996
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0188 0.933 0.000 0.000 0.004 0.996
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.3942 0.815 0.236 0.000 0.764 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.1388 0.943 0.012 0.960 0.028 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.1022 0.950 0.000 0.968 0.032 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.5550 0.766 0.692 0.000 0.060 0.248
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.1118 0.905 0.000 0.000 0.036 0.964
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0188 0.933 0.000 0.000 0.004 0.996
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.4008 0.808 0.244 0.000 0.756 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.2081 0.839 0.084 0.000 0.000 0.916
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0657 0.901 0.012 0.004 0.984 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.3831 0.818 0.792 0.000 0.004 0.204
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.718 1.000 0.000 0.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.4977 0.125 0.000 0.000 0.460 0.540
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0188 0.901 0.000 0.004 0.996 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.3649 0.828 0.204 0.000 0.796 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.2401 0.888 0.004 0.904 0.092 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.0524 0.901 0.008 0.000 0.988 0.004
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0804 0.898 0.008 0.000 0.980 0.012
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0469 0.901 0.012 0.000 0.988 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.6563 0.703 0.632 0.208 0.000 0.160
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.0188 0.901 0.000 0.000 0.996 0.004
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.4364 0.699 0.764 0.220 0.000 0.016
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.3791 0.819 0.796 0.000 0.004 0.200
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0188 0.933 0.000 0.000 0.004 0.996
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.0000 0.902 0.000 0.000 1.000 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0376 0.902 0.004 0.000 0.992 0.004
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0817 0.956 0.000 0.976 0.024 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.1716 0.881 0.064 0.000 0.936 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0376 0.901 0.004 0.004 0.992 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.2868 0.795 0.000 0.000 0.864 0.136
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0188 0.933 0.000 0.000 0.004 0.996
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0188 0.933 0.000 0.000 0.004 0.996
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.4387 0.749 0.144 0.000 0.804 0.052
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.4679 0.674 0.648 0.000 0.000 0.352
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 4 0.4746 0.411 0.000 0.368 0.000 0.632
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.1211 0.944 0.000 0.960 0.040 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0524 0.928 0.004 0.000 0.008 0.988
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.3791 0.819 0.796 0.000 0.004 0.200
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.3356 0.751 0.176 0.824 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.3810 0.820 0.804 0.000 0.008 0.188
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.0188 0.933 0.000 0.000 0.004 0.996
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.2760 0.777 0.000 0.128 0.000 0.872
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0188 0.967 0.000 0.996 0.000 0.004
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0188 0.933 0.000 0.000 0.004 0.996
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.4948 0.243 0.000 0.000 0.560 0.440
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.3907 0.803 0.768 0.000 0.000 0.232
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.2973 0.812 0.856 0.000 0.000 0.144
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.0817 0.702 0.976 0.000 0.024 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.4454 0.581 0.692 0.308 0.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0336 0.930 0.000 0.000 0.008 0.992
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.6694 0.390 0.516 0.392 0.000 0.092
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0336 0.966 0.000 0.992 0.008 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0469 0.964 0.000 0.988 0.012 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.3688 0.664 0.792 0.000 0.208 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 4 0.4564 0.483 0.000 0.328 0.000 0.672
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.0188 0.931 0.004 0.000 0.000 0.996
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.3870 0.817 0.788 0.000 0.004 0.208
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0672 0.962 0.000 0.984 0.008 0.008
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.3726 0.816 0.788 0.000 0.000 0.212
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.3610 0.820 0.800 0.000 0.000 0.200
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0376 0.901 0.004 0.004 0.992 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0000 0.933 0.000 0.000 0.000 1.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0188 0.933 0.000 0.000 0.004 0.996
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0000 0.718 1.000 0.000 0.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.4907 0.554 0.580 0.000 0.000 0.420
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.4977 0.112 0.000 0.540 0.000 0.460
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0188 0.933 0.000 0.000 0.004 0.996
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.1022 0.950 0.000 0.968 0.032 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.0376 0.902 0.004 0.000 0.992 0.004
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.3649 0.828 0.204 0.000 0.796 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.3521 0.6673 0.004 0.232 0.000 0.764 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.2848 0.7494 0.840 0.156 0.000 0.000 0.004
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.3816 0.5600 0.696 0.000 0.304 0.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.2516 0.8125 0.860 0.000 0.000 0.140 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.9274 0.000 0.000 1.000 0.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0000 0.9274 0.000 0.000 1.000 0.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.0000 0.9461 0.000 0.000 0.000 1.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.0963 0.9170 0.000 0.036 0.000 0.964 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0162 0.9274 0.004 0.000 0.996 0.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0162 0.9152 0.000 0.996 0.004 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.0290 0.9415 0.000 0.008 0.000 0.992 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 5 0.4088 0.4804 0.000 0.368 0.000 0.000 0.632
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.1121 0.8399 0.044 0.000 0.000 0.000 0.956
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0324 0.9271 0.004 0.000 0.992 0.000 0.004
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.1197 0.8382 0.048 0.000 0.000 0.000 0.952
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.9461 0.000 0.000 0.000 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0162 0.9152 0.000 0.996 0.004 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0404 0.9123 0.000 0.988 0.012 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.9461 0.000 0.000 0.000 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0290 0.9140 0.000 0.992 0.008 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.0290 0.8402 0.008 0.000 0.000 0.000 0.992
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.2127 0.8335 0.892 0.000 0.000 0.108 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0162 0.9143 0.004 0.996 0.000 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.9461 0.000 0.000 0.000 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.9461 0.000 0.000 0.000 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.9461 0.000 0.000 0.000 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.1121 0.8399 0.044 0.000 0.000 0.000 0.956
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 5 0.4446 0.4023 0.008 0.400 0.000 0.000 0.592
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.3243 0.8018 0.036 0.860 0.012 0.000 0.092
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.0162 0.9442 0.000 0.004 0.000 0.996 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.4599 0.4545 0.624 0.000 0.356 0.020 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.0000 0.9461 0.000 0.000 0.000 1.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.9461 0.000 0.000 0.000 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.0963 0.8410 0.036 0.000 0.000 0.000 0.964
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.1740 0.8914 0.056 0.000 0.000 0.932 0.012
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0324 0.9271 0.004 0.000 0.992 0.000 0.004
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0162 0.9152 0.000 0.996 0.004 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.0290 0.9423 0.008 0.000 0.000 0.992 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.2074 0.8352 0.896 0.000 0.000 0.104 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0963 0.8185 0.964 0.000 0.000 0.000 0.036
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.2074 0.8337 0.000 0.000 0.896 0.104 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0000 0.9274 0.000 0.000 1.000 0.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.0404 0.8407 0.012 0.000 0.000 0.000 0.988
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0290 0.9140 0.000 0.992 0.008 0.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 5 0.2074 0.8118 0.036 0.044 0.000 0.000 0.920
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 4 0.0000 0.9461 0.000 0.000 0.000 1.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.0486 0.9262 0.004 0.000 0.988 0.004 0.004
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0324 0.9271 0.004 0.000 0.992 0.000 0.004
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0162 0.9274 0.004 0.000 0.996 0.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.3550 0.6655 0.760 0.236 0.000 0.004 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.0162 0.9268 0.000 0.000 0.996 0.004 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.2329 0.7841 0.876 0.124 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.1952 0.8404 0.912 0.000 0.004 0.084 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0162 0.9443 0.004 0.000 0.000 0.996 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.4470 0.3708 0.012 0.000 0.616 0.000 0.372
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0162 0.9268 0.000 0.000 0.996 0.004 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.1281 0.8946 0.000 0.956 0.032 0.000 0.012
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.1197 0.9011 0.048 0.000 0.952 0.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0000 0.9274 0.000 0.000 1.000 0.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.2074 0.8332 0.000 0.000 0.896 0.104 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0000 0.9461 0.000 0.000 0.000 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.9461 0.000 0.000 0.000 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.1478 0.8877 0.064 0.000 0.936 0.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.2732 0.7956 0.840 0.000 0.000 0.160 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 2 0.4302 0.0645 0.000 0.520 0.000 0.480 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.5601 0.2761 0.036 0.584 0.028 0.000 0.352
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.0162 0.9445 0.004 0.000 0.000 0.996 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0566 0.9379 0.012 0.000 0.004 0.984 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.1331 0.8364 0.952 0.000 0.000 0.040 0.008
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.1732 0.8447 0.080 0.920 0.000 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.1845 0.8234 0.928 0.000 0.056 0.016 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.1341 0.9008 0.056 0.000 0.000 0.944 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.4251 0.5210 0.012 0.316 0.000 0.672 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0162 0.9152 0.000 0.996 0.004 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0880 0.8924 0.000 0.968 0.000 0.032 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0324 0.9142 0.004 0.992 0.004 0.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.0000 0.9461 0.000 0.000 0.000 1.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.9461 0.000 0.000 0.000 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.1568 0.8799 0.036 0.944 0.000 0.000 0.020
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 4 0.4210 0.2755 0.000 0.000 0.412 0.588 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.1792 0.8400 0.916 0.000 0.000 0.084 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0162 0.9152 0.000 0.996 0.004 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0963 0.8185 0.964 0.000 0.000 0.000 0.036
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.2020 0.8004 0.100 0.000 0.000 0.000 0.900
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.3177 0.7053 0.792 0.208 0.000 0.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0000 0.9461 0.000 0.000 0.000 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 2 0.4302 -0.0308 0.480 0.520 0.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0609 0.9085 0.000 0.980 0.020 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.2002 0.8774 0.020 0.932 0.020 0.000 0.028
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.4291 0.0661 0.464 0.000 0.536 0.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 4 0.3143 0.7084 0.000 0.204 0.000 0.796 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.1493 0.9125 0.024 0.028 0.000 0.948 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.1579 0.8823 0.024 0.944 0.000 0.000 0.032
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.1965 0.8378 0.904 0.000 0.000 0.096 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 5 0.5607 0.5606 0.036 0.284 0.000 0.044 0.636
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.1872 0.8413 0.928 0.020 0.000 0.052 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0963 0.8185 0.964 0.000 0.000 0.000 0.036
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.9461 0.000 0.000 0.000 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0000 0.9274 0.000 0.000 1.000 0.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0162 0.9152 0.000 0.996 0.004 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.0162 0.9442 0.000 0.004 0.000 0.996 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0000 0.9461 0.000 0.000 0.000 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0000 0.9461 0.000 0.000 0.000 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.3983 0.4852 0.660 0.000 0.000 0.000 0.340
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.4679 0.6988 0.716 0.068 0.000 0.216 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.4268 0.1925 0.000 0.556 0.000 0.444 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.9461 0.000 0.000 0.000 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.1168 0.8967 0.008 0.960 0.032 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.0162 0.9270 0.000 0.000 0.996 0.000 0.004
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.0290 0.8383 0.008 0.000 0.000 0.000 0.992
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.5144 0.60079 0.120 0.236 0.000 0.636 0.000 0.008
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 6 0.5095 0.46960 0.104 0.312 0.000 0.000 0.000 0.584
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.3684 0.44656 0.000 0.000 0.628 0.000 0.000 0.372
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 6 0.4882 0.70841 0.168 0.044 0.000 0.076 0.000 0.712
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0146 0.91960 0.004 0.000 0.996 0.000 0.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.1453 0.47651 0.040 0.944 0.000 0.000 0.008 0.008
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0405 0.91899 0.008 0.000 0.988 0.000 0.000 0.004
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.0508 0.85100 0.012 0.000 0.000 0.984 0.000 0.004
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.2201 0.80278 0.076 0.028 0.000 0.896 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0146 0.92092 0.000 0.000 0.996 0.000 0.000 0.004
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.2520 0.39247 0.152 0.844 0.000 0.000 0.000 0.004
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.1204 0.46470 0.056 0.944 0.000 0.000 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.4705 0.66957 0.104 0.192 0.000 0.696 0.000 0.008
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 5 0.4426 0.38895 0.068 0.156 0.012 0.000 0.752 0.012
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.4409 0.67821 0.380 0.000 0.000 0.000 0.588 0.032
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0260 0.92045 0.000 0.000 0.992 0.000 0.000 0.008
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.4409 0.67823 0.380 0.000 0.000 0.000 0.588 0.032
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.85201 0.000 0.000 0.000 1.000 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.3782 0.00553 0.412 0.588 0.000 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.3852 0.06335 0.384 0.612 0.000 0.000 0.000 0.004
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.85201 0.000 0.000 0.000 1.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.3797 -0.01910 0.420 0.580 0.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.0146 0.58490 0.000 0.000 0.000 0.000 0.996 0.004
#> F5A814F6-E824-4DB2-8497-4B99E151D450 6 0.1429 0.77779 0.004 0.000 0.004 0.052 0.000 0.940
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.2078 0.46171 0.032 0.916 0.000 0.000 0.040 0.012
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0260 0.85135 0.000 0.000 0.000 0.992 0.000 0.008
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.85201 0.000 0.000 0.000 1.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0260 0.85146 0.000 0.000 0.000 0.992 0.000 0.008
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.4362 0.67665 0.388 0.000 0.000 0.000 0.584 0.028
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 5 0.4906 0.32760 0.064 0.220 0.000 0.000 0.684 0.032
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.4274 -0.09173 0.432 0.552 0.004 0.000 0.012 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.3525 0.77298 0.096 0.080 0.000 0.816 0.000 0.008
#> F5940915-4123-49B3-95EE-4A0412BE8C30 6 0.4815 0.19654 0.004 0.000 0.396 0.048 0.000 0.552
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.3706 0.43087 0.000 0.000 0.000 0.620 0.380 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.85201 0.000 0.000 0.000 1.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.4400 0.67901 0.376 0.000 0.000 0.000 0.592 0.032
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.5395 0.50430 0.204 0.000 0.000 0.644 0.124 0.028
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0405 0.91898 0.008 0.000 0.988 0.000 0.000 0.004
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.3907 0.00650 0.408 0.588 0.000 0.000 0.000 0.004
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.2070 0.81835 0.092 0.000 0.000 0.896 0.000 0.012
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0891 0.48072 0.024 0.968 0.000 0.000 0.000 0.008
#> 692C65BB-BF32-4846-806B-01A285BED1B9 6 0.1333 0.77886 0.000 0.000 0.008 0.048 0.000 0.944
#> CB925BF0-1249-4350-A175-9A4129C43B8D 6 0.1003 0.75682 0.020 0.000 0.000 0.000 0.016 0.964
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.1700 0.85844 0.000 0.000 0.916 0.080 0.000 0.004
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0260 0.91772 0.008 0.000 0.992 0.000 0.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.4310 0.67235 0.396 0.000 0.000 0.000 0.580 0.024
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.4024 0.02071 0.400 0.592 0.004 0.000 0.000 0.004
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 5 0.3032 0.48623 0.016 0.120 0.004 0.004 0.848 0.008
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 4 0.0291 0.85192 0.004 0.000 0.000 0.992 0.000 0.004
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.0692 0.91616 0.004 0.000 0.976 0.000 0.000 0.020
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.1152 0.47646 0.044 0.952 0.000 0.000 0.000 0.004
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0146 0.92092 0.000 0.000 0.996 0.000 0.000 0.004
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0260 0.92045 0.000 0.000 0.992 0.000 0.000 0.008
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 2 0.5894 -0.12317 0.184 0.468 0.000 0.004 0.000 0.344
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.0520 0.91494 0.008 0.000 0.984 0.008 0.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 6 0.4545 0.67102 0.176 0.124 0.000 0.000 0.000 0.700
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.1434 0.47538 0.028 0.948 0.000 0.000 0.012 0.012
#> A533C39D-CE42-42AD-92AD-549157A43139 6 0.1341 0.77418 0.000 0.000 0.024 0.028 0.000 0.948
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.1524 0.82704 0.000 0.000 0.008 0.932 0.000 0.060
#> 84E18629-1B13-4696-8E54-121ABE469CD2 5 0.4380 -0.02792 0.012 0.000 0.436 0.000 0.544 0.008
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0000 0.92078 0.000 0.000 1.000 0.000 0.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.4262 0.07595 0.364 0.616 0.012 0.000 0.004 0.004
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.1082 0.48033 0.040 0.956 0.000 0.000 0.000 0.004
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0713 0.91293 0.000 0.000 0.972 0.000 0.000 0.028
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0000 0.92078 0.000 0.000 1.000 0.000 0.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.1549 0.88761 0.000 0.000 0.936 0.044 0.000 0.020
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.1663 0.80784 0.000 0.000 0.000 0.912 0.088 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.85201 0.000 0.000 0.000 1.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.1387 0.88711 0.000 0.000 0.932 0.000 0.000 0.068
#> 352471DC-A881-4EA8-B646-EB1200291893 6 0.5110 0.59066 0.136 0.000 0.000 0.248 0.000 0.616
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0405 0.48269 0.008 0.988 0.000 0.000 0.000 0.004
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.2719 0.43146 0.040 0.876 0.000 0.000 0.072 0.012
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.1501 0.46151 0.076 0.924 0.000 0.000 0.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0603 0.47963 0.016 0.980 0.000 0.000 0.000 0.004
#> D891BCA1-0323-4277-BAF7-6F505377EA45 2 0.5608 -0.21985 0.124 0.440 0.000 0.432 0.000 0.004
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 1 0.5110 0.00000 0.592 0.324 0.004 0.000 0.076 0.004
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.2070 0.81835 0.092 0.000 0.000 0.896 0.000 0.012
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.3693 0.72641 0.004 0.000 0.084 0.796 0.000 0.116
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 6 0.1049 0.77761 0.000 0.000 0.008 0.032 0.000 0.960
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.3803 0.36025 0.184 0.760 0.000 0.000 0.000 0.056
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 6 0.1410 0.76394 0.004 0.000 0.044 0.008 0.000 0.944
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.2883 0.69410 0.000 0.000 0.000 0.788 0.000 0.212
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.5880 0.22262 0.152 0.412 0.000 0.428 0.000 0.008
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.3930 0.09569 0.364 0.628 0.004 0.000 0.000 0.004
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.3817 0.31102 0.020 0.772 0.000 0.188 0.008 0.012
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.4219 0.28832 0.320 0.648 0.000 0.000 0.000 0.032
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.2113 0.81828 0.092 0.004 0.000 0.896 0.000 0.008
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.85201 0.000 0.000 0.000 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.4356 0.33598 0.132 0.764 0.000 0.076 0.024 0.004
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.2632 0.37919 0.164 0.832 0.000 0.000 0.000 0.004
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.4649 0.24348 0.004 0.000 0.556 0.412 0.012 0.016
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 6 0.3293 0.76268 0.128 0.008 0.000 0.040 0.000 0.824
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.3899 0.01822 0.404 0.592 0.000 0.000 0.000 0.004
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 6 0.1668 0.77407 0.060 0.004 0.008 0.000 0.000 0.928
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.4957 0.65750 0.332 0.000 0.000 0.000 0.584 0.084
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.5782 -0.22063 0.176 0.424 0.000 0.000 0.000 0.400
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0146 0.85206 0.000 0.000 0.004 0.996 0.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 2 0.6158 0.18309 0.184 0.552 0.000 0.040 0.000 0.224
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.4122 -0.24999 0.472 0.520 0.004 0.000 0.000 0.004
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.3995 -0.27305 0.480 0.516 0.004 0.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.2823 0.73819 0.000 0.000 0.796 0.000 0.000 0.204
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 4 0.4027 0.68086 0.028 0.224 0.000 0.736 0.004 0.008
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.5372 0.58804 0.152 0.212 0.000 0.624 0.000 0.012
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.3491 0.34114 0.040 0.804 0.000 0.000 0.148 0.008
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 6 0.1285 0.77871 0.000 0.000 0.004 0.052 0.000 0.944
#> B3561356-5A80-4C79-B23A-D518425565FE 5 0.4998 0.19413 0.016 0.236 0.000 0.068 0.672 0.008
#> F900E9BE-2400-4451-9434-EE8BC513BA94 6 0.3139 0.75818 0.120 0.036 0.000 0.008 0.000 0.836
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 6 0.2326 0.77008 0.092 0.012 0.000 0.008 0.000 0.888
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.85201 0.000 0.000 0.000 1.000 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0146 0.92076 0.000 0.000 0.996 0.000 0.000 0.004
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.3774 0.01220 0.408 0.592 0.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.1082 0.84375 0.040 0.000 0.000 0.956 0.000 0.004
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0363 0.85147 0.000 0.000 0.000 0.988 0.000 0.012
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0632 0.84592 0.000 0.000 0.000 0.976 0.000 0.024
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.2848 0.39249 0.176 0.816 0.000 0.000 0.000 0.008
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 6 0.5463 0.14073 0.148 0.000 0.000 0.000 0.312 0.540
#> 12F54761-4F68-4181-8421-88EA858902FC 6 0.5921 0.48852 0.156 0.020 0.000 0.288 0.000 0.536
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 4 0.4818 0.21910 0.036 0.460 0.000 0.496 0.000 0.008
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0363 0.85125 0.000 0.000 0.000 0.988 0.000 0.012
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.3961 -0.10645 0.440 0.556 0.004 0.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.0000 0.92078 0.000 0.000 1.000 0.000 0.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.4228 0.67161 0.392 0.000 0.000 0.000 0.588 0.020
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 17548 rows and 122 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 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 0.503 0.677 0.798 0.4312 0.522 0.522
#> 3 3 0.600 0.571 0.778 0.4302 0.671 0.462
#> 4 4 0.672 0.812 0.892 0.1868 0.804 0.524
#> 5 5 0.696 0.724 0.862 0.0377 0.991 0.964
#> 6 6 0.735 0.691 0.823 0.0283 0.972 0.888
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.1843 0.5505 0.972 0.028
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.9944 0.6119 0.544 0.456
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.9954 0.6117 0.540 0.460
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.9954 0.6117 0.540 0.460
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 2 1.0000 0.9439 0.500 0.500
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.9963 0.9840 0.464 0.536
#> 9264567D-4524-46AF-A851-C091C3CD76CF 1 0.7602 -0.0997 0.780 0.220
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.1843 0.5505 0.972 0.028
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.1843 0.5505 0.972 0.028
#> 806616FE-1855-4284-9265-42842104CB21 1 0.2778 0.5092 0.952 0.048
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.9954 0.9858 0.460 0.540
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.9954 0.9858 0.460 0.540
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0938 0.5804 0.988 0.012
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.9970 0.9820 0.468 0.532
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 1 0.1414 0.5800 0.980 0.020
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 1 1.0000 -0.9459 0.500 0.500
#> 853120F0-857B-4108-9EC8-727189630C5F 1 0.1414 0.5800 0.980 0.020
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.1843 0.5505 0.972 0.028
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.9954 0.9858 0.460 0.540
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.9954 0.9858 0.460 0.540
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.1843 0.5505 0.972 0.028
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.9954 0.9858 0.460 0.540
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 1 0.9909 -0.8403 0.556 0.444
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.9954 0.6117 0.540 0.460
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.9954 0.9858 0.460 0.540
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.1843 0.5505 0.972 0.028
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0938 0.5804 0.988 0.012
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.6438 0.6069 0.836 0.164
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.1414 0.5800 0.980 0.020
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 1.0000 -0.9467 0.500 0.500
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.9954 0.9858 0.460 0.540
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.2043 0.5539 0.968 0.032
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.9954 0.6117 0.540 0.460
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 2 0.9996 0.9607 0.488 0.512
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.1843 0.5505 0.972 0.028
#> AA403EC3-FD44-4247-B06D-AEF415391E46 1 0.1414 0.5800 0.980 0.020
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.6148 0.6063 0.848 0.152
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.2043 0.5705 0.968 0.032
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.9954 0.9858 0.460 0.540
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0938 0.5804 0.988 0.012
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.9954 0.9858 0.460 0.540
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.9954 0.6117 0.540 0.460
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.9954 0.6117 0.540 0.460
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.1843 0.5505 0.972 0.028
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.9963 0.9849 0.464 0.536
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.9963 0.9849 0.464 0.536
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.9970 0.9820 0.468 0.532
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.9954 0.9858 0.460 0.540
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 2 0.9996 0.9607 0.488 0.512
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.2778 0.5092 0.952 0.048
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 1.0000 0.9508 0.496 0.504
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.2778 0.5092 0.952 0.048
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.2603 0.5168 0.956 0.044
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.9954 0.6117 0.540 0.460
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 2 0.9963 0.9849 0.464 0.536
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.9954 0.6117 0.540 0.460
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.9954 0.9858 0.460 0.540
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.9954 0.6117 0.540 0.460
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.6438 0.6069 0.836 0.164
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.9954 0.9858 0.460 0.540
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 2 0.9996 0.9607 0.488 0.512
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.9954 0.9858 0.460 0.540
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.9970 0.9820 0.468 0.532
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.9944 0.6119 0.544 0.456
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.9963 0.9849 0.464 0.536
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.2778 0.5092 0.952 0.048
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 2 1.0000 0.9506 0.496 0.504
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.1843 0.5505 0.972 0.028
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.9954 0.6117 0.540 0.460
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.9954 0.6117 0.540 0.460
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.9954 0.9858 0.460 0.540
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.9954 0.9858 0.460 0.540
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 1.0000 0.9446 0.500 0.500
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.9954 0.9858 0.460 0.540
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.1843 0.5505 0.972 0.028
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.9954 0.9858 0.460 0.540
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.5946 0.6050 0.856 0.144
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.6438 0.6069 0.836 0.164
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.9954 0.6117 0.540 0.460
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.2603 0.5168 0.956 0.044
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.9954 0.6117 0.540 0.460
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.9954 0.6117 0.540 0.460
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.1843 0.5505 0.972 0.028
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.9954 0.9858 0.460 0.540
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.9963 0.9849 0.464 0.536
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.2603 0.5168 0.956 0.044
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0938 0.5804 0.988 0.012
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 2 1.0000 0.9506 0.496 0.504
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.9954 0.9858 0.460 0.540
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 1.0000 0.9508 0.496 0.504
#> F25A7521-2596-4D60-BABE-862023C40D40 1 1.0000 -0.9393 0.504 0.496
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.9954 0.6117 0.540 0.460
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.9954 0.9858 0.460 0.540
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.9954 0.6117 0.540 0.460
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.9954 0.6117 0.540 0.460
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.9944 0.6119 0.544 0.456
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.1843 0.5505 0.972 0.028
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.9954 0.6117 0.540 0.460
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.9954 0.9858 0.460 0.540
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.9954 0.9858 0.460 0.540
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.9954 0.6117 0.540 0.460
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 2 0.9996 0.9607 0.488 0.512
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.1843 0.5505 0.972 0.028
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.9954 0.9858 0.460 0.540
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.9954 0.6117 0.540 0.460
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.9963 0.9849 0.464 0.536
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.9954 0.6117 0.540 0.460
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.9954 0.6117 0.540 0.460
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.1843 0.5505 0.972 0.028
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.9963 0.9849 0.464 0.536
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.9954 0.9858 0.460 0.540
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.1843 0.5505 0.972 0.028
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.1843 0.5505 0.972 0.028
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.1843 0.5505 0.972 0.028
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.2603 0.5168 0.956 0.044
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.9954 0.6117 0.540 0.460
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.9954 0.6117 0.540 0.460
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.1843 0.5505 0.972 0.028
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0938 0.5804 0.988 0.012
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.9954 0.9858 0.460 0.540
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.2778 0.5092 0.952 0.048
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.9963 0.9849 0.464 0.536
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.6950 0.927 0.508 0.476 0.016
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 3 0.4883 0.772 0.208 0.004 0.788
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.0424 0.890 0.008 0.000 0.992
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 3 0.3573 0.843 0.120 0.004 0.876
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 2 0.1860 0.381 0.052 0.948 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.5138 0.559 0.252 0.748 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 2 0.5785 -0.546 0.332 0.668 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.6954 0.921 0.500 0.484 0.016
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.6954 0.921 0.500 0.484 0.016
#> 806616FE-1855-4284-9265-42842104CB21 2 0.7668 -0.867 0.460 0.496 0.044
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.6140 0.585 0.404 0.596 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.6291 0.570 0.468 0.532 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.7895 0.916 0.508 0.436 0.056
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0592 0.439 0.012 0.988 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 1 0.7895 0.915 0.508 0.436 0.056
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 2 0.1860 0.381 0.052 0.948 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 1 0.7895 0.915 0.508 0.436 0.056
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.6950 0.927 0.508 0.476 0.016
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.6308 0.561 0.492 0.508 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.6308 0.561 0.492 0.508 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.6950 0.927 0.508 0.476 0.016
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.6308 0.561 0.492 0.508 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 2 0.3116 0.248 0.108 0.892 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 3 0.0000 0.891 0.000 0.000 1.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.6308 0.561 0.492 0.508 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.6950 0.927 0.508 0.476 0.016
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.7895 0.916 0.508 0.436 0.056
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.9501 0.718 0.488 0.288 0.224
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.7895 0.915 0.508 0.436 0.056
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.1753 0.388 0.048 0.952 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.6308 0.561 0.492 0.508 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.7069 0.926 0.508 0.472 0.020
#> F5940915-4123-49B3-95EE-4A0412BE8C30 3 0.3573 0.843 0.120 0.004 0.876
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 2 0.1031 0.423 0.024 0.976 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.6950 0.927 0.508 0.476 0.016
#> AA403EC3-FD44-4247-B06D-AEF415391E46 1 0.7895 0.915 0.508 0.436 0.056
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.9405 0.747 0.496 0.300 0.204
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.7657 0.912 0.508 0.448 0.044
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.6308 0.561 0.492 0.508 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.7895 0.916 0.508 0.436 0.056
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.6291 0.570 0.468 0.532 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 3 0.0000 0.891 0.000 0.000 1.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.0000 0.891 0.000 0.000 1.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.6950 0.927 0.508 0.476 0.016
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.0592 0.439 0.012 0.988 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.2165 0.491 0.064 0.936 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0592 0.439 0.012 0.988 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.6168 0.582 0.412 0.588 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 2 0.1289 0.432 0.032 0.968 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 2 0.7668 -0.867 0.460 0.496 0.044
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.1753 0.389 0.048 0.952 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 2 0.7668 -0.867 0.460 0.496 0.044
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 2 0.7671 -0.873 0.464 0.492 0.044
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 3 0.5929 0.654 0.320 0.004 0.676
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 2 0.0000 0.449 0.000 1.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 3 0.0000 0.891 0.000 0.000 1.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.6308 0.561 0.492 0.508 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 3 0.0000 0.891 0.000 0.000 1.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.9501 0.718 0.488 0.288 0.224
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.6168 0.582 0.412 0.588 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 2 0.1031 0.423 0.024 0.976 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.6308 0.561 0.492 0.508 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.1411 0.469 0.036 0.964 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.4883 0.772 0.208 0.004 0.788
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.0592 0.439 0.012 0.988 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 2 0.7668 -0.867 0.460 0.496 0.044
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 2 0.1289 0.412 0.032 0.968 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.6950 0.927 0.508 0.476 0.016
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.5929 0.654 0.320 0.004 0.676
#> 352471DC-A881-4EA8-B646-EB1200291893 3 0.5929 0.654 0.320 0.004 0.676
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.6291 0.570 0.468 0.532 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.6291 0.570 0.468 0.532 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.1860 0.381 0.052 0.948 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.6308 0.561 0.492 0.508 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.6950 0.927 0.508 0.476 0.016
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.6308 0.561 0.492 0.508 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.9408 0.751 0.492 0.308 0.200
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.9501 0.718 0.488 0.288 0.224
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 3 0.0000 0.891 0.000 0.000 1.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.7671 -0.873 0.464 0.492 0.044
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 3 0.0000 0.891 0.000 0.000 1.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 3 0.3573 0.843 0.120 0.004 0.876
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.6950 0.927 0.508 0.476 0.016
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.6140 0.585 0.404 0.596 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.2625 0.498 0.084 0.916 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.7671 -0.873 0.464 0.492 0.044
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.7895 0.916 0.508 0.436 0.056
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 2 0.1289 0.412 0.032 0.968 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.6308 0.561 0.492 0.508 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.1753 0.389 0.048 0.952 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 2 0.1964 0.373 0.056 0.944 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 3 0.0000 0.891 0.000 0.000 1.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.6308 0.561 0.492 0.508 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 3 0.0000 0.891 0.000 0.000 1.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.0000 0.891 0.000 0.000 1.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 3 0.4883 0.772 0.208 0.004 0.788
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.6950 0.927 0.508 0.476 0.016
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 3 0.5929 0.654 0.320 0.004 0.676
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.6308 0.561 0.492 0.508 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.6308 0.561 0.492 0.508 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.0424 0.890 0.008 0.000 0.992
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 2 0.1289 0.432 0.032 0.968 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.6950 0.927 0.508 0.476 0.016
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.6308 0.561 0.492 0.508 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 3 0.0000 0.891 0.000 0.000 1.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.5178 0.556 0.256 0.744 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 3 0.0000 0.891 0.000 0.000 1.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 3 0.0000 0.891 0.000 0.000 1.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.6950 0.927 0.508 0.476 0.016
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.0592 0.439 0.012 0.988 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.6308 0.561 0.492 0.508 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.6954 0.921 0.500 0.484 0.016
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.6950 0.927 0.508 0.476 0.016
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.6950 0.927 0.508 0.476 0.016
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.7671 -0.873 0.464 0.492 0.044
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.0000 0.891 0.000 0.000 1.000
#> 12F54761-4F68-4181-8421-88EA858902FC 3 0.5929 0.654 0.320 0.004 0.676
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.6954 0.921 0.500 0.484 0.016
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.7895 0.916 0.508 0.436 0.056
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.6308 0.561 0.492 0.508 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 2 0.7668 -0.867 0.460 0.496 0.044
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.2165 0.491 0.064 0.936 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.1302 0.8759 0.000 0.000 0.044 0.956
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.4049 0.7366 0.780 0.000 0.008 0.212
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0672 0.8572 0.984 0.000 0.008 0.008
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.3681 0.7870 0.816 0.000 0.008 0.176
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.1610 0.8863 0.000 0.016 0.952 0.032
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 3 0.4741 0.4762 0.000 0.328 0.668 0.004
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.4830 0.1305 0.000 0.000 0.608 0.392
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.1722 0.8735 0.000 0.008 0.048 0.944
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.1722 0.8735 0.000 0.008 0.048 0.944
#> 806616FE-1855-4284-9265-42842104CB21 4 0.4008 0.7532 0.000 0.000 0.244 0.756
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.4072 0.6874 0.000 0.748 0.252 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.2408 0.8580 0.000 0.896 0.104 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.0188 0.8601 0.004 0.000 0.000 0.996
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 3 0.1938 0.8857 0.000 0.052 0.936 0.012
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 4 0.3873 0.7705 0.000 0.000 0.228 0.772
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.1610 0.8863 0.000 0.016 0.952 0.032
#> 853120F0-857B-4108-9EC8-727189630C5F 4 0.3873 0.7705 0.000 0.000 0.228 0.772
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.1302 0.8759 0.000 0.000 0.044 0.956
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.1302 0.8759 0.000 0.000 0.044 0.956
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.2647 0.8295 0.000 0.000 0.880 0.120
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0469 0.9086 0.000 0.988 0.012 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.1302 0.8759 0.000 0.000 0.044 0.956
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0188 0.8601 0.004 0.000 0.000 0.996
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.3311 0.7001 0.172 0.000 0.000 0.828
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 4 0.3873 0.7705 0.000 0.000 0.228 0.772
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 3 0.1510 0.8886 0.000 0.016 0.956 0.028
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.1305 0.8739 0.004 0.000 0.036 0.960
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.3681 0.7870 0.816 0.000 0.008 0.176
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.1837 0.8870 0.000 0.028 0.944 0.028
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.1302 0.8759 0.000 0.000 0.044 0.956
#> AA403EC3-FD44-4247-B06D-AEF415391E46 4 0.3873 0.7705 0.000 0.000 0.228 0.772
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.4139 0.7340 0.144 0.000 0.040 0.816
#> 50D620F3-5C52-42FB-89A1-6840A7444647 4 0.3975 0.7593 0.000 0.000 0.240 0.760
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.0188 0.8601 0.004 0.000 0.000 0.996
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.2408 0.8580 0.000 0.896 0.104 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.1302 0.8759 0.000 0.000 0.044 0.956
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.1677 0.8885 0.000 0.040 0.948 0.012
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.2888 0.8404 0.000 0.124 0.872 0.004
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 3 0.1854 0.8864 0.000 0.048 0.940 0.012
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.4134 0.6537 0.000 0.740 0.260 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 3 0.4630 0.7458 0.000 0.036 0.768 0.196
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.4008 0.7532 0.000 0.000 0.244 0.756
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 3 0.1284 0.8853 0.000 0.012 0.964 0.024
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.4008 0.7532 0.000 0.000 0.244 0.756
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 4 0.3873 0.7685 0.000 0.000 0.228 0.772
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.5085 0.5438 0.616 0.000 0.008 0.376
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.2546 0.8825 0.000 0.060 0.912 0.028
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.3311 0.7001 0.172 0.000 0.000 0.828
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.4134 0.6537 0.000 0.740 0.260 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.1837 0.8870 0.000 0.028 0.944 0.028
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 3 0.2480 0.8655 0.000 0.088 0.904 0.008
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.4049 0.7366 0.780 0.000 0.008 0.212
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.1677 0.8885 0.000 0.040 0.948 0.012
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.4008 0.7532 0.000 0.000 0.244 0.756
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 3 0.4501 0.7395 0.000 0.024 0.764 0.212
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.1302 0.8759 0.000 0.000 0.044 0.956
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.5085 0.5438 0.616 0.000 0.008 0.376
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.5085 0.5438 0.616 0.000 0.008 0.376
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.2408 0.8580 0.000 0.896 0.104 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.2408 0.8580 0.000 0.896 0.104 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 3 0.1388 0.8851 0.000 0.012 0.960 0.028
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 4 0.1302 0.8759 0.000 0.000 0.044 0.956
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.3024 0.7339 0.148 0.000 0.000 0.852
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.3311 0.7001 0.172 0.000 0.000 0.828
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 4 0.3873 0.7685 0.000 0.000 0.228 0.772
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.3681 0.7870 0.816 0.000 0.008 0.176
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.1302 0.8759 0.000 0.000 0.044 0.956
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.4040 0.6932 0.000 0.752 0.248 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 3 0.3852 0.7639 0.000 0.192 0.800 0.008
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 4 0.3873 0.7685 0.000 0.000 0.228 0.772
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.0188 0.8601 0.004 0.000 0.000 0.996
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 3 0.4501 0.7395 0.000 0.024 0.764 0.212
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 3 0.1284 0.8853 0.000 0.012 0.964 0.024
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.2142 0.8779 0.000 0.016 0.928 0.056
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.4049 0.7366 0.780 0.000 0.008 0.212
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.1302 0.8759 0.000 0.000 0.044 0.956
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.5085 0.5438 0.616 0.000 0.008 0.376
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0672 0.8572 0.984 0.000 0.008 0.008
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 3 0.2227 0.8858 0.000 0.036 0.928 0.036
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.1302 0.8759 0.000 0.000 0.044 0.956
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.5163 -0.0037 0.000 0.516 0.480 0.004
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.1302 0.8759 0.000 0.000 0.044 0.956
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.1677 0.8885 0.000 0.040 0.948 0.012
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.1722 0.8735 0.000 0.008 0.048 0.944
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.1302 0.8759 0.000 0.000 0.044 0.956
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.1302 0.8759 0.000 0.000 0.044 0.956
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 4 0.3873 0.7685 0.000 0.000 0.228 0.772
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.5085 0.5438 0.616 0.000 0.008 0.376
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 4 0.1722 0.8735 0.000 0.008 0.048 0.944
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0188 0.8601 0.004 0.000 0.000 0.996
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.4008 0.7532 0.000 0.000 0.244 0.756
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.2888 0.8404 0.000 0.124 0.872 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.0290 0.81808 0.000 0.000 0.008 0.992 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.4168 0.67973 0.756 0.000 0.000 0.200 0.044
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0703 0.81367 0.976 0.000 0.000 0.000 0.024
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.4104 0.71952 0.788 0.000 0.000 0.124 0.088
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.3621 0.65814 0.000 0.000 0.788 0.020 0.192
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 3 0.4142 0.45463 0.000 0.308 0.684 0.004 0.004
#> 9264567D-4524-46AF-A851-C091C3CD76CF 5 0.4298 -0.06849 0.000 0.000 0.352 0.008 0.640
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.0693 0.81501 0.000 0.000 0.012 0.980 0.008
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.0693 0.81501 0.000 0.000 0.012 0.980 0.008
#> 806616FE-1855-4284-9265-42842104CB21 4 0.5240 0.53785 0.000 0.000 0.216 0.672 0.112
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.4333 0.70519 0.000 0.740 0.212 0.000 0.048
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.2179 0.85288 0.000 0.896 0.100 0.000 0.004
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.1197 0.80250 0.000 0.000 0.000 0.952 0.048
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 3 0.1202 0.75527 0.000 0.032 0.960 0.004 0.004
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 4 0.4147 0.56547 0.000 0.000 0.008 0.676 0.316
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.3621 0.65814 0.000 0.000 0.788 0.020 0.192
#> 853120F0-857B-4108-9EC8-727189630C5F 4 0.4147 0.56547 0.000 0.000 0.008 0.676 0.316
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0290 0.81808 0.000 0.000 0.008 0.992 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.90663 0.000 1.000 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0162 0.90491 0.000 0.996 0.004 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0290 0.81808 0.000 0.000 0.008 0.992 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.90663 0.000 1.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.5243 0.59493 0.000 0.000 0.680 0.132 0.188
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.82029 1.000 0.000 0.000 0.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0451 0.90172 0.000 0.988 0.008 0.000 0.004
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0290 0.81808 0.000 0.000 0.008 0.992 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.1197 0.80250 0.000 0.000 0.000 0.952 0.048
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.4096 0.64007 0.144 0.000 0.000 0.784 0.072
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 4 0.4147 0.56547 0.000 0.000 0.008 0.676 0.316
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 3 0.0898 0.75220 0.000 0.000 0.972 0.020 0.008
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.90663 0.000 1.000 0.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.0162 0.81505 0.000 0.000 0.000 0.996 0.004
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.4104 0.71952 0.788 0.000 0.000 0.124 0.088
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.3146 0.73503 0.000 0.000 0.856 0.052 0.092
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0290 0.81808 0.000 0.000 0.008 0.992 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 4 0.4147 0.56547 0.000 0.000 0.008 0.676 0.316
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.4641 0.65174 0.116 0.000 0.008 0.760 0.116
#> 50D620F3-5C52-42FB-89A1-6840A7444647 5 0.4108 0.16390 0.000 0.000 0.008 0.308 0.684
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.90663 0.000 1.000 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.1197 0.80250 0.000 0.000 0.000 0.952 0.048
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.2179 0.85288 0.000 0.896 0.100 0.000 0.004
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.82029 1.000 0.000 0.000 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.82029 1.000 0.000 0.000 0.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.0290 0.81808 0.000 0.000 0.008 0.992 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.3586 0.67831 0.000 0.020 0.792 0.000 0.188
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.3648 0.72118 0.000 0.092 0.824 0.000 0.084
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 3 0.1116 0.75518 0.000 0.028 0.964 0.004 0.004
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.3916 0.62248 0.000 0.732 0.256 0.000 0.012
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 3 0.5385 0.51557 0.000 0.008 0.668 0.232 0.092
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.4847 0.57835 0.000 0.000 0.216 0.704 0.080
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 3 0.2583 0.71588 0.000 0.000 0.864 0.004 0.132
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.5240 0.53785 0.000 0.000 0.216 0.672 0.112
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 4 0.5339 0.55662 0.000 0.000 0.176 0.672 0.152
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.5538 0.51205 0.588 0.000 0.000 0.324 0.088
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.3857 0.73832 0.000 0.028 0.832 0.052 0.088
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.82029 1.000 0.000 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.90663 0.000 1.000 0.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.82029 1.000 0.000 0.000 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.4096 0.64007 0.144 0.000 0.000 0.784 0.072
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.3916 0.62248 0.000 0.732 0.256 0.000 0.012
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.3146 0.73503 0.000 0.000 0.856 0.052 0.092
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.90663 0.000 1.000 0.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 3 0.1864 0.74645 0.000 0.068 0.924 0.004 0.004
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.4168 0.67973 0.756 0.000 0.000 0.200 0.044
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.3586 0.67831 0.000 0.020 0.792 0.000 0.188
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.5240 0.53785 0.000 0.000 0.216 0.672 0.112
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 3 0.5216 0.49540 0.000 0.000 0.660 0.248 0.092
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0290 0.81808 0.000 0.000 0.008 0.992 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.5538 0.51205 0.588 0.000 0.000 0.324 0.088
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.5538 0.51205 0.588 0.000 0.000 0.324 0.088
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.2179 0.85288 0.000 0.896 0.100 0.000 0.004
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.2179 0.85288 0.000 0.896 0.100 0.000 0.004
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 3 0.2707 0.71375 0.000 0.000 0.860 0.008 0.132
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.90663 0.000 1.000 0.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 4 0.0290 0.81808 0.000 0.000 0.008 0.992 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.90663 0.000 1.000 0.000 0.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.3798 0.67119 0.128 0.000 0.000 0.808 0.064
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.4096 0.64007 0.144 0.000 0.000 0.784 0.072
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.82029 1.000 0.000 0.000 0.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 4 0.5339 0.55662 0.000 0.000 0.176 0.672 0.152
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.82029 1.000 0.000 0.000 0.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.4104 0.71952 0.788 0.000 0.000 0.124 0.088
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.0290 0.81808 0.000 0.000 0.008 0.992 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.4302 0.70998 0.000 0.744 0.208 0.000 0.048
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 3 0.4215 0.64383 0.000 0.172 0.772 0.004 0.052
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 4 0.5339 0.55662 0.000 0.000 0.176 0.672 0.152
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.1197 0.80250 0.000 0.000 0.000 0.952 0.048
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 3 0.5216 0.49540 0.000 0.000 0.660 0.248 0.092
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.90663 0.000 1.000 0.000 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 3 0.2583 0.71588 0.000 0.000 0.864 0.004 0.132
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.2853 0.73385 0.000 0.000 0.876 0.072 0.052
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.82029 1.000 0.000 0.000 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.90663 0.000 1.000 0.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.82029 1.000 0.000 0.000 0.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.0000 0.82029 1.000 0.000 0.000 0.000 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.4168 0.67973 0.756 0.000 0.000 0.200 0.044
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0290 0.81808 0.000 0.000 0.008 0.992 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.5538 0.51205 0.588 0.000 0.000 0.324 0.088
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.90663 0.000 1.000 0.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.90663 0.000 1.000 0.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0703 0.81367 0.976 0.000 0.000 0.000 0.024
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 3 0.2819 0.74821 0.000 0.008 0.884 0.032 0.076
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.0290 0.81808 0.000 0.000 0.008 0.992 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.90663 0.000 1.000 0.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.82029 1.000 0.000 0.000 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.4658 -0.00422 0.000 0.504 0.484 0.000 0.012
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.82029 1.000 0.000 0.000 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.82029 1.000 0.000 0.000 0.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0290 0.81808 0.000 0.000 0.008 0.992 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.3586 0.67831 0.000 0.020 0.792 0.000 0.188
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.90663 0.000 1.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.0693 0.81501 0.000 0.000 0.012 0.980 0.008
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0290 0.81808 0.000 0.000 0.008 0.992 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0290 0.81808 0.000 0.000 0.008 0.992 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 4 0.5339 0.55662 0.000 0.000 0.176 0.672 0.152
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0000 0.82029 1.000 0.000 0.000 0.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.5538 0.51205 0.588 0.000 0.000 0.324 0.088
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 4 0.0693 0.81501 0.000 0.000 0.012 0.980 0.008
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.1197 0.80250 0.000 0.000 0.000 0.952 0.048
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.90663 0.000 1.000 0.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.5240 0.53785 0.000 0.000 0.216 0.672 0.112
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.3648 0.72118 0.000 0.092 0.824 0.000 0.084
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.7966 1.000 0.000 0.000 0.000 0.000 NA
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 4 0.4672 0.6346 0.200 0.000 0.000 0.704 0.016 NA
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 4 0.0820 0.8019 0.000 0.000 0.000 0.972 0.012 NA
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 4 0.4780 0.6909 0.116 0.000 0.000 0.736 0.056 NA
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.6106 0.5878 0.020 0.000 0.508 0.000 0.188 NA
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 3 0.5080 0.5131 0.004 0.288 0.632 0.000 0.020 NA
#> 9264567D-4524-46AF-A851-C091C3CD76CF 5 0.4946 -0.0707 0.000 0.000 0.068 0.000 0.528 NA
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0363 0.7917 0.988 0.000 0.012 0.000 0.000 NA
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0363 0.7917 0.988 0.000 0.012 0.000 0.000 NA
#> 806616FE-1855-4284-9265-42842104CB21 1 0.5090 0.3217 0.592 0.000 0.024 0.000 0.336 NA
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.4286 0.6766 0.000 0.720 0.208 0.000 0.004 NA
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.1863 0.8664 0.000 0.896 0.104 0.000 0.000 NA
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.1367 0.7720 0.944 0.000 0.000 0.000 0.044 NA
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 3 0.2663 0.7098 0.004 0.012 0.884 0.000 0.032 NA
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.5790 0.6429 0.360 0.000 0.000 0.000 0.456 NA
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.6106 0.5878 0.020 0.000 0.508 0.000 0.188 NA
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.5790 0.6429 0.360 0.000 0.000 0.000 0.456 NA
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.7966 1.000 0.000 0.000 0.000 0.000 NA
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.9270 0.000 1.000 0.000 0.000 0.000 NA
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0632 0.9151 0.000 0.976 0.024 0.000 0.000 NA
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.7966 1.000 0.000 0.000 0.000 0.000 NA
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.9270 0.000 1.000 0.000 0.000 0.000 NA
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.6020 0.3363 0.060 0.000 0.476 0.000 0.072 NA
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.0000 0.8094 0.000 0.000 0.000 1.000 0.000 NA
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0363 0.9219 0.000 0.988 0.012 0.000 0.000 NA
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.7966 1.000 0.000 0.000 0.000 0.000 NA
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.1367 0.7720 0.944 0.000 0.000 0.000 0.044 NA
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.4298 0.5974 0.776 0.000 0.000 0.092 0.048 NA
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.5790 0.6429 0.360 0.000 0.000 0.000 0.456 NA
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 3 0.3019 0.7073 0.020 0.000 0.856 0.000 0.032 NA
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.9270 0.000 1.000 0.000 0.000 0.000 NA
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0405 0.7929 0.988 0.000 0.000 0.000 0.008 NA
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.4780 0.6909 0.116 0.000 0.000 0.736 0.056 NA
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.2046 0.6877 0.060 0.000 0.908 0.000 0.000 NA
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.7966 1.000 0.000 0.000 0.000 0.000 NA
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.5790 0.6429 0.360 0.000 0.000 0.000 0.456 NA
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.4628 0.5885 0.752 0.000 0.000 0.064 0.092 NA
#> 50D620F3-5C52-42FB-89A1-6840A7444647 5 0.4368 0.4383 0.048 0.000 0.000 0.000 0.656 NA
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.9270 0.000 1.000 0.000 0.000 0.000 NA
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.1367 0.7720 0.944 0.000 0.000 0.000 0.044 NA
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.1863 0.8664 0.000 0.896 0.104 0.000 0.000 NA
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.0000 0.8094 0.000 0.000 0.000 1.000 0.000 NA
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.0000 0.8094 0.000 0.000 0.000 1.000 0.000 NA
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.7966 1.000 0.000 0.000 0.000 0.000 NA
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.5600 0.5986 0.000 0.000 0.528 0.000 0.176 NA
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.1983 0.6846 0.000 0.072 0.908 0.000 0.000 NA
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 3 0.2487 0.7093 0.004 0.008 0.892 0.000 0.028 NA
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.3586 0.5949 0.000 0.720 0.268 0.000 0.000 NA
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 3 0.4259 0.5616 0.240 0.008 0.708 0.000 0.000 NA
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.4976 0.3725 0.624 0.000 0.024 0.000 0.304 NA
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 3 0.5666 0.6207 0.004 0.000 0.552 0.000 0.196 NA
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.5090 0.3217 0.592 0.000 0.024 0.000 0.336 NA
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.4283 0.3164 0.592 0.000 0.024 0.000 0.384 NA
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 4 0.6023 0.4556 0.316 0.000 0.000 0.536 0.056 NA
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.1781 0.6889 0.060 0.008 0.924 0.000 0.000 NA
#> B5474EEB-D585-4668-959C-38F240F55BC2 4 0.0000 0.8094 0.000 0.000 0.000 1.000 0.000 NA
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.9270 0.000 1.000 0.000 0.000 0.000 NA
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.0000 0.8094 0.000 0.000 0.000 1.000 0.000 NA
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.4298 0.5974 0.776 0.000 0.000 0.092 0.048 NA
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.3586 0.5949 0.000 0.720 0.268 0.000 0.000 NA
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.2046 0.6877 0.060 0.000 0.908 0.000 0.000 NA
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.9270 0.000 1.000 0.000 0.000 0.000 NA
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 3 0.2941 0.7060 0.004 0.048 0.872 0.000 0.020 NA
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 4 0.4672 0.6346 0.200 0.000 0.000 0.704 0.016 NA
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.5600 0.5986 0.000 0.000 0.528 0.000 0.176 NA
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.5090 0.3217 0.592 0.000 0.024 0.000 0.336 NA
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 3 0.4107 0.5431 0.256 0.000 0.700 0.000 0.000 NA
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.7966 1.000 0.000 0.000 0.000 0.000 NA
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.6023 0.4556 0.316 0.000 0.000 0.536 0.056 NA
#> 352471DC-A881-4EA8-B646-EB1200291893 4 0.6023 0.4556 0.316 0.000 0.000 0.536 0.056 NA
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.1863 0.8664 0.000 0.896 0.104 0.000 0.000 NA
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.1863 0.8664 0.000 0.896 0.104 0.000 0.000 NA
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 3 0.5768 0.6173 0.008 0.000 0.548 0.000 0.196 NA
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.9270 0.000 1.000 0.000 0.000 0.000 NA
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.7966 1.000 0.000 0.000 0.000 0.000 NA
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.9270 0.000 1.000 0.000 0.000 0.000 NA
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.3984 0.6308 0.800 0.000 0.000 0.076 0.044 NA
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.4298 0.5974 0.776 0.000 0.000 0.092 0.048 NA
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.0000 0.8094 0.000 0.000 0.000 1.000 0.000 NA
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.4283 0.3164 0.592 0.000 0.024 0.000 0.384 NA
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.0000 0.8094 0.000 0.000 0.000 1.000 0.000 NA
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.4780 0.6909 0.116 0.000 0.000 0.736 0.056 NA
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.7966 1.000 0.000 0.000 0.000 0.000 NA
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.4258 0.6831 0.000 0.724 0.204 0.000 0.004 NA
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 3 0.2520 0.6436 0.004 0.152 0.844 0.000 0.000 NA
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.4283 0.3164 0.592 0.000 0.024 0.000 0.384 NA
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.1367 0.7720 0.944 0.000 0.000 0.000 0.044 NA
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 3 0.4107 0.5431 0.256 0.000 0.700 0.000 0.000 NA
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.9270 0.000 1.000 0.000 0.000 0.000 NA
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 3 0.5666 0.6207 0.004 0.000 0.552 0.000 0.196 NA
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.5662 0.6522 0.080 0.000 0.652 0.000 0.160 NA
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 4 0.0000 0.8094 0.000 0.000 0.000 1.000 0.000 NA
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.9270 0.000 1.000 0.000 0.000 0.000 NA
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 4 0.0000 0.8094 0.000 0.000 0.000 1.000 0.000 NA
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 4 0.0000 0.8094 0.000 0.000 0.000 1.000 0.000 NA
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 4 0.4672 0.6346 0.200 0.000 0.000 0.704 0.016 NA
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.7966 1.000 0.000 0.000 0.000 0.000 NA
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 4 0.6023 0.4556 0.316 0.000 0.000 0.536 0.056 NA
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.9270 0.000 1.000 0.000 0.000 0.000 NA
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.9270 0.000 1.000 0.000 0.000 0.000 NA
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 4 0.0820 0.8019 0.000 0.000 0.000 0.972 0.012 NA
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 3 0.1906 0.6988 0.032 0.008 0.924 0.000 0.000 NA
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.7966 1.000 0.000 0.000 0.000 0.000 NA
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.9270 0.000 1.000 0.000 0.000 0.000 NA
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.0000 0.8094 0.000 0.000 0.000 1.000 0.000 NA
#> B3561356-5A80-4C79-B23A-D518425565FE 3 0.3866 -0.0490 0.000 0.484 0.516 0.000 0.000 NA
#> F900E9BE-2400-4451-9434-EE8BC513BA94 4 0.0000 0.8094 0.000 0.000 0.000 1.000 0.000 NA
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 4 0.0000 0.8094 0.000 0.000 0.000 1.000 0.000 NA
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.7966 1.000 0.000 0.000 0.000 0.000 NA
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.5600 0.5986 0.000 0.000 0.528 0.000 0.176 NA
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.9270 0.000 1.000 0.000 0.000 0.000 NA
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0363 0.7917 0.988 0.000 0.012 0.000 0.000 NA
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.7966 1.000 0.000 0.000 0.000 0.000 NA
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.7966 1.000 0.000 0.000 0.000 0.000 NA
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.4283 0.3164 0.592 0.000 0.024 0.000 0.384 NA
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 4 0.0000 0.8094 0.000 0.000 0.000 1.000 0.000 NA
#> 12F54761-4F68-4181-8421-88EA858902FC 4 0.6023 0.4556 0.316 0.000 0.000 0.536 0.056 NA
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.0363 0.7917 0.988 0.000 0.012 0.000 0.000 NA
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.1367 0.7720 0.944 0.000 0.000 0.000 0.044 NA
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.9270 0.000 1.000 0.000 0.000 0.000 NA
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.5090 0.3217 0.592 0.000 0.024 0.000 0.336 NA
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.1983 0.6846 0.000 0.072 0.908 0.000 0.000 NA
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 17548 rows and 122 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 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 0.997 0.998 0.4961 0.505 0.505
#> 3 3 1.000 0.933 0.975 0.3046 0.670 0.443
#> 4 4 0.703 0.732 0.871 0.1411 0.788 0.488
#> 5 5 0.704 0.670 0.809 0.0749 0.869 0.559
#> 6 6 0.763 0.681 0.802 0.0421 0.932 0.683
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.000 0.998 1.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.000 0.998 1.000 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.000 0.998 1.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.000 0.998 1.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 2 0.000 0.999 0.000 1.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.000 0.999 0.000 1.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 2 0.000 0.999 0.000 1.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.000 0.998 1.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.584 0.837 0.860 0.140
#> 806616FE-1855-4284-9265-42842104CB21 1 0.000 0.998 1.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.000 0.999 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.000 0.999 0.000 1.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.000 0.998 1.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.000 0.999 0.000 1.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 1 0.000 0.998 1.000 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 2 0.000 0.999 0.000 1.000
#> 853120F0-857B-4108-9EC8-727189630C5F 1 0.000 0.998 1.000 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.000 0.998 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.000 0.999 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.000 0.999 0.000 1.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.000 0.998 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.000 0.999 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 2 0.000 0.999 0.000 1.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.000 0.998 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.000 0.999 0.000 1.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.000 0.998 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.000 0.998 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.000 0.998 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.000 0.998 1.000 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.000 0.999 0.000 1.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.000 0.999 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.000 0.998 1.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.000 0.998 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 2 0.000 0.999 0.000 1.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.000 0.998 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 1 0.000 0.998 1.000 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.000 0.998 1.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.000 0.998 1.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.000 0.999 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.000 0.998 1.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.000 0.999 0.000 1.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.000 0.998 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.000 0.998 1.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.000 0.998 1.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.000 0.999 0.000 1.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.000 0.999 0.000 1.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.000 0.999 0.000 1.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.000 0.999 0.000 1.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 2 0.000 0.999 0.000 1.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.000 0.998 1.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.000 0.999 0.000 1.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.000 0.998 1.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.000 0.998 1.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.000 0.998 1.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 2 0.000 0.999 0.000 1.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.000 0.998 1.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.000 0.999 0.000 1.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.000 0.998 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.000 0.998 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.000 0.999 0.000 1.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 2 0.000 0.999 0.000 1.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.000 0.999 0.000 1.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.000 0.999 0.000 1.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.000 0.998 1.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.000 0.999 0.000 1.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.000 0.998 1.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 2 0.000 0.999 0.000 1.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.000 0.998 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.000 0.998 1.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.000 0.998 1.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.000 0.999 0.000 1.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.000 0.999 0.000 1.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.000 0.999 0.000 1.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.000 0.999 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.000 0.998 1.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.000 0.999 0.000 1.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.000 0.998 1.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.000 0.998 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.000 0.998 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.000 0.998 1.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.000 0.998 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.000 0.998 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.000 0.998 1.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.000 0.999 0.000 1.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.000 0.999 0.000 1.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.000 0.998 1.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.000 0.998 1.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 2 0.000 0.999 0.000 1.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.000 0.999 0.000 1.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.000 0.999 0.000 1.000
#> F25A7521-2596-4D60-BABE-862023C40D40 2 0.000 0.999 0.000 1.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.000 0.998 1.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.000 0.999 0.000 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.000 0.998 1.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.000 0.998 1.000 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.000 0.998 1.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.000 0.998 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.000 0.998 1.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.000 0.999 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.000 0.999 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.000 0.998 1.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 2 0.000 0.999 0.000 1.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.000 0.998 1.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.000 0.999 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.000 0.998 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.000 0.999 0.000 1.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.000 0.998 1.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.000 0.998 1.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.000 0.998 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.000 0.999 0.000 1.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.000 0.999 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.000 0.998 1.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.000 0.998 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.000 0.998 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.000 0.998 1.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.000 0.998 1.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.000 0.998 1.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.260 0.954 0.044 0.956
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.000 0.998 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.000 0.999 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.000 0.998 1.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.000 0.999 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 3 0.0000 0.973 0.000 0.000 1.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.0000 0.932 1.000 0.000 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.932 1.000 0.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.932 1.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.973 0.000 0.000 1.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.999 0.000 1.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0000 0.973 0.000 0.000 1.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 3 0.0000 0.973 0.000 0.000 1.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 3 0.0000 0.973 0.000 0.000 1.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0000 0.973 0.000 0.000 1.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.999 0.000 1.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.999 0.000 1.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 3 0.0000 0.973 0.000 0.000 1.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.999 0.000 1.000 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.0000 0.973 0.000 0.000 1.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0000 0.973 0.000 0.000 1.000
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.0000 0.973 0.000 0.000 1.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 3 0.0000 0.973 0.000 0.000 1.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.999 0.000 1.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.999 0.000 1.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 3 0.0000 0.973 0.000 0.000 1.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.999 0.000 1.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.0000 0.973 0.000 0.000 1.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.932 1.000 0.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.999 0.000 1.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 3 0.0000 0.973 0.000 0.000 1.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 3 0.0000 0.973 0.000 0.000 1.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.6260 0.274 0.552 0.000 0.448
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.0000 0.973 0.000 0.000 1.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 3 0.0000 0.973 0.000 0.000 1.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.999 0.000 1.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 3 0.0000 0.973 0.000 0.000 1.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.932 1.000 0.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.0000 0.973 0.000 0.000 1.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 3 0.0000 0.973 0.000 0.000 1.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.0000 0.973 0.000 0.000 1.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 3 0.0000 0.973 0.000 0.000 1.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0000 0.973 0.000 0.000 1.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.999 0.000 1.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 3 0.0000 0.973 0.000 0.000 1.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.999 0.000 1.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.932 1.000 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.932 1.000 0.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.0000 0.973 0.000 0.000 1.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.0000 0.999 0.000 1.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.0000 0.999 0.000 1.000 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 3 0.6126 0.345 0.000 0.400 0.600
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0000 0.999 0.000 1.000 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 3 0.2878 0.871 0.000 0.096 0.904
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.0000 0.973 0.000 0.000 1.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.999 0.000 1.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0000 0.973 0.000 0.000 1.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0000 0.973 0.000 0.000 1.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.932 1.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 2 0.0747 0.980 0.000 0.984 0.016
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.932 1.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.999 0.000 1.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.932 1.000 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.6252 0.285 0.556 0.000 0.444
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.0000 0.999 0.000 1.000 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0000 0.973 0.000 0.000 1.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.999 0.000 1.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.999 0.000 1.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.0000 0.932 1.000 0.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.6111 0.355 0.000 0.396 0.604
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.0000 0.973 0.000 0.000 1.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 3 0.0000 0.973 0.000 0.000 1.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 3 0.0000 0.973 0.000 0.000 1.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0000 0.932 1.000 0.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.932 1.000 0.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.999 0.000 1.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.999 0.000 1.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 3 0.0000 0.973 0.000 0.000 1.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.999 0.000 1.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 3 0.0000 0.973 0.000 0.000 1.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.999 0.000 1.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.6286 0.226 0.536 0.000 0.464
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.6260 0.274 0.552 0.000 0.448
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.932 1.000 0.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 3 0.0000 0.973 0.000 0.000 1.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.932 1.000 0.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.932 1.000 0.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 3 0.0000 0.973 0.000 0.000 1.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.999 0.000 1.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0000 0.999 0.000 1.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 3 0.0000 0.973 0.000 0.000 1.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 3 0.0000 0.973 0.000 0.000 1.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 3 0.0000 0.973 0.000 0.000 1.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.999 0.000 1.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.999 0.000 1.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.0000 0.973 0.000 0.000 1.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.932 1.000 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.999 0.000 1.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.932 1.000 0.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.0000 0.932 1.000 0.000 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.0000 0.932 1.000 0.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 3 0.0000 0.973 0.000 0.000 1.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.932 1.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.999 0.000 1.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.999 0.000 1.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0000 0.932 1.000 0.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 3 0.0000 0.973 0.000 0.000 1.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 3 0.0000 0.973 0.000 0.000 1.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.999 0.000 1.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.932 1.000 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.999 0.000 1.000 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.932 1.000 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.932 1.000 0.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 3 0.0000 0.973 0.000 0.000 1.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.5948 0.444 0.000 0.360 0.640
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.999 0.000 1.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 3 0.0000 0.973 0.000 0.000 1.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 3 0.0000 0.973 0.000 0.000 1.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 3 0.0000 0.973 0.000 0.000 1.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 3 0.0000 0.973 0.000 0.000 1.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0000 0.932 1.000 0.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.932 1.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 3 0.0000 0.973 0.000 0.000 1.000
#> FA716037-886B-4DD0-8016-686C2D24550A 3 0.0000 0.973 0.000 0.000 1.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.999 0.000 1.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.0000 0.973 0.000 0.000 1.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.0000 0.999 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.0336 0.7843 0.000 0.000 0.008 0.992
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.4985 0.2054 0.532 0.000 0.000 0.468
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.9059 1.000 0.000 0.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.9059 1.000 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.7843 0.000 0.000 1.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 3 0.4933 0.3155 0.000 0.432 0.568 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0336 0.7827 0.000 0.000 0.992 0.008
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.3764 0.6992 0.000 0.000 0.216 0.784
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.4933 0.2754 0.000 0.000 0.432 0.568
#> 806616FE-1855-4284-9265-42842104CB21 4 0.4961 0.5213 0.000 0.000 0.448 0.552
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.0000 0.7847 0.000 0.000 0.000 1.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 3 0.4134 0.6405 0.000 0.260 0.740 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.4040 0.4235 0.000 0.000 0.752 0.248
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0000 0.7843 0.000 0.000 1.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 4 0.4406 0.6396 0.000 0.000 0.300 0.700
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.3726 0.7033 0.000 0.000 0.212 0.788
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.3486 0.7252 0.000 0.000 0.188 0.812
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.0336 0.7827 0.000 0.000 0.992 0.008
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.9059 1.000 0.000 0.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0921 0.7808 0.000 0.000 0.028 0.972
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0336 0.7843 0.000 0.000 0.008 0.992
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0188 0.7839 0.004 0.000 0.000 0.996
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 4 0.4406 0.6396 0.000 0.000 0.300 0.700
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 3 0.0000 0.7843 0.000 0.000 1.000 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.0336 0.7843 0.000 0.000 0.008 0.992
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.9059 1.000 0.000 0.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.3942 0.6168 0.000 0.000 0.764 0.236
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.3356 0.7312 0.000 0.000 0.176 0.824
#> AA403EC3-FD44-4247-B06D-AEF415391E46 4 0.4605 0.6187 0.000 0.000 0.336 0.664
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.0000 0.7847 0.000 0.000 0.000 1.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 4 0.4406 0.6396 0.000 0.000 0.300 0.700
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.0000 0.7847 0.000 0.000 0.000 1.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.9059 1.000 0.000 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.9059 1.000 0.000 0.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.3486 0.7252 0.000 0.000 0.188 0.812
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.4164 0.6231 0.000 0.264 0.736 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.4406 0.5142 0.000 0.700 0.300 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 3 0.0336 0.7864 0.000 0.008 0.992 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.4454 0.4966 0.000 0.692 0.308 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 3 0.3975 0.6117 0.000 0.000 0.760 0.240
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.4981 0.4964 0.000 0.000 0.464 0.536
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 3 0.3610 0.6962 0.000 0.200 0.800 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.3356 0.7334 0.000 0.000 0.176 0.824
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 4 0.4477 0.6333 0.000 0.000 0.312 0.688
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 4 0.3975 0.5681 0.240 0.000 0.000 0.760
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.1940 0.7830 0.000 0.076 0.924 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.9059 1.000 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.9059 1.000 0.000 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0188 0.7839 0.004 0.000 0.000 0.996
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.4804 0.4279 0.000 0.384 0.616 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0817 0.7779 0.000 0.000 0.976 0.024
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 3 0.3024 0.7483 0.000 0.148 0.852 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.4994 0.1656 0.520 0.000 0.000 0.480
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0336 0.7864 0.000 0.008 0.992 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.3486 0.7252 0.000 0.000 0.188 0.812
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 3 0.4624 0.4435 0.000 0.000 0.660 0.340
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.2530 0.7567 0.000 0.000 0.112 0.888
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.4941 0.0918 0.436 0.000 0.000 0.564
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.4746 0.4540 0.632 0.000 0.000 0.368
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 3 0.1118 0.7691 0.000 0.000 0.964 0.036
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 4 0.3486 0.7252 0.000 0.000 0.188 0.812
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.0000 0.7847 0.000 0.000 0.000 1.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0188 0.7839 0.004 0.000 0.000 0.996
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.9059 1.000 0.000 0.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 4 0.3942 0.6715 0.000 0.000 0.236 0.764
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.9059 1.000 0.000 0.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.9059 1.000 0.000 0.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.0000 0.7847 0.000 0.000 0.000 1.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 3 0.4972 0.2450 0.000 0.456 0.544 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 4 0.4431 0.6367 0.000 0.000 0.304 0.696
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.0000 0.7847 0.000 0.000 0.000 1.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 3 0.4585 0.4598 0.000 0.000 0.668 0.332
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 3 0.3610 0.6962 0.000 0.200 0.800 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.0000 0.7843 0.000 0.000 1.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.9059 1.000 0.000 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.9059 1.000 0.000 0.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.0000 0.9059 1.000 0.000 0.000 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.4985 0.2054 0.532 0.000 0.000 0.468
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.3486 0.7252 0.000 0.000 0.188 0.812
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 4 0.4843 0.2233 0.396 0.000 0.000 0.604
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0000 0.9059 1.000 0.000 0.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 3 0.3942 0.6168 0.000 0.000 0.764 0.236
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.0000 0.7847 0.000 0.000 0.000 1.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.9059 1.000 0.000 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.4543 0.4579 0.000 0.676 0.324 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.9059 1.000 0.000 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.9059 1.000 0.000 0.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.3486 0.7252 0.000 0.000 0.188 0.812
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0336 0.7864 0.000 0.008 0.992 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.3764 0.6992 0.000 0.000 0.216 0.784
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0336 0.7843 0.000 0.000 0.008 0.992
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0336 0.7843 0.000 0.000 0.008 0.992
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 4 0.4477 0.6332 0.000 0.000 0.312 0.688
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0000 0.9059 1.000 0.000 0.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 4 0.4941 0.0918 0.436 0.000 0.000 0.564
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 4 0.4981 0.1692 0.000 0.000 0.464 0.536
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.7847 0.000 0.000 0.000 1.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.9547 0.000 1.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 4 0.4961 0.5213 0.000 0.000 0.448 0.552
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.4103 0.6334 0.000 0.256 0.744 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.2690 0.644 0.000 0.000 0.000 0.844 0.156
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 5 0.3106 0.470 0.116 0.000 0.020 0.008 0.856
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0162 0.939 0.996 0.000 0.000 0.000 0.004
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.2966 0.818 0.816 0.000 0.000 0.000 0.184
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.2054 0.749 0.000 0.000 0.920 0.052 0.028
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 3 0.3909 0.697 0.000 0.216 0.760 0.000 0.024
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.4428 0.505 0.000 0.000 0.700 0.032 0.268
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.0609 0.706 0.000 0.000 0.020 0.980 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.1851 0.643 0.000 0.000 0.088 0.912 0.000
#> 806616FE-1855-4284-9265-42842104CB21 5 0.6589 0.418 0.000 0.000 0.224 0.328 0.448
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.2208 0.924 0.000 0.908 0.072 0.000 0.020
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.1310 0.965 0.000 0.956 0.024 0.000 0.020
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.4114 0.427 0.000 0.000 0.000 0.624 0.376
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 3 0.2674 0.762 0.000 0.140 0.856 0.000 0.004
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.6690 0.368 0.000 0.000 0.300 0.268 0.432
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.1753 0.742 0.000 0.000 0.936 0.032 0.032
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.6219 0.473 0.000 0.000 0.212 0.240 0.548
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0404 0.711 0.000 0.000 0.012 0.988 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0290 0.712 0.000 0.000 0.008 0.992 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.3649 0.665 0.000 0.000 0.808 0.040 0.152
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.1117 0.969 0.000 0.964 0.016 0.000 0.020
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.2280 0.675 0.000 0.000 0.000 0.880 0.120
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.3707 0.558 0.000 0.000 0.000 0.716 0.284
#> 4496EE84-2C36-413B-A328-A5B598A6C387 5 0.4297 -0.130 0.000 0.000 0.000 0.472 0.528
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.6219 0.473 0.000 0.000 0.212 0.240 0.548
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 3 0.2597 0.756 0.000 0.000 0.884 0.092 0.024
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.2891 0.631 0.000 0.000 0.000 0.824 0.176
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.2966 0.818 0.816 0.000 0.000 0.000 0.184
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.4298 0.585 0.000 0.000 0.640 0.352 0.008
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0162 0.712 0.000 0.000 0.004 0.996 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.6396 0.451 0.000 0.000 0.212 0.280 0.508
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 5 0.3932 0.253 0.000 0.000 0.000 0.328 0.672
#> 50D620F3-5C52-42FB-89A1-6840A7444647 5 0.6219 0.473 0.000 0.000 0.212 0.240 0.548
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.4138 0.415 0.000 0.000 0.000 0.616 0.384
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.1117 0.969 0.000 0.964 0.016 0.000 0.020
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.0290 0.712 0.000 0.000 0.008 0.992 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.2127 0.769 0.000 0.108 0.892 0.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.4270 0.564 0.000 0.320 0.668 0.000 0.012
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 3 0.1638 0.767 0.000 0.000 0.932 0.064 0.004
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 3 0.4491 0.544 0.000 0.328 0.652 0.000 0.020
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 3 0.4576 0.518 0.000 0.000 0.608 0.376 0.016
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.3810 0.509 0.000 0.000 0.088 0.812 0.100
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 3 0.3110 0.741 0.000 0.060 0.860 0.000 0.080
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 5 0.6164 0.355 0.000 0.000 0.140 0.368 0.492
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 5 0.6491 0.454 0.000 0.000 0.228 0.284 0.488
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 5 0.4666 0.283 0.040 0.000 0.000 0.284 0.676
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.3427 0.758 0.000 0.028 0.836 0.128 0.008
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.1117 0.969 0.000 0.964 0.016 0.000 0.020
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 5 0.4256 -0.030 0.000 0.000 0.000 0.436 0.564
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.3003 0.734 0.000 0.188 0.812 0.000 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.4046 0.652 0.000 0.000 0.696 0.296 0.008
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 3 0.3959 0.763 0.000 0.068 0.816 0.104 0.012
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 5 0.4050 0.443 0.172 0.000 0.008 0.036 0.784
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.1341 0.739 0.000 0.000 0.944 0.000 0.056
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.2291 0.653 0.000 0.000 0.036 0.908 0.056
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.3728 0.435 0.000 0.000 0.244 0.748 0.008
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0794 0.706 0.000 0.000 0.000 0.972 0.028
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 5 0.5309 0.391 0.164 0.000 0.000 0.160 0.676
#> 352471DC-A881-4EA8-B646-EB1200291893 5 0.5905 0.315 0.276 0.000 0.000 0.144 0.580
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.1310 0.965 0.000 0.956 0.024 0.000 0.020
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.2012 0.936 0.000 0.920 0.060 0.000 0.020
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 3 0.4731 0.382 0.000 0.000 0.640 0.032 0.328
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 4 0.0162 0.712 0.000 0.000 0.004 0.996 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 5 0.4291 -0.108 0.000 0.000 0.000 0.464 0.536
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 5 0.4256 -0.030 0.000 0.000 0.000 0.436 0.564
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 5 0.4132 0.429 0.000 0.000 0.020 0.260 0.720
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.2966 0.818 0.816 0.000 0.000 0.000 0.184
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.3999 0.471 0.000 0.000 0.000 0.656 0.344
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.2208 0.924 0.000 0.908 0.072 0.000 0.020
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 3 0.4229 0.703 0.000 0.208 0.756 0.012 0.024
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 5 0.6388 0.458 0.000 0.000 0.208 0.284 0.508
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.4138 0.415 0.000 0.000 0.000 0.616 0.384
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.4538 -0.169 0.000 0.000 0.452 0.540 0.008
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.1117 0.969 0.000 0.964 0.016 0.000 0.020
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 3 0.3110 0.741 0.000 0.060 0.860 0.000 0.080
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.4163 0.696 0.000 0.000 0.740 0.228 0.032
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.2471 0.857 0.864 0.000 0.000 0.000 0.136
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 5 0.4210 0.406 0.204 0.000 0.004 0.036 0.756
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0290 0.712 0.000 0.000 0.008 0.992 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 5 0.5307 0.389 0.156 0.000 0.000 0.168 0.676
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.3966 0.601 0.664 0.000 0.000 0.000 0.336
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 3 0.4283 0.590 0.000 0.000 0.644 0.348 0.008
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.4114 0.427 0.000 0.000 0.000 0.624 0.376
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 3 0.4654 0.514 0.000 0.348 0.628 0.000 0.024
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0290 0.712 0.000 0.000 0.008 0.992 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.1341 0.739 0.000 0.000 0.944 0.000 0.056
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.0510 0.708 0.000 0.000 0.016 0.984 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.3586 0.578 0.000 0.000 0.000 0.736 0.264
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.3586 0.578 0.000 0.000 0.000 0.736 0.264
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 5 0.6333 0.456 0.000 0.000 0.196 0.288 0.516
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 5 0.5309 0.391 0.164 0.000 0.000 0.160 0.676
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 4 0.1851 0.643 0.000 0.000 0.088 0.912 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.4138 0.415 0.000 0.000 0.000 0.616 0.384
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.979 0.000 1.000 0.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 5 0.6631 0.383 0.000 0.000 0.224 0.356 0.420
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.2233 0.770 0.000 0.104 0.892 0.000 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.3991 0.472 0.240 0.000 0.008 0.724 0.028 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.5097 0.392 0.604 0.000 0.008 0.000 0.304 0.084
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 6 0.0622 0.891 0.000 0.000 0.008 0.000 0.012 0.980
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 6 0.3636 0.624 0.320 0.000 0.000 0.000 0.004 0.676
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.3232 0.710 0.020 0.000 0.812 0.008 0.160 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 3 0.4070 0.750 0.120 0.076 0.784 0.004 0.016 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 5 0.3705 0.599 0.020 0.000 0.236 0.004 0.740 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.0551 0.729 0.004 0.000 0.004 0.984 0.008 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.1232 0.720 0.004 0.000 0.016 0.956 0.024 0.000
#> 806616FE-1855-4284-9265-42842104CB21 5 0.4716 0.798 0.056 0.000 0.060 0.152 0.732 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.5128 0.646 0.104 0.660 0.216 0.000 0.020 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.3591 0.844 0.104 0.816 0.064 0.000 0.016 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.4184 0.179 0.500 0.000 0.000 0.488 0.012 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 3 0.3515 0.772 0.092 0.048 0.832 0.004 0.024 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.3208 0.791 0.012 0.000 0.068 0.076 0.844 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.3470 0.695 0.020 0.000 0.792 0.012 0.176 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.3722 0.802 0.068 0.000 0.032 0.084 0.816 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0405 0.732 0.004 0.000 0.000 0.988 0.008 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0777 0.920 0.024 0.972 0.000 0.000 0.004 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0291 0.732 0.004 0.000 0.000 0.992 0.004 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.4468 -0.197 0.020 0.000 0.488 0.004 0.488 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 6 0.0000 0.898 0.000 0.000 0.000 0.000 0.000 1.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.2133 0.901 0.052 0.912 0.020 0.000 0.016 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.2312 0.658 0.112 0.000 0.000 0.876 0.012 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.3967 0.268 0.356 0.000 0.000 0.632 0.012 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.2941 0.678 0.780 0.000 0.000 0.220 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.3722 0.802 0.068 0.000 0.032 0.084 0.816 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 3 0.3557 0.740 0.032 0.000 0.824 0.044 0.100 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.3629 0.460 0.260 0.000 0.000 0.724 0.016 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 6 0.3547 0.650 0.300 0.000 0.000 0.000 0.004 0.696
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.5451 0.222 0.020 0.000 0.472 0.440 0.068 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0291 0.732 0.004 0.000 0.000 0.992 0.004 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.3411 0.806 0.044 0.000 0.032 0.088 0.836 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.3501 0.723 0.804 0.000 0.000 0.116 0.080 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 5 0.3545 0.800 0.060 0.000 0.044 0.064 0.832 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.4177 0.241 0.520 0.000 0.000 0.468 0.012 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.3124 0.867 0.096 0.848 0.040 0.000 0.016 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 6 0.0000 0.898 0.000 0.000 0.000 0.000 0.000 1.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 6 0.0146 0.898 0.000 0.000 0.000 0.000 0.004 0.996
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.0405 0.732 0.004 0.000 0.000 0.988 0.008 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.2183 0.773 0.028 0.020 0.912 0.000 0.040 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.2858 0.771 0.028 0.092 0.864 0.000 0.016 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 3 0.3402 0.779 0.088 0.000 0.836 0.028 0.048 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 3 0.4421 0.727 0.104 0.112 0.756 0.000 0.028 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 4 0.5580 -0.214 0.048 0.000 0.440 0.468 0.044 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.3688 0.544 0.008 0.000 0.028 0.768 0.196 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 3 0.4203 0.757 0.096 0.040 0.788 0.004 0.072 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 5 0.4622 0.765 0.092 0.000 0.020 0.164 0.724 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 5 0.4500 0.801 0.076 0.000 0.040 0.132 0.752 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.3355 0.729 0.828 0.000 0.000 0.100 0.064 0.008
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.2675 0.768 0.020 0.004 0.888 0.052 0.036 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 6 0.0146 0.897 0.000 0.000 0.000 0.000 0.004 0.996
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.2039 0.903 0.052 0.916 0.020 0.000 0.012 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 6 0.0000 0.898 0.000 0.000 0.000 0.000 0.000 1.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.2882 0.714 0.812 0.000 0.000 0.180 0.008 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.2487 0.784 0.024 0.064 0.892 0.000 0.020 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.5633 0.313 0.028 0.000 0.500 0.396 0.076 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0291 0.924 0.004 0.992 0.000 0.000 0.004 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 3 0.3978 0.769 0.104 0.020 0.808 0.044 0.024 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.5017 0.522 0.660 0.000 0.016 0.000 0.232 0.092
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.3406 0.695 0.020 0.000 0.792 0.008 0.180 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.3381 0.571 0.008 0.000 0.008 0.772 0.212 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.3117 0.651 0.016 0.000 0.080 0.852 0.052 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0725 0.727 0.012 0.000 0.000 0.976 0.012 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.3603 0.712 0.828 0.000 0.000 0.044 0.064 0.064
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.3483 0.702 0.828 0.000 0.000 0.044 0.028 0.100
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.3647 0.841 0.104 0.812 0.068 0.000 0.016 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.4425 0.772 0.104 0.744 0.136 0.000 0.016 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 5 0.4082 0.627 0.056 0.000 0.188 0.008 0.748 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 4 0.0547 0.730 0.000 0.000 0.000 0.980 0.020 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0291 0.925 0.004 0.992 0.000 0.000 0.004 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.2730 0.705 0.808 0.000 0.000 0.192 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.2882 0.714 0.812 0.000 0.000 0.180 0.008 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 6 0.0146 0.898 0.000 0.000 0.000 0.000 0.004 0.996
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 5 0.5310 0.353 0.348 0.000 0.000 0.116 0.536 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 6 0.0146 0.897 0.000 0.000 0.000 0.000 0.004 0.996
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 6 0.3728 0.587 0.344 0.000 0.000 0.000 0.004 0.652
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.4726 -0.047 0.424 0.000 0.008 0.536 0.032 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.5241 0.624 0.108 0.644 0.228 0.000 0.020 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 3 0.4030 0.762 0.104 0.064 0.800 0.016 0.016 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 5 0.4672 0.784 0.112 0.000 0.024 0.136 0.728 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.4177 0.241 0.520 0.000 0.000 0.468 0.012 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.4462 0.459 0.016 0.000 0.224 0.708 0.052 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.2039 0.903 0.052 0.916 0.020 0.000 0.012 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 3 0.4203 0.757 0.096 0.040 0.788 0.004 0.072 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.6051 0.368 0.032 0.000 0.496 0.348 0.124 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 6 0.0000 0.898 0.000 0.000 0.000 0.000 0.000 1.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 6 0.0146 0.898 0.000 0.000 0.000 0.000 0.004 0.996
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 6 0.4139 0.712 0.212 0.000 0.008 0.000 0.048 0.732
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.5037 0.524 0.660 0.000 0.016 0.000 0.228 0.096
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0508 0.730 0.004 0.000 0.000 0.984 0.012 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.3613 0.717 0.828 0.000 0.000 0.052 0.064 0.056
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 6 0.5212 0.252 0.440 0.000 0.008 0.000 0.068 0.484
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 4 0.5528 -0.227 0.040 0.000 0.448 0.464 0.048 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.4700 -0.229 0.476 0.000 0.008 0.488 0.028 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 6 0.0000 0.898 0.000 0.000 0.000 0.000 0.000 1.000
#> B3561356-5A80-4C79-B23A-D518425565FE 3 0.4042 0.744 0.100 0.096 0.784 0.000 0.020 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 6 0.0146 0.898 0.000 0.000 0.000 0.000 0.004 0.996
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 6 0.0146 0.898 0.000 0.000 0.000 0.000 0.004 0.996
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0146 0.732 0.004 0.000 0.000 0.996 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.3406 0.695 0.020 0.000 0.792 0.008 0.180 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.0260 0.731 0.000 0.000 0.000 0.992 0.008 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.4380 0.337 0.312 0.000 0.012 0.652 0.024 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.4380 0.337 0.312 0.000 0.012 0.652 0.024 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 5 0.4635 0.781 0.116 0.000 0.020 0.136 0.728 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 6 0.0146 0.898 0.000 0.000 0.000 0.000 0.004 0.996
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.3610 0.715 0.828 0.000 0.000 0.048 0.064 0.060
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 4 0.1232 0.720 0.004 0.000 0.016 0.956 0.024 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.4177 0.241 0.520 0.000 0.000 0.468 0.012 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 5 0.4407 0.778 0.020 0.000 0.060 0.188 0.732 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.1749 0.783 0.008 0.024 0.932 0.000 0.036 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", "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 17548 rows and 122 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 0.978 0.992 0.5011 0.499 0.499
#> 3 3 0.935 0.928 0.969 0.2894 0.838 0.684
#> 4 4 0.949 0.915 0.960 0.0921 0.925 0.797
#> 5 5 0.874 0.839 0.913 0.0677 0.909 0.713
#> 6 6 0.842 0.748 0.875 0.0298 0.975 0.900
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.993 1.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.0000 0.993 1.000 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.993 1.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.993 1.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 2 0.0000 0.990 0.000 1.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.990 0.000 1.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 2 0.0000 0.990 0.000 1.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 2 0.4022 0.906 0.080 0.920
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 2 0.0000 0.990 0.000 1.000
#> 806616FE-1855-4284-9265-42842104CB21 1 0.0000 0.993 1.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.990 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.990 0.000 1.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.993 1.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.990 0.000 1.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 1 0.9881 0.217 0.564 0.436
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 2 0.0000 0.990 0.000 1.000
#> 853120F0-857B-4108-9EC8-727189630C5F 1 0.0000 0.993 1.000 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0672 0.985 0.992 0.008
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.990 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.990 0.000 1.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.993 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.990 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 2 0.0000 0.990 0.000 1.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.993 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.990 0.000 1.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.993 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.993 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.993 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.0000 0.993 1.000 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.0000 0.990 0.000 1.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.990 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.993 1.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.993 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 2 0.0000 0.990 0.000 1.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.993 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 1 0.0000 0.993 1.000 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0000 0.993 1.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.0000 0.993 1.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.990 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.993 1.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.990 0.000 1.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.993 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.993 1.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.993 1.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.0000 0.990 0.000 1.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.0000 0.990 0.000 1.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.990 0.000 1.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0000 0.990 0.000 1.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 2 0.0000 0.990 0.000 1.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.0000 0.993 1.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.990 0.000 1.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.0000 0.993 1.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.0000 0.993 1.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.993 1.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 2 0.0000 0.990 0.000 1.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.993 1.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.990 0.000 1.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.993 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.993 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.0000 0.990 0.000 1.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 2 0.0000 0.990 0.000 1.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.990 0.000 1.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.990 0.000 1.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.0000 0.993 1.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.0000 0.990 0.000 1.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.0000 0.993 1.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 2 0.0000 0.990 0.000 1.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.993 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0000 0.993 1.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.993 1.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.990 0.000 1.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.990 0.000 1.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.990 0.000 1.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.990 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.993 1.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.990 0.000 1.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.993 1.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.993 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.993 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.0000 0.993 1.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.993 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.993 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.993 1.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.990 0.000 1.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0000 0.990 0.000 1.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.0000 0.993 1.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.993 1.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 2 0.0000 0.990 0.000 1.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.990 0.000 1.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.990 0.000 1.000
#> F25A7521-2596-4D60-BABE-862023C40D40 2 0.0000 0.990 0.000 1.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.993 1.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.990 0.000 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.993 1.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.0000 0.993 1.000 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.0000 0.993 1.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.993 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.993 1.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.990 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.990 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0000 0.993 1.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 2 0.0000 0.990 0.000 1.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.993 1.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.990 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.993 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.990 0.000 1.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.993 1.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.993 1.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.993 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.0000 0.990 0.000 1.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.990 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 2 0.9909 0.195 0.444 0.556
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.993 1.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.993 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.0000 0.993 1.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0000 0.993 1.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.993 1.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.0000 0.990 0.000 1.000
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.993 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.990 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.0000 0.993 1.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.0000 0.990 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 3 0.0000 0.987 0.000 0.000 1.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.0000 0.946 1.000 0.000 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.946 1.000 0.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.946 1.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 2 0.0000 0.978 0.000 1.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.978 0.000 1.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 2 0.0000 0.978 0.000 1.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 3 0.0000 0.987 0.000 0.000 1.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 3 0.0000 0.987 0.000 0.000 1.000
#> 806616FE-1855-4284-9265-42842104CB21 1 0.0000 0.946 1.000 0.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.978 0.000 1.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.978 0.000 1.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.5733 0.566 0.676 0.000 0.324
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.978 0.000 1.000 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 1 0.2448 0.865 0.924 0.076 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 2 0.0000 0.978 0.000 1.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 1 0.0000 0.946 1.000 0.000 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 3 0.0000 0.987 0.000 0.000 1.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.978 0.000 1.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.978 0.000 1.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 3 0.0000 0.987 0.000 0.000 1.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.978 0.000 1.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 2 0.0000 0.978 0.000 1.000 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.946 1.000 0.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.978 0.000 1.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 3 0.0000 0.987 0.000 0.000 1.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 3 0.0000 0.987 0.000 0.000 1.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.5733 0.566 0.676 0.000 0.324
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.0000 0.946 1.000 0.000 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.0000 0.978 0.000 1.000 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.978 0.000 1.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 3 0.0000 0.987 0.000 0.000 1.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.946 1.000 0.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 2 0.5968 0.436 0.000 0.636 0.364
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 3 0.0000 0.987 0.000 0.000 1.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 1 0.0000 0.946 1.000 0.000 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0000 0.946 1.000 0.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.0000 0.946 1.000 0.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.978 0.000 1.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.5733 0.566 0.676 0.000 0.324
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.978 0.000 1.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.946 1.000 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.946 1.000 0.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.0000 0.987 0.000 0.000 1.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.0000 0.978 0.000 1.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.0000 0.978 0.000 1.000 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.978 0.000 1.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0000 0.978 0.000 1.000 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 2 0.6280 0.168 0.000 0.540 0.460
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.0000 0.987 0.000 0.000 1.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.978 0.000 1.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.0000 0.946 1.000 0.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.0000 0.946 1.000 0.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.946 1.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 2 0.0000 0.978 0.000 1.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.946 1.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.978 0.000 1.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.946 1.000 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.946 1.000 0.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.0000 0.978 0.000 1.000 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 2 0.2625 0.899 0.000 0.916 0.084
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.978 0.000 1.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.978 0.000 1.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.0000 0.946 1.000 0.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.0000 0.978 0.000 1.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.4555 0.727 0.200 0.000 0.800
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 3 0.0000 0.987 0.000 0.000 1.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 3 0.0000 0.987 0.000 0.000 1.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0000 0.946 1.000 0.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.946 1.000 0.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.978 0.000 1.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.978 0.000 1.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.978 0.000 1.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.978 0.000 1.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 3 0.0000 0.987 0.000 0.000 1.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.978 0.000 1.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.946 1.000 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.946 1.000 0.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.946 1.000 0.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.0000 0.946 1.000 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.946 1.000 0.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.946 1.000 0.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.6045 0.449 0.620 0.000 0.380
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.978 0.000 1.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0000 0.978 0.000 1.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.0000 0.946 1.000 0.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.5733 0.566 0.676 0.000 0.324
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 3 0.0000 0.987 0.000 0.000 1.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.978 0.000 1.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.978 0.000 1.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 2 0.1643 0.939 0.000 0.956 0.044
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.946 1.000 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.978 0.000 1.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.946 1.000 0.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.0000 0.946 1.000 0.000 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.0000 0.946 1.000 0.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 3 0.0000 0.987 0.000 0.000 1.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.946 1.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.978 0.000 1.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.978 0.000 1.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0000 0.946 1.000 0.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 2 0.2625 0.899 0.000 0.916 0.084
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.5733 0.566 0.676 0.000 0.324
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.978 0.000 1.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.946 1.000 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.978 0.000 1.000 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.946 1.000 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.946 1.000 0.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 3 0.0000 0.987 0.000 0.000 1.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.0000 0.978 0.000 1.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.978 0.000 1.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 3 0.0000 0.987 0.000 0.000 1.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 3 0.0592 0.977 0.012 0.000 0.988
#> F205F9FC-F2D5-4164-9A40-1279647F900B 3 0.0592 0.977 0.012 0.000 0.988
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.0000 0.946 1.000 0.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0000 0.946 1.000 0.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.946 1.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 3 0.1163 0.960 0.000 0.028 0.972
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.5733 0.566 0.676 0.000 0.324
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.978 0.000 1.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.1753 0.905 0.952 0.000 0.048
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.0000 0.978 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.2521 0.881 0.064 0.000 0.024 0.912
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.0188 0.987 0.996 0.000 0.004 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 2 0.3219 0.799 0.000 0.836 0.164 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.1022 0.922 0.000 0.032 0.968 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.0336 0.928 0.000 0.000 0.008 0.992
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.0336 0.928 0.000 0.000 0.008 0.992
#> 806616FE-1855-4284-9265-42842104CB21 3 0.1022 0.958 0.032 0.000 0.968 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.1629 0.957 0.952 0.000 0.024 0.024
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.1022 0.958 0.032 0.000 0.968 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 2 0.4624 0.546 0.000 0.660 0.340 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.1389 0.948 0.048 0.000 0.952 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0336 0.928 0.000 0.000 0.008 0.992
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.929 0.000 0.000 0.000 1.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 2 0.4356 0.632 0.000 0.708 0.292 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0817 0.922 0.000 0.000 0.024 0.976
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.2670 0.874 0.072 0.000 0.024 0.904
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.1629 0.957 0.952 0.000 0.024 0.024
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.1389 0.948 0.048 0.000 0.952 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.2813 0.867 0.080 0.000 0.024 0.896
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 2 0.5250 0.239 0.000 0.552 0.008 0.440
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.929 0.000 0.000 0.000 1.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.1022 0.958 0.032 0.000 0.968 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.1022 0.958 0.032 0.000 0.968 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.1629 0.957 0.952 0.000 0.024 0.024
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.0000 0.929 0.000 0.000 0.000 1.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.0592 0.928 0.000 0.984 0.016 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 4 0.5138 0.282 0.000 0.392 0.008 0.600
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.1637 0.916 0.000 0.000 0.940 0.060
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.1211 0.954 0.040 0.000 0.960 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.1022 0.958 0.032 0.000 0.968 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0469 0.983 0.988 0.000 0.012 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 2 0.5523 0.381 0.000 0.596 0.024 0.380
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.3074 0.811 0.000 0.848 0.152 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.4008 0.634 0.000 0.000 0.756 0.244
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0336 0.928 0.000 0.000 0.008 0.992
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0592 0.926 0.000 0.000 0.016 0.984
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0188 0.937 0.000 0.996 0.004 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 4 0.0817 0.922 0.000 0.000 0.024 0.976
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0817 0.975 0.976 0.000 0.024 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0469 0.983 0.988 0.000 0.012 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.0188 0.987 0.996 0.000 0.004 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.1733 0.954 0.948 0.000 0.024 0.028
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.0188 0.987 0.996 0.000 0.004 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.1629 0.957 0.952 0.000 0.024 0.024
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0336 0.928 0.000 0.000 0.008 0.992
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 2 0.5364 0.543 0.000 0.652 0.320 0.028
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0592 0.926 0.000 0.000 0.016 0.984
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 2 0.5150 0.361 0.000 0.596 0.008 0.396
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.1629 0.957 0.952 0.000 0.024 0.024
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.929 0.000 0.000 0.000 1.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.3123 0.807 0.000 0.844 0.156 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.0336 0.928 0.000 0.000 0.008 0.992
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.3205 0.839 0.104 0.000 0.024 0.872
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.3205 0.839 0.104 0.000 0.024 0.872
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.0188 0.987 0.996 0.000 0.004 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 4 0.0524 0.926 0.000 0.004 0.008 0.988
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.1629 0.957 0.952 0.000 0.024 0.024
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.1174 0.950 0.020 0.000 0.968 0.012
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.0000 0.940 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.0451 0.689 0.008 0.000 0.000 0.988 0.004
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.1943 0.911 0.924 0.000 0.056 0.000 0.020
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 2 0.4221 0.761 0.000 0.780 0.112 0.000 0.108
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.2286 0.852 0.000 0.004 0.888 0.000 0.108
#> 45EAD449-C59A-463E-880A-C375CDD039BA 5 0.2852 0.823 0.000 0.000 0.000 0.172 0.828
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 5 0.2852 0.823 0.000 0.000 0.000 0.172 0.828
#> 806616FE-1855-4284-9265-42842104CB21 3 0.1792 0.864 0.000 0.000 0.916 0.000 0.084
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.3612 0.689 0.268 0.000 0.000 0.732 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.0794 0.875 0.000 0.000 0.972 0.000 0.028
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 2 0.5426 0.533 0.000 0.640 0.252 0.000 0.108
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.1300 0.870 0.016 0.000 0.956 0.000 0.028
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 5 0.3305 0.800 0.000 0.000 0.000 0.224 0.776
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 5 0.4302 0.478 0.000 0.000 0.000 0.480 0.520
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 2 0.5370 0.392 0.000 0.584 0.348 0.000 0.068
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0290 0.678 0.000 0.000 0.000 0.992 0.008
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0290 0.689 0.008 0.000 0.000 0.992 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.3684 0.682 0.280 0.000 0.000 0.720 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.1493 0.864 0.024 0.000 0.948 0.000 0.028
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.0290 0.689 0.008 0.000 0.000 0.992 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 5 0.3346 0.787 0.000 0.064 0.000 0.092 0.844
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.4300 -0.441 0.000 0.000 0.000 0.524 0.476
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.0794 0.875 0.000 0.000 0.972 0.000 0.028
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.3707 0.678 0.284 0.000 0.000 0.716 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 5 0.4283 0.528 0.000 0.000 0.000 0.456 0.544
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.2124 0.888 0.000 0.900 0.004 0.000 0.096
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.0404 0.954 0.000 0.988 0.000 0.000 0.012
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 5 0.3641 0.799 0.000 0.060 0.000 0.120 0.820
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.4310 0.468 0.000 0.000 0.604 0.004 0.392
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0510 0.951 0.000 0.984 0.000 0.000 0.016
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.2079 0.843 0.064 0.000 0.916 0.000 0.020
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0794 0.877 0.000 0.000 0.972 0.000 0.028
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 2 0.1270 0.927 0.000 0.948 0.000 0.000 0.052
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.2813 0.763 0.832 0.000 0.000 0.168 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.0404 0.954 0.000 0.988 0.000 0.000 0.012
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 5 0.2177 0.694 0.000 0.080 0.008 0.004 0.908
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.3866 0.794 0.000 0.808 0.096 0.000 0.096
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.5359 0.294 0.000 0.000 0.532 0.412 0.056
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 5 0.2852 0.823 0.000 0.000 0.000 0.172 0.828
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.2852 0.475 0.000 0.000 0.000 0.828 0.172
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.2451 0.888 0.000 0.904 0.056 0.004 0.036
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 4 0.2074 0.585 0.000 0.000 0.000 0.896 0.104
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.4294 0.285 0.468 0.000 0.000 0.532 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.2732 0.776 0.840 0.000 0.000 0.160 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.2103 0.908 0.920 0.000 0.056 0.004 0.020
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.2891 0.707 0.176 0.000 0.000 0.824 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.2451 0.893 0.904 0.000 0.056 0.004 0.036
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.3684 0.682 0.280 0.000 0.000 0.720 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 5 0.2852 0.823 0.000 0.000 0.000 0.172 0.828
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0510 0.951 0.000 0.984 0.000 0.000 0.016
#> F25A7521-2596-4D60-BABE-862023C40D40 5 0.5260 0.287 0.000 0.348 0.060 0.000 0.592
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.1608 0.915 0.928 0.000 0.072 0.000 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.0162 0.970 0.996 0.000 0.000 0.000 0.004
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.2377 0.550 0.000 0.000 0.000 0.872 0.128
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 5 0.3868 0.725 0.000 0.140 0.000 0.060 0.800
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.3366 0.701 0.232 0.000 0.000 0.768 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 5 0.4030 0.686 0.000 0.000 0.000 0.352 0.648
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.4123 0.771 0.000 0.788 0.108 0.000 0.104
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 5 0.2929 0.820 0.000 0.000 0.000 0.180 0.820
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0609 0.696 0.020 0.000 0.000 0.980 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0703 0.697 0.024 0.000 0.000 0.976 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.2451 0.893 0.904 0.000 0.056 0.004 0.036
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.973 1.000 0.000 0.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 5 0.2852 0.823 0.000 0.000 0.000 0.172 0.828
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.3661 0.685 0.276 0.000 0.000 0.724 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.1792 0.864 0.000 0.000 0.916 0.000 0.084
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.0404 0.954 0.000 0.988 0.000 0.000 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.2170 0.7074 0.888 0.000 0.000 0.100 0.012 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 6 0.4141 0.7063 0.092 0.000 0.000 0.000 0.168 0.740
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 2 0.6017 -0.1588 0.004 0.424 0.368 0.000 0.204 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.2964 0.3470 0.004 0.000 0.792 0.000 0.204 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.0000 0.8390 0.000 0.000 0.000 1.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.0000 0.8390 0.000 0.000 0.000 1.000 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.1285 0.3481 0.000 0.000 0.944 0.000 0.052 0.004
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.3240 0.6904 0.752 0.000 0.000 0.000 0.004 0.244
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.3869 0.7673 0.000 0.000 0.500 0.000 0.500 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.6022 0.1506 0.004 0.376 0.416 0.000 0.204 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.4264 0.7741 0.000 0.000 0.484 0.000 0.500 0.016
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.2066 0.8120 0.072 0.000 0.000 0.904 0.024 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.4028 0.5881 0.308 0.000 0.000 0.668 0.024 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.5170 0.2361 0.000 0.204 0.176 0.000 0.620 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.2432 0.6971 0.876 0.000 0.000 0.100 0.024 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.2170 0.7025 0.888 0.000 0.000 0.100 0.012 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.3288 0.6713 0.724 0.000 0.000 0.000 0.000 0.276
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.4264 0.7741 0.000 0.000 0.484 0.000 0.500 0.016
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.1958 0.7067 0.896 0.000 0.000 0.100 0.004 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.1036 0.8173 0.000 0.024 0.004 0.964 0.008 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.4167 0.5185 0.344 0.000 0.000 0.632 0.024 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.3999 0.7730 0.000 0.000 0.496 0.000 0.500 0.004
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.2482 -0.0202 0.004 0.000 0.848 0.000 0.148 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.3309 0.6669 0.720 0.000 0.000 0.000 0.000 0.280
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.3956 0.6130 0.292 0.000 0.000 0.684 0.024 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.5388 0.3945 0.004 0.604 0.192 0.000 0.200 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.1075 0.8939 0.000 0.952 0.000 0.000 0.048 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 4 0.0547 0.8279 0.000 0.020 0.000 0.980 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.4640 0.3482 0.020 0.000 0.728 0.128 0.124 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.1124 0.8968 0.008 0.956 0.000 0.000 0.036 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.2669 0.1293 0.000 0.000 0.836 0.000 0.008 0.156
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.4064 0.1776 0.068 0.000 0.784 0.000 0.120 0.028
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 2 0.3270 0.7560 0.000 0.820 0.060 0.000 0.120 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 6 0.2300 0.7788 0.144 0.000 0.000 0.000 0.000 0.856
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.1075 0.8941 0.000 0.952 0.000 0.000 0.048 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.5996 0.3399 0.004 0.044 0.152 0.600 0.200 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.5530 0.3403 0.004 0.580 0.216 0.000 0.200 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.4968 0.1350 0.328 0.000 0.604 0.052 0.016 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0000 0.8390 0.000 0.000 0.000 1.000 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.4252 0.2350 0.604 0.000 0.000 0.372 0.024 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> 352471DC-A881-4EA8-B646-EB1200291893 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.4922 0.4210 0.096 0.616 0.000 0.000 0.288 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.3636 0.4131 0.676 0.000 0.000 0.320 0.004 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.3857 0.2756 0.532 0.000 0.000 0.000 0.000 0.468
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 6 0.2178 0.7955 0.132 0.000 0.000 0.000 0.000 0.868
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 6 0.4887 0.5579 0.096 0.000 0.000 0.000 0.280 0.624
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.2313 0.7249 0.884 0.000 0.000 0.004 0.012 0.100
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 6 0.5073 0.5330 0.096 0.000 0.004 0.000 0.292 0.608
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.3266 0.6750 0.728 0.000 0.000 0.000 0.000 0.272
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.8390 0.000 0.000 0.000 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.1124 0.8968 0.008 0.956 0.000 0.000 0.036 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.7634 0.1995 0.004 0.252 0.364 0.180 0.200 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 6 0.1501 0.8798 0.000 0.000 0.000 0.000 0.076 0.924
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 6 0.1265 0.9013 0.044 0.000 0.000 0.000 0.008 0.948
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.3743 0.5126 0.724 0.000 0.000 0.252 0.024 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 4 0.1765 0.7415 0.000 0.096 0.000 0.904 0.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.3014 0.7106 0.804 0.000 0.000 0.000 0.012 0.184
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.3394 0.7223 0.200 0.000 0.000 0.776 0.024 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.5853 0.1214 0.004 0.504 0.292 0.000 0.200 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.0547 0.8357 0.020 0.000 0.000 0.980 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.2274 0.7155 0.892 0.000 0.000 0.088 0.008 0.012
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.2274 0.7155 0.892 0.000 0.000 0.088 0.008 0.012
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 6 0.4939 0.5398 0.096 0.000 0.000 0.000 0.292 0.612
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> 12F54761-4F68-4181-8421-88EA858902FC 6 0.0000 0.9394 0.000 0.000 0.000 0.000 0.000 1.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 4 0.0146 0.8378 0.000 0.004 0.000 0.996 0.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.3221 0.6809 0.736 0.000 0.000 0.000 0.000 0.264
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.0146 0.2951 0.000 0.000 0.996 0.000 0.004 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.1444 0.8766 0.000 0.928 0.000 0.000 0.072 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", "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 17548 rows and 122 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 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 0.978 0.990 0.4707 0.531 0.531
#> 3 3 0.816 0.891 0.954 0.3568 0.777 0.601
#> 4 4 0.810 0.856 0.928 0.1567 0.798 0.510
#> 5 5 0.779 0.680 0.819 0.0641 0.971 0.886
#> 6 6 0.782 0.679 0.756 0.0453 0.894 0.593
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0376 0.990 0.996 0.004
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.0000 0.989 1.000 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.989 1.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.989 1.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 2 0.0000 0.991 0.000 1.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.991 0.000 1.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 1 0.9491 0.428 0.632 0.368
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0376 0.990 0.996 0.004
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0376 0.990 0.996 0.004
#> 806616FE-1855-4284-9265-42842104CB21 1 0.0376 0.990 0.996 0.004
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.991 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.991 0.000 1.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0376 0.990 0.996 0.004
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.991 0.000 1.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 1 0.0376 0.990 0.996 0.004
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 2 0.0000 0.991 0.000 1.000
#> 853120F0-857B-4108-9EC8-727189630C5F 1 0.0376 0.990 0.996 0.004
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0376 0.990 0.996 0.004
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.991 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.991 0.000 1.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0376 0.990 0.996 0.004
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.991 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 1 0.0376 0.990 0.996 0.004
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.989 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.991 0.000 1.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0376 0.990 0.996 0.004
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0376 0.990 0.996 0.004
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.989 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.0376 0.990 0.996 0.004
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.0376 0.990 0.996 0.004
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.991 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0376 0.990 0.996 0.004
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.989 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 2 0.7219 0.751 0.200 0.800
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0376 0.990 0.996 0.004
#> AA403EC3-FD44-4247-B06D-AEF415391E46 1 0.0376 0.990 0.996 0.004
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0376 0.990 0.996 0.004
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.0376 0.990 0.996 0.004
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.991 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0376 0.990 0.996 0.004
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.991 0.000 1.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.989 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.989 1.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0376 0.990 0.996 0.004
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.0000 0.991 0.000 1.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.0000 0.991 0.000 1.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.991 0.000 1.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0000 0.991 0.000 1.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 2 0.0000 0.991 0.000 1.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.0376 0.990 0.996 0.004
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.991 0.000 1.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.0376 0.990 0.996 0.004
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.0376 0.990 0.996 0.004
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.989 1.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 2 0.0000 0.991 0.000 1.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.989 1.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.991 0.000 1.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.989 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.989 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.0000 0.991 0.000 1.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 2 0.7219 0.751 0.200 0.800
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.991 0.000 1.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.991 0.000 1.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.0000 0.989 1.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.0000 0.991 0.000 1.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.0376 0.990 0.996 0.004
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0376 0.990 0.996 0.004
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0376 0.990 0.996 0.004
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0000 0.989 1.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.989 1.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.991 0.000 1.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.991 0.000 1.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 1 0.8443 0.632 0.728 0.272
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.991 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0376 0.990 0.996 0.004
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.991 0.000 1.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0376 0.990 0.996 0.004
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.989 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.989 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.0376 0.990 0.996 0.004
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.989 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.989 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0376 0.990 0.996 0.004
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.991 0.000 1.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0000 0.991 0.000 1.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.0376 0.990 0.996 0.004
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0376 0.990 0.996 0.004
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.1633 0.971 0.976 0.024
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.991 0.000 1.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.991 0.000 1.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.0672 0.987 0.992 0.008
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.989 1.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.991 0.000 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.989 1.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.0000 0.989 1.000 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.0000 0.989 1.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0376 0.990 0.996 0.004
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.989 1.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.991 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.991 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0000 0.989 1.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 2 0.0000 0.991 0.000 1.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0376 0.990 0.996 0.004
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.991 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.989 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.991 0.000 1.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.989 1.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.989 1.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0376 0.990 0.996 0.004
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.0000 0.991 0.000 1.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.991 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0376 0.990 0.996 0.004
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0376 0.990 0.996 0.004
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0376 0.990 0.996 0.004
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.0376 0.990 0.996 0.004
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0000 0.989 1.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.989 1.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.0376 0.990 0.996 0.004
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0376 0.990 0.996 0.004
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.991 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.0376 0.990 0.996 0.004
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.0000 0.991 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.946 1.000 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 3 0.3192 0.862 0.112 0.000 0.888
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.0000 0.994 0.000 0.000 1.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 3 0.0000 0.994 0.000 0.000 1.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 2 0.6095 0.468 0.392 0.608 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.920 0.000 1.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 1 0.1411 0.913 0.964 0.036 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.946 1.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0000 0.946 1.000 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 1 0.0000 0.946 1.000 0.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.920 0.000 1.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.920 0.000 1.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.946 1.000 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.3686 0.821 0.140 0.860 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 1 0.0000 0.946 1.000 0.000 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 1 0.1031 0.925 0.976 0.024 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 1 0.0000 0.946 1.000 0.000 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.946 1.000 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.920 0.000 1.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.920 0.000 1.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.946 1.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.920 0.000 1.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 1 0.0000 0.946 1.000 0.000 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 3 0.0000 0.994 0.000 0.000 1.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.920 0.000 1.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.946 1.000 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.946 1.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.6026 0.444 0.624 0.000 0.376
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.0000 0.946 1.000 0.000 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.0000 0.946 1.000 0.000 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.920 0.000 1.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.946 1.000 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 3 0.0000 0.994 0.000 0.000 1.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.0000 0.946 1.000 0.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.946 1.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 1 0.0000 0.946 1.000 0.000 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.1860 0.900 0.948 0.000 0.052
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.0000 0.946 1.000 0.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.920 0.000 1.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.946 1.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.920 0.000 1.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 3 0.0000 0.994 0.000 0.000 1.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.0000 0.994 0.000 0.000 1.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.946 1.000 0.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.0000 0.920 0.000 1.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.0000 0.920 0.000 1.000 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.5678 0.611 0.316 0.684 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0000 0.920 0.000 1.000 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 2 0.6309 0.112 0.500 0.500 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.0000 0.946 1.000 0.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.4346 0.781 0.184 0.816 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.0000 0.946 1.000 0.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.0000 0.946 1.000 0.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.6026 0.444 0.624 0.000 0.376
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 2 0.3686 0.821 0.140 0.860 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 3 0.0000 0.994 0.000 0.000 1.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.920 0.000 1.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 3 0.0000 0.994 0.000 0.000 1.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.6026 0.444 0.624 0.000 0.376
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.0000 0.920 0.000 1.000 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.0000 0.946 1.000 0.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.920 0.000 1.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.3879 0.811 0.152 0.848 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0000 0.994 0.000 0.000 1.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.6026 0.503 0.376 0.624 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.0000 0.946 1.000 0.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.946 1.000 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.946 1.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.0000 0.994 0.000 0.000 1.000
#> 352471DC-A881-4EA8-B646-EB1200291893 3 0.0000 0.994 0.000 0.000 1.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.920 0.000 1.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.920 0.000 1.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 1 0.2261 0.878 0.932 0.068 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.920 0.000 1.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.946 1.000 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.920 0.000 1.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.6026 0.444 0.624 0.000 0.376
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.6026 0.444 0.624 0.000 0.376
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 3 0.0000 0.994 0.000 0.000 1.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.0000 0.946 1.000 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 3 0.0000 0.994 0.000 0.000 1.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 3 0.0000 0.994 0.000 0.000 1.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.946 1.000 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.920 0.000 1.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0000 0.920 0.000 1.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.0000 0.946 1.000 0.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.946 1.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.946 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.920 0.000 1.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.3752 0.818 0.144 0.856 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.0000 0.946 1.000 0.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 3 0.0000 0.994 0.000 0.000 1.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.920 0.000 1.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 3 0.0000 0.994 0.000 0.000 1.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.0000 0.994 0.000 0.000 1.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 3 0.0237 0.990 0.004 0.000 0.996
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.946 1.000 0.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.6026 0.444 0.624 0.000 0.376
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.920 0.000 1.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.920 0.000 1.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.0000 0.994 0.000 0.000 1.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0000 0.946 1.000 0.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.946 1.000 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.920 0.000 1.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 3 0.0000 0.994 0.000 0.000 1.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.920 0.000 1.000 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 3 0.0000 0.994 0.000 0.000 1.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 3 0.0000 0.994 0.000 0.000 1.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.946 1.000 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.6008 0.511 0.372 0.628 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.920 0.000 1.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.946 1.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.946 1.000 0.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.946 1.000 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.0000 0.946 1.000 0.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.0000 0.994 0.000 0.000 1.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.6026 0.444 0.624 0.000 0.376
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.0000 0.946 1.000 0.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.946 1.000 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.920 0.000 1.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.0000 0.946 1.000 0.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.0000 0.920 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.0336 0.9602 0.00 0.000 0.008 0.992
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 4 0.1211 0.9271 0.04 0.000 0.000 0.960
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 1.0000 1.00 0.000 0.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 1.0000 1.00 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.8192 0.00 0.000 1.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.2345 0.8505 0.00 0.900 0.100 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0000 0.8192 0.00 0.000 1.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 3 0.4103 0.7204 0.00 0.000 0.744 0.256
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 3 0.4103 0.7204 0.00 0.000 0.744 0.256
#> 806616FE-1855-4284-9265-42842104CB21 3 0.2704 0.7906 0.00 0.000 0.876 0.124
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.4925 0.2627 0.00 0.572 0.428 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.1716 0.8146 0.00 0.000 0.936 0.064
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0000 0.8192 0.00 0.000 1.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 3 0.0592 0.8192 0.00 0.000 0.984 0.016
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 3 0.4877 0.5477 0.00 0.000 0.592 0.408
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.0000 0.8192 0.00 0.000 1.000 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 1.0000 1.00 0.000 0.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 4 0.0469 0.9558 0.00 0.000 0.012 0.988
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 3 0.0000 0.8192 0.00 0.000 1.000 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 1.0000 1.00 0.000 0.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.0000 0.8192 0.00 0.000 1.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.2345 0.8375 0.00 0.000 0.100 0.900
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.4804 0.5935 0.00 0.000 0.616 0.384
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 1.0000 1.00 0.000 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 1.0000 1.00 0.000 0.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.3356 0.7702 0.00 0.000 0.824 0.176
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.4193 0.6648 0.00 0.732 0.268 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.4103 0.6754 0.00 0.744 0.256 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 3 0.4103 0.6066 0.00 0.256 0.744 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 3 0.0000 0.8192 0.00 0.000 1.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.2589 0.7948 0.00 0.000 0.884 0.116
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.4925 0.2627 0.00 0.572 0.428 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.4790 0.6002 0.00 0.000 0.620 0.380
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.2216 0.7469 0.00 0.092 0.908 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 1.0000 1.00 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 1.0000 1.00 0.000 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0000 0.8192 0.00 0.000 1.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 3 0.4103 0.6066 0.00 0.256 0.744 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 4 0.3801 0.6875 0.22 0.000 0.000 0.780
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0336 0.8171 0.00 0.008 0.992 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.4996 -0.0886 0.00 0.000 0.484 0.516
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 3 0.0000 0.8192 0.00 0.000 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> 352471DC-A881-4EA8-B646-EB1200291893 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 3 0.5288 0.6509 0.00 0.224 0.720 0.056
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 3 0.4790 0.6002 0.00 0.000 0.620 0.380
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 1.0000 1.00 0.000 0.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 1.0000 1.00 0.000 0.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 1.0000 1.00 0.000 0.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.2704 0.8319 0.00 0.876 0.124 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 3 0.4790 0.6002 0.00 0.000 0.620 0.380
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 3 0.0000 0.8192 0.00 0.000 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.4925 0.2627 0.00 0.572 0.428 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.0000 0.8192 0.00 0.000 1.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 1.0000 1.00 0.000 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 1.0000 1.00 0.000 0.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.0000 1.0000 1.00 0.000 0.000 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 3 0.4790 0.6002 0.00 0.000 0.620 0.380
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0000 1.0000 1.00 0.000 0.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 3 0.0000 0.8192 0.00 0.000 1.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 1.0000 1.00 0.000 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 1.0000 1.00 0.000 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 1.0000 1.00 0.000 0.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 3 0.4730 0.6185 0.00 0.000 0.636 0.364
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0000 0.8192 0.00 0.000 1.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 3 0.4103 0.7204 0.00 0.000 0.744 0.256
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 3 0.4790 0.6002 0.00 0.000 0.620 0.380
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0000 1.0000 1.00 0.000 0.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 3 0.4103 0.7204 0.00 0.000 0.744 0.256
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.9680 0.00 0.000 0.000 1.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.9240 0.00 1.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.2760 0.7896 0.00 0.000 0.872 0.128
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.4222 0.6872 0.00 0.728 0.272 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0609 0.8129 0.980 0.000 0.020 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 5 0.4235 -0.3273 0.424 0.000 0.000 0.000 0.576
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 4 0.0000 0.9263 0.000 0.000 0.000 1.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 4 0.3561 0.7741 0.000 0.000 0.000 0.740 0.260
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.2230 0.6319 0.000 0.000 0.884 0.000 0.116
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.5314 0.5722 0.000 0.528 0.052 0.000 0.420
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.3895 0.6212 0.000 0.000 0.680 0.000 0.320
#> 45EAD449-C59A-463E-880A-C375CDD039BA 3 0.1197 0.7018 0.048 0.000 0.952 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 3 0.1121 0.7027 0.044 0.000 0.956 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.4908 0.6200 0.044 0.000 0.636 0.000 0.320
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.3895 0.7905 0.000 0.680 0.000 0.000 0.320
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.3796 0.8035 0.000 0.700 0.000 0.000 0.300
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.8240 1.000 0.000 0.000 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 5 0.6710 -0.0226 0.000 0.316 0.264 0.000 0.420
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.4908 0.6200 0.044 0.000 0.636 0.000 0.320
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.2377 0.6894 0.000 0.000 0.872 0.000 0.128
#> 853120F0-857B-4108-9EC8-727189630C5F 1 0.3895 0.4478 0.680 0.000 0.000 0.000 0.320
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 3 0.1121 0.7018 0.044 0.000 0.956 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.7414 0.000 1.000 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.3612 0.8078 0.000 0.732 0.000 0.000 0.268
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 3 0.4219 0.4474 0.416 0.000 0.584 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.7414 0.000 1.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.3895 0.6212 0.000 0.000 0.680 0.000 0.320
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.0000 0.9263 0.000 0.000 0.000 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.3684 0.8083 0.000 0.720 0.000 0.000 0.280
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.8240 1.000 0.000 0.000 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.8240 1.000 0.000 0.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.3561 0.7110 0.740 0.000 0.000 0.000 0.260
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.4270 0.4327 0.668 0.000 0.012 0.000 0.320
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 3 0.0000 0.7001 0.000 0.000 1.000 0.000 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.7414 0.000 1.000 0.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.8240 1.000 0.000 0.000 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.3561 0.7741 0.000 0.000 0.000 0.740 0.260
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.0000 0.7001 0.000 0.000 1.000 0.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0609 0.8129 0.980 0.000 0.020 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.5036 0.6178 0.052 0.000 0.628 0.000 0.320
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0000 0.8240 1.000 0.000 0.000 0.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.3895 0.4478 0.680 0.000 0.000 0.000 0.320
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.7414 0.000 1.000 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.8240 1.000 0.000 0.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.3796 0.8035 0.000 0.700 0.000 0.000 0.300
#> 692C65BB-BF32-4846-806B-01A285BED1B9 4 0.0000 0.9263 0.000 0.000 0.000 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 4 0.0000 0.9263 0.000 0.000 0.000 1.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.4074 0.5068 0.364 0.000 0.636 0.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.4473 0.6667 0.000 0.580 0.008 0.000 0.412
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.4025 0.8027 0.000 0.700 0.008 0.000 0.292
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 3 0.2605 0.6046 0.000 0.000 0.852 0.000 0.148
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.3796 0.8035 0.000 0.700 0.000 0.000 0.300
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 3 0.2516 0.6086 0.000 0.000 0.860 0.000 0.140
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.4402 0.5154 0.352 0.000 0.636 0.000 0.012
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 5 0.3561 0.1653 0.000 0.000 0.260 0.000 0.740
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.0000 0.8240 1.000 0.000 0.000 0.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.5036 0.6178 0.052 0.000 0.628 0.000 0.320
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.3561 0.7110 0.740 0.000 0.000 0.000 0.260
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.3452 0.4373 0.000 0.000 0.756 0.000 0.244
#> B5474EEB-D585-4668-959C-38F240F55BC2 4 0.0000 0.9263 0.000 0.000 0.000 1.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.3684 0.8083 0.000 0.720 0.000 0.000 0.280
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.0000 0.9263 0.000 0.000 0.000 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.3561 0.7110 0.740 0.000 0.000 0.000 0.260
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.4227 0.6698 0.000 0.580 0.000 0.000 0.420
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0000 0.7001 0.000 0.000 1.000 0.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.3684 0.8083 0.000 0.720 0.000 0.000 0.280
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 3 0.2605 0.6046 0.000 0.000 0.852 0.000 0.148
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.6400 0.4247 0.512 0.000 0.000 0.228 0.260
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.4287 0.5071 0.000 0.000 0.540 0.000 0.460
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.3424 0.5496 0.760 0.000 0.240 0.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 3 0.0000 0.7001 0.000 0.000 1.000 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.8240 1.000 0.000 0.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.3561 0.7110 0.740 0.000 0.000 0.000 0.260
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.3561 0.7110 0.740 0.000 0.000 0.000 0.260
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.3796 0.8035 0.000 0.700 0.000 0.000 0.300
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.3796 0.8035 0.000 0.700 0.000 0.000 0.300
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 3 0.4366 0.6217 0.016 0.000 0.664 0.000 0.320
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.7414 0.000 1.000 0.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 3 0.4101 0.5008 0.372 0.000 0.628 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.3684 0.8083 0.000 0.720 0.000 0.000 0.280
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.1121 0.8056 0.956 0.000 0.000 0.000 0.044
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.3561 0.7110 0.740 0.000 0.000 0.000 0.260
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.0000 0.9263 0.000 0.000 0.000 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.3895 0.4478 0.680 0.000 0.000 0.000 0.320
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 4 0.0000 0.9263 0.000 0.000 0.000 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.3561 0.7741 0.000 0.000 0.000 0.740 0.260
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.8240 1.000 0.000 0.000 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.3932 0.7838 0.000 0.672 0.000 0.000 0.328
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 5 0.6606 0.0735 0.000 0.216 0.364 0.000 0.420
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 3 0.5036 0.6178 0.052 0.000 0.628 0.000 0.320
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.8240 1.000 0.000 0.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 3 0.0000 0.7001 0.000 0.000 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.3684 0.8083 0.000 0.720 0.000 0.000 0.280
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 5 0.3561 0.1653 0.000 0.000 0.260 0.000 0.740
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.0000 0.7001 0.000 0.000 1.000 0.000 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 4 0.0000 0.9263 0.000 0.000 0.000 1.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.7414 0.000 1.000 0.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 4 0.0000 0.9263 0.000 0.000 0.000 1.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 4 0.3561 0.7741 0.000 0.000 0.000 0.740 0.260
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 5 0.4242 -0.3340 0.428 0.000 0.000 0.000 0.572
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 3 0.4101 0.5008 0.372 0.000 0.628 0.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.3561 0.7110 0.740 0.000 0.000 0.000 0.260
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.7414 0.000 1.000 0.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.7414 0.000 1.000 0.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 4 0.3561 0.7741 0.000 0.000 0.000 0.740 0.260
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 3 0.2280 0.6285 0.000 0.000 0.880 0.000 0.120
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.8240 1.000 0.000 0.000 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.7414 0.000 1.000 0.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.0000 0.9263 0.000 0.000 0.000 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.4084 0.7815 0.000 0.668 0.004 0.000 0.328
#> F900E9BE-2400-4451-9434-EE8BC513BA94 4 0.0000 0.9263 0.000 0.000 0.000 1.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 4 0.0000 0.9263 0.000 0.000 0.000 1.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 3 0.3752 0.5707 0.292 0.000 0.708 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.4283 0.5096 0.000 0.000 0.544 0.000 0.456
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.7414 0.000 1.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 3 0.1121 0.7027 0.044 0.000 0.956 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.8240 1.000 0.000 0.000 0.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.8240 1.000 0.000 0.000 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 3 0.5036 0.6178 0.052 0.000 0.628 0.000 0.320
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 4 0.0000 0.9263 0.000 0.000 0.000 1.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.3561 0.7110 0.740 0.000 0.000 0.000 0.260
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 3 0.0290 0.7009 0.008 0.000 0.992 0.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.8240 1.000 0.000 0.000 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.7414 0.000 1.000 0.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.4973 0.6191 0.048 0.000 0.632 0.000 0.320
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.6638 -0.2613 0.000 0.364 0.224 0.000 0.412
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.1501 0.7744 0.924 0.000 0.076 0.000 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 4 0.4493 0.4153 0.040 0.000 0.364 0.596 0.000 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 4 0.3695 0.3827 0.000 0.000 0.000 0.624 0.000 0.376
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.6067 0.5834 0.000 0.004 0.404 0.376 0.216 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.3469 0.7403 0.000 0.824 0.072 0.012 0.092 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.2234 0.5949 0.000 0.000 0.872 0.004 0.124 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 3 0.4948 0.6598 0.076 0.000 0.564 0.360 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 3 0.4808 0.6628 0.064 0.000 0.576 0.360 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.2231 0.6080 0.068 0.000 0.900 0.004 0.028 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0713 0.8318 0.000 0.972 0.000 0.000 0.028 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0146 0.8351 0.000 0.996 0.000 0.000 0.004 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.1075 0.8042 0.952 0.000 0.000 0.048 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.4807 0.6329 0.000 0.684 0.092 0.012 0.212 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.1387 0.6065 0.068 0.000 0.932 0.000 0.000 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.5453 0.6372 0.000 0.000 0.556 0.284 0.160 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 1 0.4609 0.4567 0.588 0.000 0.364 0.048 0.000 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 3 0.5379 0.6456 0.120 0.000 0.516 0.364 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 5 0.2996 0.9811 0.000 0.228 0.000 0.000 0.772 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.2883 0.5824 0.000 0.788 0.000 0.000 0.212 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.3765 0.1097 0.596 0.000 0.404 0.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 5 0.2912 0.9902 0.000 0.216 0.000 0.000 0.784 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.0000 0.6219 0.000 0.000 1.000 0.000 0.000 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0632 0.8254 0.000 0.976 0.000 0.000 0.024 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.8110 1.000 0.000 0.000 0.000 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.8110 1.000 0.000 0.000 0.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.3804 0.4985 0.424 0.000 0.000 0.576 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.4620 0.4538 0.584 0.000 0.368 0.048 0.000 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 3 0.3647 0.6637 0.000 0.000 0.640 0.360 0.000 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 5 0.2912 0.9902 0.000 0.216 0.000 0.000 0.784 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.1075 0.7931 0.952 0.000 0.048 0.000 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 4 0.3804 0.3158 0.000 0.000 0.000 0.576 0.000 0.424
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.5411 0.6371 0.000 0.000 0.512 0.364 0.124 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0458 0.8032 0.984 0.000 0.016 0.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.1444 0.6047 0.072 0.000 0.928 0.000 0.000 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.1075 0.8042 0.952 0.000 0.000 0.048 0.000 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.4609 0.4567 0.588 0.000 0.364 0.048 0.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 5 0.2912 0.9902 0.000 0.216 0.000 0.000 0.784 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.1075 0.8042 0.952 0.000 0.000 0.048 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0260 0.8339 0.000 0.992 0.000 0.000 0.008 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 3 0.3995 0.1807 0.480 0.000 0.516 0.004 0.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.3488 0.6920 0.000 0.764 0.004 0.016 0.216 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.1364 0.8181 0.000 0.944 0.004 0.004 0.048 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 3 0.5757 0.6294 0.000 0.028 0.508 0.372 0.092 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0146 0.8351 0.000 0.996 0.000 0.000 0.004 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 3 0.6451 0.5692 0.000 0.024 0.384 0.376 0.216 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.3872 0.3215 0.392 0.000 0.604 0.004 0.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 3 0.5343 0.0339 0.000 0.324 0.572 0.012 0.092 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.0000 0.8110 1.000 0.000 0.000 0.000 0.000 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.1444 0.6047 0.072 0.000 0.928 0.000 0.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 4 0.3706 0.5325 0.380 0.000 0.000 0.620 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 4 0.7519 -0.4285 0.000 0.200 0.208 0.376 0.216 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0713 0.8232 0.000 0.972 0.000 0.000 0.028 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.3695 0.5374 0.376 0.000 0.000 0.624 0.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.2070 0.7869 0.000 0.896 0.000 0.012 0.092 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.5411 0.6371 0.000 0.000 0.512 0.364 0.124 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.1075 0.8140 0.000 0.952 0.000 0.000 0.048 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 3 0.5820 0.6281 0.000 0.032 0.504 0.372 0.092 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 4 0.5055 0.5414 0.132 0.000 0.000 0.624 0.000 0.244
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.3865 0.5390 0.000 0.020 0.748 0.016 0.216 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.2871 0.6108 0.804 0.000 0.192 0.004 0.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 3 0.3899 0.6633 0.000 0.000 0.628 0.364 0.008 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.8110 1.000 0.000 0.000 0.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 4 0.3695 0.5374 0.376 0.000 0.000 0.624 0.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 4 0.3695 0.5374 0.376 0.000 0.000 0.624 0.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0146 0.8351 0.000 0.996 0.000 0.000 0.004 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0146 0.8351 0.000 0.996 0.000 0.000 0.004 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 3 0.0146 0.6214 0.000 0.004 0.996 0.000 0.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 5 0.3050 0.9757 0.000 0.236 0.000 0.000 0.764 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 3 0.3823 0.2518 0.436 0.000 0.564 0.000 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.2300 0.6962 0.000 0.856 0.000 0.000 0.144 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.2048 0.7074 0.880 0.000 0.000 0.120 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.3747 0.5070 0.396 0.000 0.000 0.604 0.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.4609 0.4567 0.588 0.000 0.364 0.048 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.3804 0.3158 0.000 0.000 0.000 0.576 0.000 0.424
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.1075 0.8042 0.952 0.000 0.000 0.048 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0937 0.8307 0.000 0.960 0.000 0.000 0.040 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.6282 0.2961 0.000 0.468 0.068 0.372 0.092 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 3 0.1444 0.6047 0.072 0.000 0.928 0.000 0.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.1075 0.8042 0.952 0.000 0.000 0.048 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 3 0.5641 0.6356 0.008 0.000 0.504 0.364 0.124 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0713 0.8232 0.000 0.972 0.000 0.000 0.028 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 3 0.5288 0.0839 0.000 0.308 0.588 0.012 0.092 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.5411 0.6371 0.000 0.000 0.512 0.364 0.124 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 5 0.3050 0.9731 0.000 0.236 0.000 0.000 0.764 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 4 0.3804 0.3158 0.000 0.000 0.000 0.576 0.000 0.424
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 4 0.4218 0.4262 0.024 0.000 0.360 0.616 0.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 3 0.3866 0.1726 0.484 0.000 0.516 0.000 0.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 4 0.3695 0.5374 0.376 0.000 0.000 0.624 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 5 0.2912 0.9902 0.000 0.216 0.000 0.000 0.784 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 5 0.2912 0.9902 0.000 0.216 0.000 0.000 0.784 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 4 0.3804 0.3158 0.000 0.000 0.000 0.576 0.000 0.424
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 3 0.5283 0.6391 0.000 0.004 0.528 0.376 0.092 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.1075 0.8042 0.952 0.000 0.000 0.048 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 5 0.3050 0.9757 0.000 0.236 0.000 0.000 0.764 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0713 0.8295 0.000 0.972 0.000 0.000 0.028 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 3 0.5205 0.3197 0.412 0.000 0.496 0.092 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.4102 0.5437 0.000 0.020 0.736 0.028 0.216 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 5 0.2912 0.9902 0.000 0.216 0.000 0.000 0.784 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 3 0.4707 0.6639 0.056 0.000 0.584 0.360 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.8110 1.000 0.000 0.000 0.000 0.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.8110 1.000 0.000 0.000 0.000 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 3 0.1444 0.6047 0.072 0.000 0.928 0.000 0.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> 12F54761-4F68-4181-8421-88EA858902FC 4 0.3695 0.5374 0.376 0.000 0.000 0.624 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 3 0.3782 0.6641 0.004 0.000 0.636 0.360 0.000 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.8110 1.000 0.000 0.000 0.000 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 5 0.2912 0.9902 0.000 0.216 0.000 0.000 0.784 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.1444 0.6047 0.072 0.000 0.928 0.000 0.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.6156 0.4361 0.000 0.520 0.024 0.240 0.216 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", "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 17548 rows and 122 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 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 0.538 0.919 0.946 0.5014 0.497 0.497
#> 3 3 0.667 0.848 0.890 0.3085 0.684 0.450
#> 4 4 0.498 0.452 0.654 0.1105 0.841 0.571
#> 5 5 0.801 0.744 0.873 0.0841 0.859 0.521
#> 6 6 0.849 0.775 0.869 0.0432 0.924 0.659
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 2 0.5178 0.913 0.116 0.884
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.0938 0.952 0.012 0.988
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.4815 0.926 0.896 0.104
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 2 0.5178 0.913 0.116 0.884
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 1 0.4939 0.925 0.892 0.108
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.953 0.000 1.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 1 0.4939 0.925 0.892 0.108
#> 45EAD449-C59A-463E-880A-C375CDD039BA 2 0.5178 0.913 0.116 0.884
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 2 0.5178 0.913 0.116 0.884
#> 806616FE-1855-4284-9265-42842104CB21 1 0.4939 0.925 0.892 0.108
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.953 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.953 0.000 1.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 2 0.5178 0.913 0.116 0.884
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0672 0.952 0.008 0.992
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 1 0.4939 0.925 0.892 0.108
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 1 0.4939 0.925 0.892 0.108
#> 853120F0-857B-4108-9EC8-727189630C5F 1 0.4939 0.925 0.892 0.108
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.927 1.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.953 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.953 0.000 1.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.927 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.953 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 1 0.4939 0.925 0.892 0.108
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.927 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.953 0.000 1.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.927 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.927 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.927 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 1 0.4939 0.925 0.892 0.108
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.0938 0.952 0.012 0.988
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.953 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 2 0.5178 0.913 0.116 0.884
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.927 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.4431 0.927 0.908 0.092
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.927 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 1 0.4939 0.925 0.892 0.108
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.4431 0.927 0.908 0.092
#> 50D620F3-5C52-42FB-89A1-6840A7444647 1 0.4939 0.925 0.892 0.108
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.953 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 2 0.5178 0.913 0.116 0.884
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.953 0.000 1.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.927 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.1414 0.928 0.980 0.020
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.927 1.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 1 0.4939 0.925 0.892 0.108
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 1 0.4939 0.925 0.892 0.108
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0672 0.952 0.008 0.992
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.6048 0.799 0.148 0.852
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 2 0.5178 0.913 0.116 0.884
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.4815 0.926 0.896 0.104
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.953 0.000 1.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.4815 0.926 0.896 0.104
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 1 0.4939 0.925 0.892 0.108
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 2 0.5178 0.913 0.116 0.884
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.4815 0.926 0.896 0.104
#> B5474EEB-D585-4668-959C-38F240F55BC2 2 0.5178 0.913 0.116 0.884
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.953 0.000 1.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.927 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.927 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 1 0.4939 0.925 0.892 0.108
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.4815 0.926 0.896 0.104
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.953 0.000 1.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0938 0.952 0.012 0.988
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.4815 0.926 0.896 0.104
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 1 0.4939 0.925 0.892 0.108
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.4431 0.927 0.908 0.092
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.927 1.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.927 1.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0000 0.927 1.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.9998 -0.142 0.508 0.492
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.953 0.000 1.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.953 0.000 1.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.953 0.000 1.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.953 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 2 0.5178 0.913 0.116 0.884
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0672 0.952 0.008 0.992
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.8555 0.548 0.720 0.280
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.927 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.927 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.0938 0.952 0.012 0.988
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.927 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.927 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 2 0.5178 0.913 0.116 0.884
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#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0938 0.952 0.012 0.988
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0672 0.952 0.008 0.992
#> 6F7DB73C-FE46-402C-9001-DC2005278069 2 0.5178 0.913 0.116 0.884
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.927 1.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0672 0.952 0.008 0.992
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.953 0.000 1.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.4815 0.926 0.896 0.104
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#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.927 1.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.4939 0.925 0.892 0.108
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.0938 0.952 0.012 0.988
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.927 1.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 2 0.5178 0.913 0.116 0.884
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.953 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.953 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.4815 0.926 0.896 0.104
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 2 0.5178 0.913 0.116 0.884
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 2 0.5178 0.913 0.116 0.884
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.953 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.927 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0938 0.952 0.012 0.988
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.927 1.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.927 1.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.927 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 1 0.4939 0.925 0.892 0.108
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.953 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 2 0.5178 0.913 0.116 0.884
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.3431 0.884 0.936 0.064
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.927 1.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0938 0.952 0.012 0.988
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.4815 0.926 0.896 0.104
#> 12F54761-4F68-4181-8421-88EA858902FC 2 0.5178 0.913 0.116 0.884
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.5178 0.913 0.116 0.884
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.927 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.953 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.4815 0.926 0.896 0.104
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 1 0.4939 0.925 0.892 0.108
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0747 0.827 0.984 0.016 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 2 0.2711 0.895 0.088 0.912 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.0892 0.926 0.020 0.000 0.980
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0747 0.827 0.984 0.016 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0747 0.928 0.016 0.000 0.984
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.4702 0.890 0.212 0.788 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.1529 0.926 0.000 0.040 0.960
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0747 0.827 0.984 0.016 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.1411 0.809 0.964 0.036 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0747 0.928 0.016 0.000 0.984
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.2537 0.894 0.080 0.920 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.4702 0.890 0.212 0.788 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0747 0.827 0.984 0.016 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.2297 0.878 0.036 0.944 0.020
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.1860 0.921 0.000 0.052 0.948
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.1529 0.926 0.000 0.040 0.960
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.1529 0.926 0.000 0.040 0.960
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.4702 0.831 0.788 0.000 0.212
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0237 0.874 0.004 0.996 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.1289 0.884 0.032 0.968 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.4702 0.831 0.788 0.000 0.212
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0424 0.869 0.000 0.992 0.008
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.1753 0.923 0.000 0.048 0.952
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.4702 0.831 0.788 0.000 0.212
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.4702 0.890 0.212 0.788 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.4702 0.831 0.788 0.000 0.212
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.4702 0.831 0.788 0.000 0.212
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.4702 0.831 0.788 0.000 0.212
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.1529 0.926 0.000 0.040 0.960
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.4555 0.891 0.200 0.800 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0592 0.866 0.000 0.988 0.012
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0747 0.827 0.984 0.016 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 3 0.5678 0.401 0.316 0.000 0.684
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 3 0.2537 0.865 0.080 0.000 0.920
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.4702 0.831 0.788 0.000 0.212
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.1753 0.923 0.000 0.048 0.952
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 3 0.0892 0.926 0.020 0.000 0.980
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.1529 0.926 0.000 0.040 0.960
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.1860 0.888 0.052 0.948 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0747 0.827 0.984 0.016 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.4702 0.890 0.212 0.788 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.4702 0.831 0.788 0.000 0.212
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.5968 0.248 0.364 0.000 0.636
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.5098 0.795 0.752 0.000 0.248
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.1860 0.921 0.000 0.052 0.948
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.1529 0.926 0.000 0.040 0.960
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.4750 0.888 0.216 0.784 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.6318 0.353 0.008 0.636 0.356
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0747 0.827 0.984 0.016 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.0747 0.928 0.016 0.000 0.984
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.2959 0.896 0.100 0.900 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0747 0.928 0.016 0.000 0.984
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.4233 0.810 0.004 0.160 0.836
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0747 0.827 0.984 0.016 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.0747 0.928 0.016 0.000 0.984
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0747 0.827 0.984 0.016 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.4702 0.890 0.212 0.788 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.4796 0.824 0.780 0.000 0.220
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.4702 0.831 0.788 0.000 0.212
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.4235 0.790 0.000 0.176 0.824
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.0747 0.928 0.016 0.000 0.984
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0747 0.863 0.000 0.984 0.016
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.4750 0.888 0.216 0.784 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0892 0.926 0.020 0.000 0.980
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.2261 0.909 0.000 0.068 0.932
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.0747 0.928 0.016 0.000 0.984
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.4702 0.831 0.788 0.000 0.212
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.4702 0.831 0.788 0.000 0.212
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.0892 0.926 0.020 0.000 0.980
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0747 0.827 0.984 0.016 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.4702 0.890 0.212 0.788 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.4702 0.890 0.212 0.788 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.3192 0.896 0.112 0.888 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0592 0.866 0.000 0.988 0.012
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0747 0.827 0.984 0.016 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0892 0.862 0.000 0.980 0.020
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0424 0.829 0.992 0.008 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.4702 0.831 0.788 0.000 0.212
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.5254 0.775 0.736 0.000 0.264
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.4750 0.888 0.216 0.784 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.6307 0.304 0.512 0.000 0.488
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.4702 0.831 0.788 0.000 0.212
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0747 0.827 0.984 0.016 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.4702 0.890 0.212 0.788 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.4750 0.888 0.216 0.784 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.4750 0.888 0.216 0.784 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0747 0.827 0.984 0.016 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.4702 0.831 0.788 0.000 0.212
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.4702 0.890 0.212 0.788 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.2625 0.895 0.084 0.916 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.0747 0.928 0.016 0.000 0.984
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.1289 0.834 0.968 0.000 0.032
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0237 0.874 0.004 0.996 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.6252 0.433 0.556 0.000 0.444
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.1411 0.927 0.000 0.036 0.964
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 2 0.4750 0.888 0.216 0.784 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.4702 0.831 0.788 0.000 0.212
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0747 0.827 0.984 0.016 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0592 0.866 0.000 0.988 0.012
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.2229 0.883 0.044 0.944 0.012
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.0892 0.926 0.020 0.000 0.980
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.1163 0.817 0.972 0.028 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0747 0.827 0.984 0.016 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0592 0.866 0.000 0.988 0.012
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.4702 0.831 0.788 0.000 0.212
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.4702 0.889 0.212 0.788 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.2537 0.835 0.920 0.000 0.080
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.4702 0.831 0.788 0.000 0.212
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.4702 0.831 0.788 0.000 0.212
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.2261 0.909 0.000 0.068 0.932
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0237 0.871 0.000 0.996 0.004
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0747 0.827 0.984 0.016 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.4575 0.832 0.812 0.004 0.184
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.4702 0.831 0.788 0.000 0.212
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.4750 0.888 0.216 0.784 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.0892 0.926 0.020 0.000 0.980
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0747 0.827 0.984 0.016 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.0892 0.824 0.980 0.020 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.4702 0.831 0.788 0.000 0.212
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0592 0.866 0.000 0.988 0.012
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.0747 0.928 0.016 0.000 0.984
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.1860 0.921 0.000 0.052 0.948
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.4776 0.61518 0.624 0.000 0.000 0.376
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.7704 -0.20031 0.440 0.412 0.020 0.128
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.7815 0.01402 0.256 0.000 0.392 0.352
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.3123 0.57756 0.844 0.000 0.000 0.156
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.2408 0.58001 0.000 0.000 0.896 0.104
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.6360 0.69170 0.180 0.656 0.000 0.164
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0188 0.61978 0.000 0.000 0.996 0.004
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.5712 0.61401 0.584 0.032 0.000 0.384
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.6738 0.57517 0.544 0.104 0.000 0.352
#> 806616FE-1855-4284-9265-42842104CB21 3 0.3801 0.49054 0.000 0.000 0.780 0.220
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.3377 0.74285 0.140 0.848 0.000 0.012
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.6279 0.69968 0.180 0.664 0.000 0.156
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.4776 0.61518 0.624 0.000 0.000 0.376
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.7782 0.48174 0.032 0.556 0.248 0.164
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.0188 0.61978 0.000 0.000 0.996 0.004
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0657 0.61988 0.000 0.004 0.984 0.012
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.0336 0.62014 0.000 0.000 0.992 0.008
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.3873 0.52833 0.000 0.000 0.228 0.772
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.1118 0.75316 0.000 0.964 0.000 0.036
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0804 0.76081 0.012 0.980 0.000 0.008
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.4307 0.56613 0.024 0.000 0.192 0.784
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.1022 0.75415 0.000 0.968 0.000 0.032
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.0336 0.61945 0.000 0.008 0.992 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 4 0.7922 0.06775 0.340 0.000 0.320 0.340
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.6279 0.69968 0.180 0.664 0.000 0.156
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.4679 0.56981 0.044 0.000 0.184 0.772
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.4916 0.56954 0.056 0.000 0.184 0.760
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.4956 0.56965 0.056 0.000 0.188 0.756
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.0000 0.62041 0.000 0.000 1.000 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 4 0.8307 -0.29705 0.080 0.324 0.104 0.492
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.1118 0.75316 0.000 0.964 0.000 0.036
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.5112 0.61787 0.608 0.008 0.000 0.384
#> F5940915-4123-49B3-95EE-4A0412BE8C30 3 0.7674 0.07059 0.224 0.000 0.436 0.340
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.4992 0.13336 0.000 0.000 0.476 0.524
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.4677 0.56944 0.040 0.000 0.192 0.768
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.0000 0.62041 0.000 0.000 1.000 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.6101 0.07991 0.052 0.000 0.388 0.560
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0188 0.61978 0.000 0.000 0.996 0.004
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0927 0.76143 0.016 0.976 0.000 0.008
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.5099 0.61768 0.612 0.008 0.000 0.380
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.6240 0.70078 0.176 0.668 0.000 0.156
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.7922 -0.50977 0.344 0.000 0.320 0.336
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.7918 -0.04215 0.316 0.000 0.352 0.332
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.4843 0.33851 0.000 0.000 0.396 0.604
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0712 0.61506 0.008 0.004 0.984 0.004
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.0564 0.62008 0.004 0.004 0.988 0.004
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.6508 0.68254 0.192 0.640 0.000 0.168
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.8169 0.16101 0.028 0.404 0.400 0.168
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.5712 0.61401 0.584 0.032 0.000 0.384
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.4948 0.07959 0.000 0.000 0.560 0.440
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.4121 0.73161 0.184 0.796 0.000 0.020
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.3837 0.49154 0.000 0.000 0.776 0.224
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.7214 0.36007 0.024 0.184 0.620 0.172
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.3074 0.57810 0.848 0.000 0.000 0.152
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.4564 0.31580 0.000 0.000 0.672 0.328
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.1867 0.52160 0.928 0.000 0.000 0.072
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.6279 0.69968 0.180 0.664 0.000 0.156
#> A533C39D-CE42-42AD-92AD-549157A43139 4 0.7922 0.01096 0.320 0.000 0.340 0.340
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.4973 0.41325 0.008 0.000 0.348 0.644
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.6774 0.33835 0.008 0.196 0.636 0.160
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.4907 0.13837 0.000 0.000 0.580 0.420
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.1917 0.74669 0.012 0.944 0.008 0.036
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.5720 0.59969 0.296 0.652 0.000 0.052
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.7654 0.08122 0.220 0.000 0.440 0.340
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.3719 0.52494 0.008 0.020 0.848 0.124
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.4992 -0.04042 0.000 0.000 0.524 0.476
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.4920 0.56658 0.052 0.000 0.192 0.756
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.4406 0.56778 0.028 0.000 0.192 0.780
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.7563 0.09153 0.220 0.000 0.476 0.304
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.4220 0.52091 0.748 0.004 0.000 0.248
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.6279 0.69968 0.180 0.664 0.000 0.156
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.6279 0.69968 0.180 0.664 0.000 0.156
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.4121 0.73161 0.184 0.796 0.000 0.020
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.1118 0.75316 0.000 0.964 0.000 0.036
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.6263 0.60563 0.576 0.068 0.000 0.356
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.7677 0.46123 0.024 0.552 0.260 0.164
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.5193 0.60453 0.580 0.008 0.000 0.412
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.5007 0.39995 0.008 0.000 0.356 0.636
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 4 0.7923 0.02933 0.324 0.000 0.332 0.344
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.5530 0.00978 0.632 0.336 0.000 0.032
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 3 0.7921 -0.05685 0.320 0.000 0.348 0.332
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.7721 0.24452 0.248 0.000 0.312 0.440
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.4776 0.61518 0.624 0.000 0.000 0.376
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.6279 0.69968 0.180 0.664 0.000 0.156
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.7351 0.42077 0.264 0.524 0.000 0.212
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 1 0.5816 -0.13209 0.572 0.392 0.000 0.036
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.5112 0.61787 0.608 0.008 0.000 0.384
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.4406 0.56778 0.028 0.000 0.192 0.780
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.6320 0.69597 0.180 0.660 0.000 0.160
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.4121 0.73161 0.184 0.796 0.000 0.020
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.4222 0.42638 0.000 0.000 0.728 0.272
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.3649 0.40184 0.796 0.000 0.000 0.204
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0779 0.75784 0.004 0.980 0.000 0.016
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 4 0.7740 0.02924 0.328 0.000 0.244 0.428
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.2466 0.60100 0.028 0.000 0.916 0.056
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.5005 0.15943 0.712 0.264 0.004 0.020
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.6586 0.36717 0.184 0.000 0.184 0.632
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.3074 0.57810 0.848 0.000 0.000 0.152
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.1118 0.75316 0.000 0.964 0.000 0.036
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0524 0.76058 0.008 0.988 0.000 0.004
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 4 0.7638 -0.08928 0.220 0.000 0.332 0.448
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.6677 0.58098 0.552 0.100 0.000 0.348
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.4776 0.61518 0.624 0.000 0.000 0.376
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.1118 0.75316 0.000 0.964 0.000 0.036
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 4 0.7922 0.06559 0.336 0.000 0.320 0.344
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.8132 0.57628 0.084 0.532 0.096 0.288
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.4361 0.33709 0.772 0.000 0.020 0.208
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 4 0.7716 0.05973 0.356 0.000 0.228 0.416
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.4839 0.57022 0.052 0.000 0.184 0.764
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.3663 0.52634 0.008 0.020 0.852 0.120
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.1305 0.75402 0.004 0.960 0.000 0.036
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.5626 0.61483 0.588 0.028 0.000 0.384
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.7184 -0.01669 0.316 0.000 0.160 0.524
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.6653 0.34776 0.196 0.000 0.180 0.624
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.5931 -0.29050 0.504 0.460 0.000 0.036
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.7900 -0.04314 0.296 0.000 0.360 0.344
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.3123 0.57756 0.844 0.000 0.000 0.156
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.6584 0.59061 0.568 0.096 0.000 0.336
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.6511 0.33777 0.188 0.000 0.172 0.640
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.1118 0.75316 0.000 0.964 0.000 0.036
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.3569 0.51406 0.000 0.000 0.804 0.196
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.0524 0.61729 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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0880 0.8428 0.968 0.000 0.000 0.000 0.032
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.5051 0.6086 0.664 0.264 0.000 0.000 0.072
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 5 0.3970 0.7798 0.000 0.000 0.096 0.104 0.800
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0880 0.8428 0.968 0.000 0.000 0.000 0.032
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.3304 0.7481 0.000 0.000 0.816 0.016 0.168
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.1270 0.9174 0.052 0.948 0.000 0.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0000 0.8308 0.000 0.000 1.000 0.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0955 0.8380 0.968 0.000 0.000 0.028 0.004
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.1628 0.8376 0.936 0.056 0.000 0.000 0.008
#> 806616FE-1855-4284-9265-42842104CB21 3 0.3727 0.7046 0.000 0.000 0.768 0.016 0.216
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.9308 0.000 1.000 0.000 0.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.1270 0.9174 0.052 0.948 0.000 0.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0880 0.8428 0.968 0.000 0.000 0.000 0.032
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 1 0.5891 0.1725 0.476 0.448 0.016 0.000 0.060
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.0000 0.8308 0.000 0.000 1.000 0.000 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0404 0.8278 0.000 0.000 0.988 0.012 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.0000 0.8308 0.000 0.000 1.000 0.000 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0162 0.8673 0.000 0.000 0.000 0.996 0.004
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.9308 0.000 1.000 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.9308 0.000 1.000 0.000 0.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.8672 0.000 0.000 0.000 1.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.9308 0.000 1.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.0000 0.8308 0.000 0.000 1.000 0.000 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 5 0.1851 0.8286 0.000 0.000 0.000 0.088 0.912
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.1270 0.9174 0.052 0.948 0.000 0.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0510 0.8696 0.016 0.000 0.000 0.984 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0290 0.8699 0.008 0.000 0.000 0.992 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0290 0.8699 0.008 0.000 0.000 0.992 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.0000 0.8308 0.000 0.000 1.000 0.000 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.2124 0.8327 0.916 0.056 0.000 0.000 0.028
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.9308 0.000 1.000 0.000 0.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0510 0.8444 0.984 0.000 0.000 0.000 0.016
#> F5940915-4123-49B3-95EE-4A0412BE8C30 5 0.2522 0.8234 0.000 0.000 0.012 0.108 0.880
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.0771 0.8580 0.000 0.000 0.004 0.976 0.020
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0609 0.8679 0.020 0.000 0.000 0.980 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.0000 0.8308 0.000 0.000 1.000 0.000 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 4 0.5962 -0.1372 0.000 0.000 0.108 0.468 0.424
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0000 0.8308 0.000 0.000 1.000 0.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0671 0.9280 0.004 0.980 0.000 0.000 0.016
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0609 0.8442 0.980 0.000 0.000 0.000 0.020
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.1270 0.9174 0.052 0.948 0.000 0.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 5 0.1851 0.8286 0.000 0.000 0.000 0.088 0.912
#> CB925BF0-1249-4350-A175-9A4129C43B8D 5 0.1908 0.8235 0.000 0.000 0.000 0.092 0.908
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.0324 0.8669 0.000 0.000 0.004 0.992 0.004
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0162 0.8301 0.000 0.000 0.996 0.000 0.004
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.0290 0.8292 0.000 0.000 0.992 0.008 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 1 0.4297 0.1606 0.528 0.472 0.000 0.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 1 0.7189 0.2229 0.464 0.084 0.356 0.000 0.096
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.1357 0.8286 0.948 0.000 0.000 0.048 0.004
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.6643 -0.1522 0.000 0.000 0.372 0.404 0.224
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0671 0.9271 0.004 0.980 0.000 0.000 0.016
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.3852 0.6955 0.000 0.000 0.760 0.020 0.220
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 5 0.7565 -0.1337 0.100 0.024 0.376 0.060 0.440
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0880 0.8428 0.968 0.000 0.000 0.000 0.032
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.6635 0.1975 0.000 0.000 0.416 0.360 0.224
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.1197 0.8399 0.952 0.000 0.000 0.000 0.048
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.1270 0.9174 0.052 0.948 0.000 0.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 5 0.2074 0.8251 0.000 0.000 0.000 0.104 0.896
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.1732 0.8026 0.000 0.000 0.000 0.920 0.080
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.4914 0.6478 0.000 0.028 0.672 0.016 0.284
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.6581 0.2668 0.000 0.000 0.452 0.324 0.224
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0579 0.9209 0.000 0.984 0.008 0.000 0.008
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.4283 0.0614 0.456 0.544 0.000 0.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 5 0.5901 0.1823 0.000 0.000 0.400 0.104 0.496
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.3690 0.7460 0.000 0.020 0.780 0.000 0.200
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.5917 0.3372 0.000 0.000 0.180 0.596 0.224
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0794 0.8625 0.028 0.000 0.000 0.972 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0510 0.8696 0.016 0.000 0.000 0.984 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 5 0.4172 0.7709 0.000 0.000 0.108 0.108 0.784
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.2359 0.8047 0.904 0.000 0.000 0.060 0.036
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.1270 0.9174 0.052 0.948 0.000 0.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.1270 0.9174 0.052 0.948 0.000 0.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0898 0.9259 0.008 0.972 0.000 0.000 0.020
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.9308 0.000 1.000 0.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.1628 0.8376 0.936 0.056 0.000 0.000 0.008
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 1 0.6202 0.2064 0.472 0.424 0.016 0.000 0.088
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.4787 0.3653 0.608 0.000 0.000 0.364 0.028
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.3143 0.6300 0.000 0.000 0.000 0.796 0.204
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 5 0.1608 0.8251 0.000 0.000 0.000 0.072 0.928
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 1 0.2966 0.7393 0.816 0.184 0.000 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 5 0.2127 0.8233 0.000 0.000 0.000 0.108 0.892
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 5 0.4331 0.3826 0.004 0.000 0.000 0.400 0.596
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0880 0.8428 0.968 0.000 0.000 0.000 0.032
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.1270 0.9174 0.052 0.948 0.000 0.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 1 0.1792 0.8263 0.916 0.084 0.000 0.000 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.5044 0.1970 0.408 0.556 0.000 0.000 0.036
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0609 0.8442 0.980 0.000 0.000 0.000 0.020
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0510 0.8696 0.016 0.000 0.000 0.984 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.3730 0.5647 0.288 0.712 0.000 0.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0798 0.9272 0.008 0.976 0.000 0.000 0.016
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.6108 0.4826 0.000 0.000 0.568 0.208 0.224
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 5 0.4718 0.0462 0.444 0.000 0.000 0.016 0.540
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.9308 0.000 1.000 0.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 5 0.1608 0.8251 0.000 0.000 0.000 0.072 0.928
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.0290 0.8286 0.000 0.000 0.992 0.000 0.008
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.5064 0.6458 0.680 0.232 0.000 0.000 0.088
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0671 0.8691 0.016 0.000 0.000 0.980 0.004
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0880 0.8428 0.968 0.000 0.000 0.000 0.032
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.9308 0.000 1.000 0.000 0.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.9308 0.000 1.000 0.000 0.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 5 0.4169 0.7600 0.000 0.000 0.116 0.100 0.784
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.1628 0.8376 0.936 0.056 0.000 0.000 0.008
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0880 0.8428 0.968 0.000 0.000 0.000 0.032
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.9308 0.000 1.000 0.000 0.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 5 0.1851 0.8286 0.000 0.000 0.000 0.088 0.912
#> B3561356-5A80-4C79-B23A-D518425565FE 1 0.1956 0.8301 0.916 0.076 0.000 0.000 0.008
#> F900E9BE-2400-4451-9434-EE8BC513BA94 5 0.2390 0.7513 0.084 0.000 0.000 0.020 0.896
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 5 0.1704 0.8235 0.004 0.000 0.000 0.068 0.928
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0510 0.8696 0.016 0.000 0.000 0.984 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.3319 0.7690 0.000 0.020 0.820 0.000 0.160
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.9308 0.000 1.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0162 0.8434 0.996 0.000 0.000 0.000 0.004
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.2464 0.7864 0.096 0.000 0.000 0.888 0.016
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.1197 0.8443 0.048 0.000 0.000 0.952 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 1 0.3707 0.6076 0.716 0.284 0.000 0.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 5 0.3862 0.7771 0.000 0.000 0.104 0.088 0.808
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0880 0.8428 0.968 0.000 0.000 0.000 0.032
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.1628 0.8376 0.936 0.056 0.000 0.000 0.008
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0451 0.8679 0.004 0.000 0.000 0.988 0.008
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.9308 0.000 1.000 0.000 0.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.3727 0.7046 0.000 0.000 0.768 0.016 0.216
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.0162 0.8301 0.000 0.000 0.996 0.000 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 5 0.2699 0.674 0.032 0.028 0.004 0.000 0.888 0.048
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 6 0.2218 0.849 0.000 0.000 0.104 0.012 0.000 0.884
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.1745 0.840 0.000 0.000 0.924 0.000 0.020 0.056
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.3409 0.663 0.000 0.700 0.000 0.000 0.300 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0458 0.865 0.000 0.000 0.984 0.000 0.016 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0363 0.942 0.988 0.000 0.000 0.012 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0520 0.943 0.984 0.008 0.000 0.000 0.008 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.3345 0.729 0.000 0.000 0.776 0.000 0.020 0.204
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0260 0.830 0.000 0.992 0.000 0.000 0.008 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.3330 0.690 0.000 0.716 0.000 0.000 0.284 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 5 0.5814 0.495 0.064 0.340 0.004 0.000 0.544 0.048
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.0603 0.864 0.000 0.000 0.980 0.000 0.016 0.004
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0363 0.865 0.000 0.000 0.988 0.000 0.012 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.0458 0.865 0.000 0.000 0.984 0.000 0.016 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.886 0.000 0.000 0.000 1.000 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.830 0.000 1.000 0.000 0.000 0.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0146 0.830 0.000 0.996 0.000 0.000 0.004 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.886 0.000 0.000 0.000 1.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.830 0.000 1.000 0.000 0.000 0.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 3 0.0458 0.865 0.000 0.000 0.984 0.000 0.016 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 6 0.0363 0.922 0.000 0.000 0.000 0.012 0.000 0.988
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.3330 0.690 0.000 0.716 0.000 0.000 0.284 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.886 0.000 0.000 0.000 1.000 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.886 0.000 0.000 0.000 1.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.886 0.000 0.000 0.000 1.000 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.0458 0.865 0.000 0.000 0.984 0.000 0.016 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 1 0.5038 -0.121 0.516 0.008 0.004 0.000 0.428 0.044
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0291 0.826 0.000 0.992 0.000 0.000 0.004 0.004
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 6 0.1003 0.915 0.000 0.000 0.016 0.020 0.000 0.964
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.2823 0.692 0.000 0.000 0.000 0.796 0.000 0.204
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.886 0.000 0.000 0.000 1.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.0458 0.865 0.000 0.000 0.984 0.000 0.016 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 6 0.5343 0.105 0.000 0.000 0.108 0.408 0.000 0.484
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0458 0.865 0.000 0.000 0.984 0.000 0.016 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.830 0.000 1.000 0.000 0.000 0.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.3265 0.724 0.000 0.748 0.000 0.000 0.248 0.004
#> 692C65BB-BF32-4846-806B-01A285BED1B9 6 0.0363 0.922 0.000 0.000 0.000 0.012 0.000 0.988
#> CB925BF0-1249-4350-A175-9A4129C43B8D 6 0.0363 0.922 0.000 0.000 0.000 0.012 0.000 0.988
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.0000 0.886 0.000 0.000 0.000 1.000 0.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.0520 0.863 0.000 0.000 0.984 0.000 0.008 0.008
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.0000 0.865 0.000 0.000 1.000 0.000 0.000 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 5 0.4738 0.488 0.064 0.336 0.000 0.000 0.600 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 5 0.6322 0.354 0.064 0.032 0.324 0.000 0.532 0.048
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.1141 0.900 0.948 0.000 0.000 0.052 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 4 0.6272 0.177 0.000 0.000 0.308 0.464 0.020 0.208
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.2003 0.802 0.000 0.884 0.000 0.000 0.116 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.3374 0.724 0.000 0.000 0.772 0.000 0.020 0.208
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.4512 0.633 0.028 0.000 0.676 0.000 0.024 0.272
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0146 0.949 0.996 0.004 0.000 0.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.6305 0.229 0.000 0.000 0.448 0.324 0.020 0.208
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.3530 0.737 0.792 0.000 0.000 0.000 0.152 0.056
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.3330 0.690 0.000 0.716 0.000 0.000 0.284 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 6 0.0363 0.922 0.000 0.000 0.000 0.012 0.000 0.988
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0458 0.880 0.000 0.000 0.000 0.984 0.000 0.016
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.2637 0.819 0.000 0.008 0.872 0.000 0.024 0.096
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 4 0.5741 0.452 0.000 0.000 0.184 0.588 0.020 0.208
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.3081 0.736 0.000 0.776 0.000 0.000 0.220 0.004
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 5 0.4700 0.480 0.060 0.340 0.000 0.000 0.600 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.4627 0.248 0.000 0.000 0.532 0.012 0.020 0.436
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0881 0.860 0.000 0.008 0.972 0.000 0.012 0.008
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 4 0.5573 0.478 0.000 0.000 0.160 0.612 0.020 0.208
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0146 0.884 0.004 0.000 0.000 0.996 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.886 0.000 0.000 0.000 1.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 6 0.2250 0.860 0.000 0.000 0.092 0.020 0.000 0.888
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0547 0.933 0.980 0.000 0.000 0.000 0.000 0.020
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.3330 0.690 0.000 0.716 0.000 0.000 0.284 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.3330 0.690 0.000 0.716 0.000 0.000 0.284 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.3464 0.659 0.000 0.688 0.000 0.000 0.312 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.830 0.000 1.000 0.000 0.000 0.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0405 0.946 0.988 0.008 0.000 0.000 0.004 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 5 0.5803 0.495 0.064 0.336 0.004 0.000 0.548 0.048
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.4256 0.152 0.464 0.000 0.000 0.520 0.000 0.016
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.2048 0.795 0.000 0.000 0.000 0.880 0.000 0.120
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 6 0.0363 0.922 0.000 0.000 0.000 0.012 0.000 0.988
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 5 0.1528 0.690 0.048 0.016 0.000 0.000 0.936 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 6 0.0363 0.922 0.000 0.000 0.000 0.012 0.000 0.988
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 4 0.1387 0.847 0.000 0.000 0.000 0.932 0.000 0.068
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.3266 0.701 0.000 0.728 0.000 0.000 0.272 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 5 0.3975 0.404 0.392 0.008 0.000 0.000 0.600 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 5 0.1485 0.684 0.024 0.028 0.000 0.000 0.944 0.004
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.886 0.000 0.000 0.000 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 5 0.4428 0.361 0.032 0.388 0.000 0.000 0.580 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.2003 0.802 0.000 0.884 0.000 0.000 0.116 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.5310 0.595 0.000 0.000 0.644 0.128 0.020 0.208
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 6 0.1007 0.891 0.044 0.000 0.000 0.000 0.000 0.956
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.830 0.000 1.000 0.000 0.000 0.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 6 0.0260 0.920 0.000 0.000 0.000 0.008 0.000 0.992
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.0458 0.865 0.000 0.000 0.984 0.000 0.016 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 5 0.1675 0.684 0.032 0.024 0.000 0.000 0.936 0.008
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0291 0.885 0.004 0.000 0.000 0.992 0.000 0.004
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0291 0.948 0.992 0.004 0.000 0.000 0.004 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0291 0.826 0.000 0.992 0.000 0.000 0.004 0.004
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0508 0.828 0.000 0.984 0.000 0.000 0.012 0.004
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 6 0.2572 0.814 0.000 0.000 0.136 0.012 0.000 0.852
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0520 0.943 0.984 0.008 0.000 0.000 0.008 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0291 0.826 0.000 0.992 0.000 0.000 0.004 0.004
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 6 0.0363 0.922 0.000 0.000 0.000 0.012 0.000 0.988
#> B3561356-5A80-4C79-B23A-D518425565FE 5 0.4671 0.391 0.392 0.008 0.004 0.000 0.572 0.024
#> F900E9BE-2400-4451-9434-EE8BC513BA94 6 0.0363 0.914 0.012 0.000 0.000 0.000 0.000 0.988
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 6 0.0291 0.918 0.004 0.000 0.000 0.004 0.000 0.992
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.886 0.000 0.000 0.000 1.000 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0881 0.860 0.000 0.008 0.972 0.000 0.012 0.008
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.830 0.000 1.000 0.000 0.000 0.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0146 0.950 0.996 0.004 0.000 0.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0777 0.872 0.024 0.000 0.000 0.972 0.000 0.004
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0363 0.879 0.012 0.000 0.000 0.988 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 5 0.1713 0.692 0.044 0.028 0.000 0.000 0.928 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 6 0.2743 0.794 0.000 0.000 0.164 0.008 0.000 0.828
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.0405 0.946 0.988 0.008 0.000 0.000 0.004 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0363 0.882 0.000 0.000 0.000 0.988 0.000 0.012
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0291 0.826 0.000 0.992 0.000 0.000 0.004 0.004
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.3345 0.729 0.000 0.000 0.776 0.000 0.020 0.204
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.0000 0.865 0.000 0.000 1.000 0.000 0.000 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", "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 17548 rows and 122 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 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 0.965 0.986 0.4322 0.568 0.568
#> 3 3 1.000 0.962 0.985 0.4965 0.702 0.510
#> 4 4 0.816 0.868 0.927 0.1528 0.858 0.618
#> 5 5 0.776 0.640 0.821 0.0518 0.943 0.784
#> 6 6 0.844 0.793 0.892 0.0302 0.920 0.671
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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 2 0.0000 0.989 0.000 1.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.0000 0.975 1.000 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.975 1.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.975 1.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 2 0.0000 0.989 0.000 1.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.989 0.000 1.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 2 0.0000 0.989 0.000 1.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 2 0.0000 0.989 0.000 1.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 2 0.0000 0.989 0.000 1.000
#> 806616FE-1855-4284-9265-42842104CB21 2 0.0000 0.989 0.000 1.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.989 0.000 1.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.989 0.000 1.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.9393 0.460 0.644 0.356
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.989 0.000 1.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 2 0.0000 0.989 0.000 1.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 2 0.0000 0.989 0.000 1.000
#> 853120F0-857B-4108-9EC8-727189630C5F 2 0.0000 0.989 0.000 1.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 2 0.0000 0.989 0.000 1.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.989 0.000 1.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.989 0.000 1.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 2 0.0000 0.989 0.000 1.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.989 0.000 1.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 2 0.0000 0.989 0.000 1.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.975 1.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.989 0.000 1.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 2 0.9044 0.518 0.320 0.680
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.1184 0.964 0.984 0.016
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.975 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 2 0.0000 0.989 0.000 1.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.0000 0.989 0.000 1.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.989 0.000 1.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 2 0.0672 0.982 0.008 0.992
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.975 1.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 2 0.0000 0.989 0.000 1.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 2 0.0000 0.989 0.000 1.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 2 0.0000 0.989 0.000 1.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.0672 0.970 0.992 0.008
#> 50D620F3-5C52-42FB-89A1-6840A7444647 2 0.0000 0.989 0.000 1.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.989 0.000 1.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.3114 0.929 0.944 0.056
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.989 0.000 1.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.975 1.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.975 1.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 2 0.0000 0.989 0.000 1.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.0000 0.989 0.000 1.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.0000 0.989 0.000 1.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.989 0.000 1.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0000 0.989 0.000 1.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 2 0.0000 0.989 0.000 1.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 2 0.0000 0.989 0.000 1.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.989 0.000 1.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 2 0.0000 0.989 0.000 1.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 2 0.0000 0.989 0.000 1.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.975 1.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 2 0.0000 0.989 0.000 1.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.975 1.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.989 0.000 1.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.975 1.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.975 1.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.0000 0.989 0.000 1.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 2 0.0000 0.989 0.000 1.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.989 0.000 1.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.989 0.000 1.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 1 0.0000 0.975 1.000 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.0000 0.989 0.000 1.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 2 0.0000 0.989 0.000 1.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 2 0.0000 0.989 0.000 1.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 2 0.1184 0.974 0.016 0.984
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.0000 0.975 1.000 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0000 0.975 1.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.989 0.000 1.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.989 0.000 1.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.989 0.000 1.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.989 0.000 1.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 2 0.0000 0.989 0.000 1.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.989 0.000 1.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.975 1.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.975 1.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.975 1.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.0672 0.982 0.008 0.992
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.975 1.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.0000 0.975 1.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 2 0.2778 0.942 0.048 0.952
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.989 0.000 1.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.0000 0.989 0.000 1.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0000 0.989 0.000 1.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.2423 0.943 0.960 0.040
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 2 0.0000 0.989 0.000 1.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.989 0.000 1.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.989 0.000 1.000
#> F25A7521-2596-4D60-BABE-862023C40D40 2 0.0000 0.989 0.000 1.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.975 1.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.989 0.000 1.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.975 1.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.0000 0.975 1.000 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.0000 0.975 1.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 2 0.0000 0.989 0.000 1.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.975 1.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.989 0.000 1.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.989 0.000 1.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.0000 0.975 1.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 2 0.0000 0.989 0.000 1.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.8955 0.558 0.688 0.312
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.989 0.000 1.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.975 1.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0000 0.989 0.000 1.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.975 1.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.975 1.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 2 0.0000 0.989 0.000 1.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.0000 0.989 0.000 1.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.989 0.000 1.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 2 0.0000 0.989 0.000 1.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 2 0.9427 0.424 0.360 0.640
#> F205F9FC-F2D5-4164-9A40-1279647F900B 2 0.5059 0.867 0.112 0.888
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0000 0.989 0.000 1.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0000 0.975 1.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.975 1.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 2 0.0000 0.989 0.000 1.000
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.4815 0.878 0.896 0.104
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.989 0.000 1.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 2 0.0000 0.989 0.000 1.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.0000 0.989 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 1 0.0000 0.960 1.000 0.000 0.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 3 0.0000 0.989 0.000 0.000 1.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 3 0.0000 0.989 0.000 0.000 1.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 3 0.0000 0.989 0.000 0.000 1.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 2 0.0000 0.996 0.000 1.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0000 0.996 0.000 1.000 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 2 0.0000 0.996 0.000 1.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 1 0.0000 0.960 1.000 0.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 1 0.0000 0.960 1.000 0.000 0.000
#> 806616FE-1855-4284-9265-42842104CB21 2 0.0237 0.993 0.004 0.996 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0000 0.996 0.000 1.000 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0000 0.996 0.000 1.000 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 1 0.0000 0.960 1.000 0.000 0.000
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.0000 0.996 0.000 1.000 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 2 0.0000 0.996 0.000 1.000 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 2 0.0000 0.996 0.000 1.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 2 0.0000 0.996 0.000 1.000 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 1 0.0000 0.960 1.000 0.000 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.0000 0.996 0.000 1.000 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0000 0.996 0.000 1.000 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 1 0.0000 0.960 1.000 0.000 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.0000 0.996 0.000 1.000 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 2 0.1753 0.949 0.048 0.952 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 3 0.0000 0.989 0.000 0.000 1.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.0000 0.996 0.000 1.000 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 1 0.0000 0.960 1.000 0.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 1 0.0000 0.960 1.000 0.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 1 0.0000 0.960 1.000 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 2 0.0000 0.996 0.000 1.000 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.0000 0.996 0.000 1.000 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.0000 0.996 0.000 1.000 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 1 0.0000 0.960 1.000 0.000 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 3 0.0000 0.989 0.000 0.000 1.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 1 0.0000 0.960 1.000 0.000 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 1 0.0000 0.960 1.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 2 0.0000 0.996 0.000 1.000 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 3 0.4062 0.806 0.164 0.000 0.836
#> 50D620F3-5C52-42FB-89A1-6840A7444647 2 0.0000 0.996 0.000 1.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.0000 0.996 0.000 1.000 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 1 0.0000 0.960 1.000 0.000 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0000 0.996 0.000 1.000 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 3 0.0000 0.989 0.000 0.000 1.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 3 0.0000 0.989 0.000 0.000 1.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 1 0.0000 0.960 1.000 0.000 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 2 0.0000 0.996 0.000 1.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.0000 0.996 0.000 1.000 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.0000 0.996 0.000 1.000 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0000 0.996 0.000 1.000 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 1 0.0000 0.960 1.000 0.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 1 0.0000 0.960 1.000 0.000 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.996 0.000 1.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 1 0.0237 0.956 0.996 0.000 0.004
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 2 0.0000 0.996 0.000 1.000 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 3 0.0000 0.989 0.000 0.000 1.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 1 0.6295 0.145 0.528 0.472 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 3 0.0000 0.989 0.000 0.000 1.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.0000 0.996 0.000 1.000 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 3 0.0000 0.989 0.000 0.000 1.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 1 0.0000 0.960 1.000 0.000 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.0000 0.996 0.000 1.000 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 1 0.0000 0.960 1.000 0.000 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.0000 0.996 0.000 1.000 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0000 0.996 0.000 1.000 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.0000 0.989 0.000 0.000 1.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 2 0.0000 0.996 0.000 1.000 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 1 0.0000 0.960 1.000 0.000 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 1 0.0000 0.960 1.000 0.000 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 1 0.0000 0.960 1.000 0.000 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 3 0.0000 0.989 0.000 0.000 1.000
#> 352471DC-A881-4EA8-B646-EB1200291893 3 0.0592 0.979 0.012 0.000 0.988
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0000 0.996 0.000 1.000 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0000 0.996 0.000 1.000 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.996 0.000 1.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.0000 0.996 0.000 1.000 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 1 0.0000 0.960 1.000 0.000 0.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.0000 0.996 0.000 1.000 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 1 0.0000 0.960 1.000 0.000 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 1 0.0000 0.960 1.000 0.000 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 3 0.0000 0.989 0.000 0.000 1.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.2878 0.895 0.000 0.904 0.096
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 3 0.0000 0.989 0.000 0.000 1.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 3 0.3116 0.878 0.108 0.000 0.892
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 1 0.0000 0.960 1.000 0.000 0.000
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.0000 0.996 0.000 1.000 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.1529 0.956 0.040 0.960 0.000
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0000 0.996 0.000 1.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 1 0.0000 0.960 1.000 0.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 1 0.0000 0.960 1.000 0.000 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0000 0.996 0.000 1.000 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.996 0.000 1.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 1 0.6008 0.430 0.628 0.372 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 3 0.0000 0.989 0.000 0.000 1.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.0000 0.996 0.000 1.000 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 3 0.0000 0.989 0.000 0.000 1.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.0000 0.989 0.000 0.000 1.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 3 0.0000 0.989 0.000 0.000 1.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 1 0.0000 0.960 1.000 0.000 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 3 0.0000 0.989 0.000 0.000 1.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.0000 0.996 0.000 1.000 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.0000 0.996 0.000 1.000 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.0000 0.989 0.000 0.000 1.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 1 0.0000 0.960 1.000 0.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 1 0.2066 0.902 0.940 0.000 0.060
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.0000 0.996 0.000 1.000 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 3 0.0000 0.989 0.000 0.000 1.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0237 0.993 0.004 0.996 0.000
#> F900E9BE-2400-4451-9434-EE8BC513BA94 3 0.0000 0.989 0.000 0.000 1.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 3 0.0000 0.989 0.000 0.000 1.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 1 0.0000 0.960 1.000 0.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 2 0.0000 0.996 0.000 1.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.0000 0.996 0.000 1.000 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 1 0.0000 0.960 1.000 0.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 1 0.0000 0.960 1.000 0.000 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 1 0.0000 0.960 1.000 0.000 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0000 0.996 0.000 1.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 3 0.0000 0.989 0.000 0.000 1.000
#> 12F54761-4F68-4181-8421-88EA858902FC 3 0.0000 0.989 0.000 0.000 1.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 1 0.0237 0.956 0.996 0.004 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 1 0.0000 0.960 1.000 0.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.0000 0.996 0.000 1.000 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 1 0.6111 0.371 0.604 0.396 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 2 0.0000 0.996 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.3907 0.7379 0.768 0.232 0.000 0.000
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.0000 0.9515 1.000 0.000 0.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.9515 1.000 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0188 0.8663 0.000 0.996 0.004 0.000
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.0817 0.9520 0.000 0.024 0.000 0.976
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.0817 0.8735 0.000 0.976 0.024 0.000
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0592 0.8710 0.000 0.984 0.016 0.000
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.0921 0.9511 0.028 0.000 0.000 0.972
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.4164 0.7894 0.000 0.736 0.264 0.000
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.2647 0.8714 0.000 0.880 0.120 0.000
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.0817 0.8735 0.000 0.976 0.024 0.000
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.3311 0.8528 0.000 0.828 0.172 0.000
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 2 0.7169 0.4697 0.000 0.508 0.344 0.148
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.9515 1.000 0.000 0.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.1302 0.8775 0.000 0.956 0.044 0.000
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.3649 0.8357 0.000 0.796 0.204 0.000
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.3975 0.8102 0.000 0.760 0.240 0.000
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.9515 1.000 0.000 0.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 1 0.5543 0.3904 0.612 0.000 0.360 0.028
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.2589 0.8723 0.000 0.884 0.116 0.000
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0188 0.8663 0.000 0.996 0.004 0.000
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.9515 1.000 0.000 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.9515 1.000 0.000 0.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.1118 0.8701 0.000 0.036 0.964 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 3 0.0707 0.8843 0.000 0.020 0.980 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.1940 0.8780 0.000 0.924 0.076 0.000
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.4382 0.7521 0.000 0.704 0.296 0.000
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.2345 0.8313 0.000 0.000 0.900 0.100
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.0000 0.8642 0.000 1.000 0.000 0.000
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.0524 0.8927 0.004 0.000 0.988 0.008
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0188 0.8963 0.004 0.000 0.996 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.0000 0.9515 1.000 0.000 0.000 0.000
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.3710 0.7439 0.000 0.004 0.804 0.192
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.9515 1.000 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.1867 0.8783 0.000 0.928 0.072 0.000
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.9515 1.000 0.000 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0188 0.9715 0.004 0.000 0.000 0.996
#> 84E18629-1B13-4696-8E54-121ABE469CD2 3 0.3942 0.5607 0.000 0.236 0.764 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.3074 0.7880 0.000 0.000 0.848 0.152
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.4222 0.7810 0.000 0.728 0.272 0.000
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.0188 0.8663 0.000 0.996 0.004 0.000
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.4955 0.1638 0.444 0.000 0.556 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.0188 0.8960 0.000 0.004 0.996 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.4855 0.3116 0.000 0.000 0.600 0.400
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.2530 0.8537 0.888 0.000 0.112 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.1211 0.9191 0.960 0.000 0.000 0.040
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.1557 0.8781 0.000 0.944 0.056 0.000
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.0592 0.8714 0.000 0.984 0.016 0.000
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.0000 0.8642 0.000 1.000 0.000 0.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.3024 0.8624 0.000 0.852 0.148 0.000
#> D891BCA1-0323-4277-BAF7-6F505377EA45 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.4382 0.7521 0.000 0.704 0.296 0.000
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.9515 1.000 0.000 0.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.1302 0.8422 0.044 0.956 0.000 0.000
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.9515 1.000 0.000 0.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.1557 0.9039 0.944 0.000 0.000 0.056
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.2530 0.8758 0.100 0.004 0.000 0.896
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.1118 0.8758 0.000 0.964 0.036 0.000
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.4295 0.6962 0.000 0.752 0.008 0.240
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.0000 0.8642 0.000 1.000 0.000 0.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.3400 0.8493 0.000 0.820 0.180 0.000
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.0000 0.8642 0.000 1.000 0.000 0.000
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.0188 0.8968 0.000 0.000 0.996 0.004
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.9515 1.000 0.000 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.2530 0.8733 0.000 0.888 0.112 0.000
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.9515 1.000 0.000 0.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 3 0.4994 0.0112 0.480 0.000 0.520 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.3726 0.7643 0.788 0.212 0.000 0.000
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.0000 0.9515 1.000 0.000 0.000 0.000
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.4134 0.7934 0.000 0.740 0.260 0.000
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.4134 0.7934 0.000 0.740 0.260 0.000
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 1 0.2469 0.8554 0.892 0.000 0.108 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 4 0.0188 0.9714 0.000 0.004 0.000 0.996
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.4990 0.4499 0.352 0.008 0.000 0.640
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.3688 0.8333 0.000 0.792 0.208 0.000
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.9515 1.000 0.000 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.5585 0.7792 0.000 0.712 0.204 0.084
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.9515 1.000 0.000 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.9515 1.000 0.000 0.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.1474 0.8778 0.000 0.948 0.052 0.000
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.0000 0.8642 0.000 1.000 0.000 0.000
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0000 0.9515 1.000 0.000 0.000 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0000 0.9515 1.000 0.000 0.000 0.000
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 4 0.3688 0.7134 0.000 0.208 0.000 0.792
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.9748 0.000 0.000 0.000 1.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.4040 0.8037 0.000 0.752 0.248 0.000
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.0000 0.8982 0.000 0.000 1.000 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 3 0.0000 0.8982 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
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.1282 0.9470 0.000 0.004 0.000 0.952 0.044
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 1 0.6707 0.0451 0.388 0.368 0.000 0.000 0.244
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.2286 0.7959 0.888 0.000 0.108 0.000 0.004
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.8912 1.000 0.000 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.2054 0.6486 0.000 0.028 0.920 0.000 0.052
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 2 0.0963 0.6482 0.000 0.964 0.000 0.000 0.036
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.0324 0.6657 0.000 0.004 0.992 0.000 0.004
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.0000 0.9792 0.000 0.000 0.000 1.000 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.0290 0.9758 0.000 0.008 0.000 0.992 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0912 0.6656 0.000 0.012 0.972 0.000 0.016
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 2 0.3074 0.5983 0.000 0.804 0.000 0.000 0.196
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 2 0.0510 0.6494 0.000 0.984 0.000 0.000 0.016
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.0693 0.9708 0.008 0.000 0.000 0.980 0.012
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.4973 0.1372 0.000 0.632 0.048 0.000 0.320
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.4307 -0.0513 0.000 0.000 0.496 0.000 0.504
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.1300 0.6641 0.000 0.016 0.956 0.000 0.028
#> 853120F0-857B-4108-9EC8-727189630C5F 3 0.4450 -0.0645 0.000 0.004 0.508 0.000 0.488
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0162 0.9785 0.000 0.000 0.004 0.996 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.2189 0.6126 0.000 0.904 0.012 0.000 0.084
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 2 0.1792 0.6391 0.000 0.916 0.000 0.000 0.084
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0162 0.9785 0.000 0.000 0.004 0.996 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.4398 0.3878 0.000 0.720 0.040 0.000 0.240
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 5 0.4404 0.5803 0.000 0.292 0.024 0.000 0.684
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.8912 1.000 0.000 0.000 0.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.2848 0.5575 0.000 0.840 0.004 0.000 0.156
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0000 0.9792 0.000 0.000 0.000 1.000 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0000 0.9792 0.000 0.000 0.000 1.000 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.9792 0.000 0.000 0.000 1.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 3 0.4306 -0.0443 0.000 0.000 0.508 0.000 0.492
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.5380 0.4753 0.000 0.712 0.040 0.072 0.176
#> F798E986-79BB-48FD-8514-95571EDB594B 5 0.5351 0.3136 0.000 0.464 0.052 0.000 0.484
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.0162 0.9786 0.000 0.000 0.000 0.996 0.004
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0162 0.8901 0.996 0.000 0.000 0.000 0.004
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.0992 0.9652 0.000 0.000 0.008 0.968 0.024
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.9792 0.000 0.000 0.000 1.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.4450 -0.0237 0.000 0.004 0.488 0.000 0.508
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 3 0.7696 0.0841 0.376 0.000 0.388 0.108 0.128
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.1544 0.6482 0.000 0.000 0.932 0.000 0.068
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.1981 0.6233 0.000 0.920 0.016 0.000 0.064
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.0162 0.9784 0.004 0.000 0.000 0.996 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 2 0.0404 0.6492 0.000 0.988 0.000 0.000 0.012
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.8912 1.000 0.000 0.000 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0000 0.8912 1.000 0.000 0.000 0.000 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.0671 0.9707 0.000 0.000 0.016 0.980 0.004
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.5543 0.2474 0.000 0.224 0.640 0.000 0.136
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 5 0.5516 0.5943 0.000 0.220 0.136 0.000 0.644
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 2 0.2753 0.6228 0.000 0.856 0.008 0.000 0.136
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 5 0.5215 0.5233 0.000 0.372 0.052 0.000 0.576
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 4 0.0000 0.9792 0.000 0.000 0.000 1.000 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.4719 0.5467 0.000 0.016 0.720 0.228 0.036
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 2 0.3480 0.5713 0.000 0.752 0.000 0.000 0.248
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.1124 0.6651 0.000 0.000 0.960 0.036 0.004
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.0771 0.6665 0.000 0.004 0.976 0.000 0.020
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.2124 0.8302 0.900 0.000 0.000 0.004 0.096
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 3 0.8488 -0.0227 0.000 0.188 0.320 0.272 0.220
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0000 0.8912 1.000 0.000 0.000 0.000 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.3391 0.5149 0.000 0.800 0.012 0.000 0.188
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.8912 1.000 0.000 0.000 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0703 0.9657 0.024 0.000 0.000 0.976 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 5 0.5921 0.5690 0.000 0.296 0.136 0.000 0.568
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.5328 0.4436 0.000 0.016 0.604 0.344 0.036
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.5359 -0.1830 0.000 0.532 0.056 0.000 0.412
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 2 0.1270 0.6463 0.000 0.948 0.000 0.000 0.052
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.3690 0.5755 0.200 0.000 0.780 0.000 0.020
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.3840 0.5570 0.000 0.116 0.808 0.000 0.076
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.4276 0.4104 0.000 0.000 0.616 0.380 0.004
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.0771 0.9705 0.000 0.000 0.004 0.976 0.020
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0162 0.9785 0.000 0.000 0.004 0.996 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.4449 0.0316 0.512 0.000 0.484 0.000 0.004
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.0609 0.8778 0.980 0.000 0.000 0.020 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 2 0.0404 0.6463 0.000 0.988 0.000 0.000 0.012
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.1270 0.6368 0.000 0.948 0.000 0.000 0.052
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 2 0.3534 0.5659 0.000 0.744 0.000 0.000 0.256
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.4042 0.4522 0.000 0.756 0.032 0.000 0.212
#> D891BCA1-0323-4277-BAF7-6F505377EA45 4 0.0290 0.9772 0.000 0.000 0.000 0.992 0.008
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 5 0.5264 0.4960 0.000 0.392 0.052 0.000 0.556
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.0162 0.9784 0.004 0.000 0.000 0.996 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0290 0.9770 0.008 0.000 0.000 0.992 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.8912 1.000 0.000 0.000 0.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 2 0.4477 0.5326 0.040 0.708 0.000 0.000 0.252
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0162 0.8901 0.996 0.000 0.000 0.000 0.004
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.1121 0.8560 0.956 0.000 0.000 0.044 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.2969 0.8472 0.020 0.000 0.000 0.852 0.128
#> 969A8063-FE1C-426C-821D-BDC714F1E385 2 0.3305 0.5850 0.000 0.776 0.000 0.000 0.224
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.5373 0.2818 0.000 0.652 0.000 0.236 0.112
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 2 0.3586 0.5599 0.000 0.736 0.000 0.000 0.264
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.0000 0.9792 0.000 0.000 0.000 1.000 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0162 0.9785 0.000 0.000 0.004 0.996 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.4973 -0.3157 0.000 0.496 0.020 0.004 0.480
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 2 0.3395 0.5785 0.000 0.764 0.000 0.000 0.236
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.2792 0.6486 0.000 0.004 0.884 0.072 0.040
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.8912 1.000 0.000 0.000 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.1549 0.6347 0.000 0.944 0.016 0.000 0.040
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0000 0.8912 1.000 0.000 0.000 0.000 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 1 0.6024 0.1669 0.512 0.000 0.364 0.000 0.124
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.5946 0.5013 0.592 0.184 0.000 0.000 0.224
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0162 0.9787 0.000 0.000 0.000 0.996 0.004
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.2338 0.8176 0.884 0.000 0.000 0.004 0.112
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.5176 -0.0458 0.000 0.572 0.048 0.000 0.380
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.5350 -0.3309 0.000 0.488 0.052 0.000 0.460
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.4341 0.2454 0.404 0.000 0.592 0.000 0.004
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 4 0.0404 0.9729 0.000 0.012 0.000 0.988 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.3617 0.8114 0.044 0.004 0.000 0.824 0.128
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.5291 -0.3026 0.000 0.496 0.048 0.000 0.456
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.8912 1.000 0.000 0.000 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 5 0.5488 0.4757 0.000 0.396 0.020 0.032 0.552
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.8912 1.000 0.000 0.000 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0000 0.8912 1.000 0.000 0.000 0.000 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0000 0.9792 0.000 0.000 0.000 1.000 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.3532 0.5854 0.000 0.092 0.832 0.000 0.076
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.1251 0.6388 0.000 0.956 0.008 0.000 0.036
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.0000 0.9792 0.000 0.000 0.000 1.000 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.0404 0.9762 0.000 0.000 0.000 0.988 0.012
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.0404 0.9762 0.000 0.000 0.000 0.988 0.012
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 2 0.3508 0.5684 0.000 0.748 0.000 0.000 0.252
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0162 0.8901 0.996 0.000 0.000 0.000 0.004
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0566 0.8843 0.984 0.000 0.000 0.004 0.012
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 4 0.1341 0.9230 0.000 0.056 0.000 0.944 0.000
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0000 0.9792 0.000 0.000 0.000 1.000 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.5206 -0.1943 0.000 0.528 0.044 0.000 0.428
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.0865 0.6616 0.000 0.004 0.972 0.000 0.024
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.6118 0.4464 0.000 0.164 0.288 0.000 0.548
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> F9537DC1-770E-4CE2-9D67-CCAABCF60931 4 0.1938 0.928 0.000 0.000 0.008 0.920 0.020 0.052
#> 7CBD6124-AB7E-46AD-8273-FA826F8F067F 6 0.3636 0.330 0.320 0.000 0.000 0.000 0.004 0.676
#> 203691F1-8D53-4E50-8B64-56D2E0D04208 1 0.3023 0.695 0.768 0.000 0.232 0.000 0.000 0.000
#> 604AF3D4-BCBE-4C9F-9FD5-85C65EEE3E01 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> 1304CB8A-91FA-46E5-A4E4-F0B6DD3EF838 3 0.0508 0.817 0.000 0.012 0.984 0.000 0.004 0.000
#> 0B261D94-FFEC-42D3-B17F-15FD25A46422 6 0.3288 0.535 0.000 0.276 0.000 0.000 0.000 0.724
#> 9264567D-4524-46AF-A851-C091C3CD76CF 3 0.1411 0.811 0.000 0.004 0.936 0.000 0.060 0.000
#> 45EAD449-C59A-463E-880A-C375CDD039BA 4 0.0363 0.970 0.000 0.000 0.000 0.988 0.012 0.000
#> 03EEEE15-9BAF-429A-B6D0-0BD2FB6EB3B5 4 0.0260 0.971 0.000 0.000 0.000 0.992 0.008 0.000
#> 806616FE-1855-4284-9265-42842104CB21 3 0.0405 0.817 0.000 0.004 0.988 0.000 0.008 0.000
#> 108CF4F0-31D6-4C2C-984B-5AAF0829CED7 6 0.0790 0.779 0.000 0.032 0.000 0.000 0.000 0.968
#> B19B0DC5-14B3-427B-94D7-082F3DC614C9 6 0.3547 0.418 0.000 0.332 0.000 0.000 0.000 0.668
#> D9FD8955-3006-45CA-BC9A-A8BAF18264FE 4 0.0603 0.966 0.000 0.000 0.000 0.980 0.004 0.016
#> A2A46B51-FF44-45C0-B886-12313AC0FC0A 2 0.3897 0.640 0.000 0.696 0.000 0.000 0.024 0.280
#> 2FBB0C34-2E4D-4396-897D-990625749EFB 5 0.2263 0.918 0.000 0.056 0.048 0.000 0.896 0.000
#> 96E78E9B-6BBC-4C06-9D0F-DAC051828DA3 3 0.0603 0.818 0.000 0.004 0.980 0.000 0.016 0.000
#> 853120F0-857B-4108-9EC8-727189630C5F 5 0.2134 0.920 0.000 0.052 0.044 0.000 0.904 0.000
#> EF9A5BA0-F672-48F0-8F0A-8E7B6A75B9E5 4 0.0146 0.971 0.000 0.000 0.000 0.996 0.004 0.000
#> FD3F7EF7-3C82-49BB-ABC1-179CAF03B3E7 2 0.3857 0.301 0.000 0.532 0.000 0.000 0.000 0.468
#> DD14F103-81DC-482F-BE77-B5DC48C59DD7 6 0.2527 0.695 0.000 0.168 0.000 0.000 0.000 0.832
#> 8DFD6709-70F8-4BC0-941C-3B673F86CF29 4 0.0260 0.972 0.000 0.000 0.000 0.992 0.008 0.000
#> F6DBC40E-0AE2-453F-B1D5-1BB0453C19D4 2 0.2838 0.741 0.000 0.808 0.004 0.000 0.000 0.188
#> BA15FA9A-CE07-46A9-A42C-D33AB54B4CBF 2 0.1531 0.721 0.000 0.928 0.004 0.000 0.068 0.000
#> F5A814F6-E824-4DB2-8497-4B99E151D450 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> C86373C0-AC79-4748-A7F1-B63DA3A40055 2 0.3151 0.693 0.000 0.748 0.000 0.000 0.000 0.252
#> CE6BF7C2-5006-4148-9EAB-4054DD052977 4 0.0146 0.971 0.000 0.000 0.000 0.996 0.004 0.000
#> D1E7B413-20DD-466B-9E97-E72CCFF692A7 4 0.0260 0.971 0.000 0.000 0.000 0.992 0.008 0.000
#> 4496EE84-2C36-413B-A328-A5B598A6C387 4 0.0000 0.972 0.000 0.000 0.000 1.000 0.000 0.000
#> A553770E-41B0-4BD7-BA33-ED0A33DD8529 5 0.2066 0.920 0.000 0.052 0.040 0.000 0.908 0.000
#> F50803F9-FEF7-444A-B585-7082DCA1D11A 2 0.5254 0.602 0.000 0.660 0.028 0.008 0.072 0.232
#> F798E986-79BB-48FD-8514-95571EDB594B 2 0.1219 0.762 0.000 0.948 0.004 0.000 0.000 0.048
#> BB2707B0-F108-4076-A0AA-F10385BB41CB 4 0.0146 0.971 0.000 0.000 0.000 0.996 0.004 0.000
#> F5940915-4123-49B3-95EE-4A0412BE8C30 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> FB377B95-88DD-4C3B-9C5D-A552D9C2B4AB 4 0.2189 0.917 0.000 0.032 0.004 0.904 0.060 0.000
#> 2618AF36-2C3D-459B-BBBB-ACB23BCC3C1B 4 0.0000 0.972 0.000 0.000 0.000 1.000 0.000 0.000
#> AA403EC3-FD44-4247-B06D-AEF415391E46 5 0.2058 0.920 0.000 0.056 0.036 0.000 0.908 0.000
#> 2E053273-F46D-4677-8BE9-9CE90D19D359 5 0.3048 0.798 0.028 0.000 0.012 0.116 0.844 0.000
#> 50D620F3-5C52-42FB-89A1-6840A7444647 3 0.2597 0.737 0.000 0.000 0.824 0.000 0.176 0.000
#> CCF8CEFA-6676-4B41-A86E-AC0CBF30D2DD 2 0.3838 0.354 0.000 0.552 0.000 0.000 0.000 0.448
#> 37A58DA6-AEE8-4279-9738-FCBB1B92AA3E 4 0.0146 0.971 0.000 0.000 0.000 0.996 0.004 0.000
#> 86DD14A6-828B-4AF1-8032-63569F2759F9 6 0.3634 0.340 0.000 0.356 0.000 0.000 0.000 0.644
#> 692C65BB-BF32-4846-806B-01A285BED1B9 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> CB925BF0-1249-4350-A175-9A4129C43B8D 1 0.0146 0.938 0.996 0.000 0.000 0.000 0.004 0.000
#> 368F63CC-493E-45CA-A846-8A4D943C4928 4 0.0909 0.961 0.000 0.000 0.012 0.968 0.020 0.000
#> 4EBCD716-0014-4C79-843D-6E3F5AA743E1 3 0.3717 0.322 0.000 0.384 0.616 0.000 0.000 0.000
#> C7C6FBAA-4A63-4B32-9B43-306F69E1867E 2 0.3756 0.280 0.000 0.600 0.000 0.000 0.400 0.000
#> B9084CCA-6AB7-44D2-94BB-EF6D7AC7985F 6 0.1814 0.748 0.000 0.100 0.000 0.000 0.000 0.900
#> 01680E9C-60C7-442F-8CE9-B4890EFA8C44 2 0.0405 0.744 0.000 0.988 0.000 0.000 0.008 0.004
#> F84FBB47-11F2-45D5-9C5F-7C80AB0C9FBB 4 0.0146 0.971 0.000 0.000 0.000 0.996 0.004 0.000
#> 06E30C6B-2F20-4619-9775-88B3B55602C6 3 0.2195 0.788 0.000 0.012 0.904 0.068 0.016 0.000
#> BF5BE22A-239B-4DB8-9E69-34F89C9CDA22 6 0.0146 0.780 0.000 0.004 0.000 0.000 0.000 0.996
#> C7EBFCFA-8415-4C72-A436-BD36391DC066 3 0.1779 0.810 0.000 0.000 0.920 0.016 0.064 0.000
#> AE2B25DD-F8AE-4B1B-B40A-E1D6784D019C 3 0.1285 0.813 0.000 0.004 0.944 0.000 0.052 0.000
#> D5F0EEAA-18A5-4CA5-8554-1052AADCB53E 1 0.1812 0.881 0.912 0.000 0.000 0.008 0.000 0.080
#> B6656C31-B782-4FD3-B07D-ABF664BA8915 2 0.3776 0.601 0.000 0.756 0.196 0.048 0.000 0.000
#> B5474EEB-D585-4668-959C-38F240F55BC2 1 0.0146 0.938 0.996 0.000 0.000 0.000 0.004 0.000
#> EC595B05-067F-47E7-A3B1-6C2DD73A2AB2 2 0.2454 0.752 0.000 0.840 0.000 0.000 0.000 0.160
#> A533C39D-CE42-42AD-92AD-549157A43139 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> D93F1B3C-1502-46FD-8EEE-973CF8BB3724 4 0.0603 0.964 0.016 0.000 0.000 0.980 0.004 0.000
#> 84E18629-1B13-4696-8E54-121ABE469CD2 2 0.1700 0.723 0.000 0.916 0.080 0.000 0.004 0.000
#> 7219A24C-306D-4C24-98DA-8F194D0B809B 3 0.4163 0.502 0.000 0.008 0.656 0.320 0.016 0.000
#> 1E91EE61-6412-44DD-B13C-910244D4D4D4 2 0.2243 0.765 0.000 0.880 0.004 0.000 0.004 0.112
#> 9E34636D-C99E-4AF2-A417-8AEAC8084144 6 0.2823 0.651 0.000 0.204 0.000 0.000 0.000 0.796
#> 3288DA84-360C-452F-AB9A-7148CC18AE95 3 0.1176 0.808 0.024 0.000 0.956 0.000 0.020 0.000
#> B17D9FCC-C687-4C04-BACD-846B822E1FE7 3 0.2214 0.760 0.000 0.096 0.888 0.000 0.016 0.000
#> 282EDEC3-3F81-45D3-BF27-F1242ED966CC 3 0.3674 0.571 0.000 0.000 0.716 0.268 0.016 0.000
#> 446EC9FF-4F10-40BC-8383-736FD7BCF55D 4 0.1511 0.945 0.000 0.012 0.004 0.940 0.044 0.000
#> E406ADC2-DB17-4F81-B4A7-FF6FAEF225EA 4 0.0146 0.972 0.000 0.000 0.000 0.996 0.004 0.000
#> 6A7BAB3B-752A-4671-A166-90643D5B29AF 1 0.3777 0.684 0.752 0.000 0.216 0.020 0.012 0.000
#> 352471DC-A881-4EA8-B646-EB1200291893 1 0.1267 0.885 0.940 0.000 0.000 0.060 0.000 0.000
#> F779417A-9E29-4B27-BEA3-B23273A66021 6 0.3756 0.187 0.000 0.400 0.000 0.000 0.000 0.600
#> BD9C0D6B-83A9-4B4B-AD84-50EAD8409DFB 2 0.3810 0.414 0.000 0.572 0.000 0.000 0.000 0.428
#> 4401759F-1FB6-4589-9AB6-F582EA99AAF2 6 0.0000 0.778 0.000 0.000 0.000 0.000 0.000 1.000
#> 73D21B57-EA30-4D90-B667-AC95C8A66264 2 0.2793 0.734 0.000 0.800 0.000 0.000 0.000 0.200
#> D891BCA1-0323-4277-BAF7-6F505377EA45 4 0.0405 0.970 0.000 0.000 0.000 0.988 0.008 0.004
#> 5E367AE1-8F91-4E28-9908-F66CD59CF8E4 2 0.1116 0.744 0.000 0.960 0.004 0.000 0.028 0.008
#> A314C4E6-B245-4F10-A555-50B9B819040D 4 0.0363 0.970 0.000 0.000 0.000 0.988 0.012 0.000
#> 037B1CEE-3B36-4EB8-B4AD-BFFC1972D06C 4 0.0291 0.971 0.004 0.000 0.000 0.992 0.004 0.000
#> BC4C8A01-362C-4772-8399-4803AD32D6FA 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> C7676B99-E211-43C4-8D07-1F91B8EE1F31 6 0.0520 0.772 0.008 0.000 0.008 0.000 0.000 0.984
#> 0FBDDB5D-8C3F-4A12-B834-72F2C02202EA 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> 47B91ADC-2079-40C1-927B-F9C38297AF80 1 0.1765 0.843 0.904 0.000 0.000 0.096 0.000 0.000
#> E11DB312-6ACB-4787-8A2B-AFD814F82900 4 0.3165 0.847 0.000 0.000 0.008 0.836 0.040 0.116
#> 969A8063-FE1C-426C-821D-BDC714F1E385 6 0.1080 0.779 0.000 0.032 0.004 0.000 0.004 0.960
#> 1816CF84-8DAF-4CC9-AD93-F3C5439082E7 2 0.3975 0.679 0.000 0.716 0.000 0.040 0.000 0.244
#> 6AE31A62-2F65-40FC-AE48-72B822AE64CA 6 0.0000 0.778 0.000 0.000 0.000 0.000 0.000 1.000
#> 6F7DB73C-FE46-402C-9001-DC2005278069 4 0.0146 0.971 0.000 0.000 0.000 0.996 0.004 0.000
#> 16F2C47A-F5A6-483D-86E4-36F9CF6F35EB 4 0.0146 0.972 0.000 0.000 0.000 0.996 0.004 0.000
#> C8B6FE79-5718-46C2-8D75-6D91539EFA6B 2 0.0865 0.758 0.000 0.964 0.000 0.000 0.000 0.036
#> 53A37AC7-4504-4176-9B38-A3B2A7C6BE5B 6 0.0146 0.780 0.000 0.004 0.000 0.000 0.000 0.996
#> F25A7521-2596-4D60-BABE-862023C40D40 3 0.5496 0.472 0.000 0.008 0.592 0.240 0.160 0.000
#> 1F87CAF7-FDDF-4C01-817A-361EADB9EAB4 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> 158EF652-E08F-4671-B2E1-AE48ACDD7E31 2 0.3869 0.181 0.000 0.500 0.000 0.000 0.000 0.500
#> AB2E57AC-5BB3-4849-9D48-C87BBF8B413F 1 0.0146 0.938 0.996 0.000 0.000 0.000 0.004 0.000
#> 31354101-6FAF-40DB-BC13-1A4889C8CCDB 5 0.3123 0.804 0.136 0.000 0.040 0.000 0.824 0.000
#> 3DFCF947-3A16-49B8-B7BB-4F2F2A0333E6 1 0.3789 0.580 0.668 0.000 0.004 0.000 0.004 0.324
#> 0FEC3F05-D63C-4631-AC08-CDC2D89BE67E 4 0.0692 0.964 0.000 0.000 0.004 0.976 0.020 0.000
#> AF06ED6A-EF09-4E84-A728-4988BFA90339 1 0.1814 0.866 0.900 0.000 0.000 0.000 0.000 0.100
#> BCEA34CA-360D-4776-BAD1-387F48E5550D 2 0.2624 0.756 0.000 0.844 0.004 0.000 0.004 0.148
#> A7D93D12-C461-4953-AB66-9458C1D2A06A 2 0.1745 0.767 0.000 0.920 0.000 0.000 0.012 0.068
#> AFC6EAFF-3B81-46F0-8C48-9270FF5BE164 3 0.1501 0.782 0.076 0.000 0.924 0.000 0.000 0.000
#> EE5C7BA5-055C-4D13-BA54-C5F85E6F9781 4 0.0000 0.972 0.000 0.000 0.000 1.000 0.000 0.000
#> 7B0C596C-AA30-453E-B978-481B67BDC55B 4 0.3124 0.859 0.032 0.000 0.004 0.852 0.016 0.096
#> 092B3FA6-407C-40AB-8A96-29E5CEBC58C7 2 0.1588 0.767 0.000 0.924 0.004 0.000 0.000 0.072
#> D643D42B-D8D2-4B14-8576-279E9D3C6219 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> B3561356-5A80-4C79-B23A-D518425565FE 2 0.0405 0.746 0.000 0.988 0.000 0.004 0.000 0.008
#> F900E9BE-2400-4451-9434-EE8BC513BA94 1 0.0000 0.939 1.000 0.000 0.000 0.000 0.000 0.000
#> 6BC21C0D-3866-4503-A72C-BF550CB67E97 1 0.0146 0.938 0.996 0.000 0.000 0.000 0.004 0.000
#> 5DFB79B7-E93A-4E6A-A8E9-06F0863E8BC9 4 0.0146 0.972 0.000 0.000 0.000 0.996 0.004 0.000
#> 607034BC-E86A-424E-B8AA-3F2598CC7986 3 0.0790 0.812 0.000 0.032 0.968 0.000 0.000 0.000
#> AFAEF14F-81B6-493F-89BC-A689F699AD66 2 0.3862 0.273 0.000 0.524 0.000 0.000 0.000 0.476
#> 85DDCDE4-1535-48EE-8DBF-155A68A51909 4 0.0146 0.972 0.000 0.000 0.000 0.996 0.004 0.000
#> 472B75A2-A8C0-4503-B212-CADB781802EB 4 0.1957 0.925 0.000 0.008 0.008 0.912 0.072 0.000
#> F205F9FC-F2D5-4164-9A40-1279647F900B 4 0.2978 0.879 0.000 0.012 0.056 0.860 0.072 0.000
#> D82FECBA-9DB2-4376-BAAB-1AEA25CE4F67 6 0.0146 0.780 0.000 0.004 0.000 0.000 0.000 0.996
#> AAAA6565-B578-4F16-8651-00420EA3BBA9 1 0.0146 0.938 0.996 0.000 0.000 0.000 0.004 0.000
#> 12F54761-4F68-4181-8421-88EA858902FC 1 0.0146 0.937 0.996 0.000 0.000 0.000 0.000 0.004
#> AD8F2E20-9D30-460B-A53C-3847C12464E9 4 0.0458 0.966 0.000 0.000 0.000 0.984 0.000 0.016
#> FA716037-886B-4DD0-8016-686C2D24550A 4 0.0146 0.971 0.000 0.000 0.000 0.996 0.004 0.000
#> 529FB954-9F8F-4D0C-95FB-130172C04FFF 2 0.1644 0.767 0.000 0.920 0.004 0.000 0.000 0.076
#> ACD42BD5-BC9C-460A-9672-A0CFAF3B96BE 3 0.2597 0.738 0.000 0.000 0.824 0.000 0.176 0.000
#> 13531B65-487B-4964-9AFA-4DE90C2DBBAE 5 0.2266 0.879 0.000 0.108 0.012 0.000 0.880 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.
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):
#> [1] circlize_0.4.8 shape_1.4.4 xfun_0.11 slam_0.1-46
#> [5] lattice_0.20-38 splines_3.6.0 colorspace_1.4-1 vctrs_0.2.0
#> [9] stats4_3.6.0 blob_1.2.0 XML_3.98-1.20 survival_2.44-1.1
#> [13] rlang_0.4.2 pillar_1.4.2 DBI_1.0.0 BiocGenerics_0.30.0
#> [17] bit64_0.9-7 RColorBrewer_1.1-2 matrixStats_0.55.0 stringr_1.4.0
#> [21] GlobalOptions_0.1.1 evaluate_0.14 memoise_1.1.0 Biobase_2.44.0
#> [25] IRanges_2.18.3 parallel_3.6.0 AnnotationDbi_1.46.1 highr_0.8
#> [29] Rcpp_1.0.3 xtable_1.8-4 backports_1.1.5 S4Vectors_0.22.1
#> [33] annotate_1.62.0 skmeans_0.2-11 bit_1.1-14 microbenchmark_1.4-7
#> [37] brew_1.0-6 impute_1.58.0 rjson_0.2.20 png_0.1-7
#> [41] digest_0.6.23 stringi_1.4.3 polyclip_1.10-0 clue_0.3-57
#> [45] tools_3.6.0 bitops_1.0-6 magrittr_1.5 eulerr_6.0.0
#> [49] RCurl_1.95-4.12 RSQLite_2.1.4 tibble_2.1.3 cluster_2.1.0
#> [53] crayon_1.3.4 pkgconfig_2.0.3 zeallot_0.1.0 Matrix_1.2-17
#> [57] xml2_1.2.2 httr_1.4.1 R6_2.4.1 mclust_5.4.5
#> [61] compiler_3.6.0