suggest_best_k-ConsensusPartition-method | R Documentation |
Suggest the best number of subgroups
## S4 method for signature 'ConsensusPartition'
suggest_best_k(object,
jaccard_index_cutoff = select_jaccard_cutoff(ncol(object)),
mean_silhouette_cutoff = NULL,
stable_PAC = 0.1, help = cola_opt$help)
object |
A |
jaccard_index_cutoff |
The cutoff for Jaccard index for comparing to previous k. |
mean_silhouette_cutoff |
Cutoff for mean silhourtte scores. |
stable_PAC |
Cutoff for stable PAC. This argument only take effect when |
help |
Whether to print help message. |
The best k is selected according to following rules:
All k with Jaccard index larger than jaccard_index_cutoff
are removed because increasing k does not provide enough extra information. If all k are removed, it is marked as no subgroup is detected.
If all k with Jaccard index larger than 0.75, k with the highest mean silhourtte score is taken as the best k.
For all k with mean silhouette score larger than mean_silhouette_cutoff
, the maximal k is taken as the best k, and other k are marked as optional best k.
If argument mean_silhouette_cutoff
is set to NULL, which means we do not filter by mean silhouette scores while by 1-PAC scores. Similarly, k with the highest 1-PAC is taken the best k and other k are marked as optional best k.
If it does not fit the second rule. The k with the maximal vote of the highest 1-PAC score, highest mean silhouette, and highest concordance is taken as the best k.
It should be noted that it is difficult to find the best k deterministically, we encourage users to compare results for all k and determine a proper one which best explain their studies.
The best k.
The selection of the best k can be visualized by select_partition_number
.
Zuguang Gu <z.gu@dkfz.de>
data(golub_cola)
obj = golub_cola["ATC", "skmeans"]
suggest_best_k(obj)
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