cc_cluster_count {ConsensusClustering}

Count the number of clusters based on stability score.

Description

Count the number of clusters based on stability score.

Usage

cc_cluster_count(CM, plot.cdf = TRUE, plot.logit = FALSE)

Arguments

CM

list of consensus matrices each for a specific number of clusters. It can be the output of consensus_matrix() and multiview_consensus_matrix() functions.

plot.cdf

binary value to plot the cumulative distribution functions of CM (default TRUE).

plot.logit

binary value to plot the logit model of cumulative distribution functions of CM (default FALSE).

Details

Count the number of clusters given a list of consensus matrices each for a specific number of clusters. Using different methods: "LogitScore", "PAC", "deltaA", "CMavg"

Value

results as a list: "LogitScore", "PAC", "deltaA", "CMavg", "Kopt_LogitScore", "Kopt_PAC", "Kopt_deltaA", "Kopt_CMavg"

Examples

X = gaussian_clusters()$X
Adj = adj_mat(X, method = "euclidian")
CM = consensus_matrix(Adj, max.cluster=3, max.itter=10)
Result = cc_cluster_count(CM, plot.cdf=FALSE)