diceR (version 3.0.0)

sigclust: Significant Testing of Clustering Results

Description

Uses the SigClust K-Means algorithm to assess significance of clustering results.

Usage

sigclust(x, k, nsim, nrep = 1, labflag = 0, label = 0, icovest = 2)

Value

An object of class sigclust. See sigclust::sigclust() for details.

Arguments

x

data matrix, samples are rows and features are columns

k

cluster size to test against

nsim

number of simulations

nrep

See sigclust::sigclust() for details.

labflag

See sigclust::sigclust() for details.

label

true class label. See sigclust::sigclust() for details.

icovest

type of covariance matrix estimation

Author

Hanwen Huang: ; Yufeng Liu: ; J. S. Marron:

Details

This function is a wrapper for the original sigclust::sigclust(), except that an additional parameter k is allows testing against any number of clusters. In addition, the default type of covariance estimation is also different.

References

Liu, Yufeng, Hayes, David Neil, Nobel, Andrew and Marron, J. S, 2008, Statistical Significance of Clustering for High-Dimension, Low-Sample Size Data, Journal of the American Statistical Association 103(483) 1281--1293.

Examples

if (FALSE) { # rlang::is_installed("sigclust")
data(hgsc)
dat <- hgsc[1:100, 1:50]
nk <- 4
cc <- consensus_cluster(dat, nk = nk, reps = 5, algorithms = "pam",
progress = FALSE)
cl.mat <- consensus_combine(cc, element = "class")
lab <- cl.mat$`4`[, 1]
set.seed(1)
str(sigclust(x = dat, k = nk, nsim = 50, labflag = 1, label = lab))
}