sigclust {diceR}

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)

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

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.

Value

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

Author(s)

Hanwen Huang: [e-mail]; Yufeng Liu: [e-mail]; J. S. Marron: [e-mail]

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

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))