jackstraw_kmeans: Non-Parametric Jackstraw for K-means Clustering

Description Usage Arguments Details Value Author(s) References

View source: R/jackstraw_kmeans.R

Description

Test the cluster membership for K-means clustering

Usage

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jackstraw_kmeans(dat, kmeans.dat, s = 1, B = 1000, covariate = NULL,
  verbose = FALSE, seed = NULL, ...)

Arguments

dat

a data matrix with m rows as variables and n columns as observations.

kmeans.dat

an output from applying kmeans() onto dat.

s

a number of “synthetic” null variables. Out of m variables, s variables are independently permuted.

B

a number of resampling iterations.

covariate

a model matrix of covariates with n observations. Must include an intercept in the first column.

verbose

a logical specifying to print the computational progress. By default, FALSE.

seed

a seed for the random number generator.

...

optional arguments to control the k-means clustering algorithm (refers to kmeans).

Details

K-means clustering assign m rows into K clusters. This function enable statistical evaluation if the cluster membership is correctly assigned. Each of m p-values refers to the statistical test of that row with regard to its assigned cluster. Its resampling strategy accounts for the over-fitting characteristics due to direct computation of clusters from the observed data and protects against an anti-conservative bias.

Value

jackstraw_kmeans returns a list consisting of

F.obs

m observed F statistics between variables and cluster centers.

F.null

F null statistics between null variables and cluster centers, from the jackstraw method.

p.F

m p-values of membership.

Author(s)

Neo Christopher Chung [email protected]

References

Chung (2018) Statistical significance for cluster membership. biorxiv, doi:10.1101/248633 https://www.biorxiv.org/content/early/2018/01/16/248633


ncchung/jackstraw documentation built on April 4, 2018, 7:58 a.m.