cov2.2015WL {SHT}

Two-sample Test for Covariance Matrices by Wu and Li (2015)

Description

Given two multivariate data $X$ and $Y$ of same dimension, it tests

$H_0 : \Sigma_x = \Sigma_y\quad vs\quad H_1 : \Sigma_x \neq \Sigma_y$

using the procedure by Wu and Li (2015).

Usage

cov2.2015WL(X, Y, m = 50)

Arguments

X

an $(n_x \times p)$ data matrix of 1st sample.

Y

an $(n_y \times p)$ data matrix of 2nd sample.

m

the number of random projections to be applied.

Value

a (list) object of S3 class htest containing:

statistic

a test statistic.

p.value

$p$-value under $H_0$.

alternative

alternative hypothesis.

method

name of the test.

data.name

name(s) of provided sample data.

References

Wu T, Li P (2015). “Tests for High-Dimensional Covariance Matrices Using Random Matrix Projection.” arXiv:1511.01611 [stat].

Examples

## CRAN-purpose small example
smallX = matrix(rnorm(10*3),ncol=3)
smallY = matrix(rnorm(10*3),ncol=3)
cov2.2015WL(smallX, smallY) # run the test


## empirical Type 1 error 
niter   = 1000
counter = rep(0,niter)  # record p-values
for (i in 1:niter){
  X = matrix(rnorm(50*5), ncol=10)
  Y = matrix(rnorm(50*5), ncol=10)
  
  counter[i] = ifelse(cov2.2015WL(X, Y)$p.value < 0.05, 1, 0)
}

## print the result
cat(paste("\n* Example for 'cov2.2015WL'\n","*\n",
"* number of rejections   : ", sum(counter),"\n",
"* total number of trials : ", niter,"\n",
"* empirical Type 1 error : ",round(sum(counter/niter),5),"\n",sep=""))