cov2.2013CLX {SHT} | R Documentation |
Two-sample Test for Covariance Matrices by Cai, Liu, and Xia (2013)
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 Cai, Liu, and Xia (2013).
Usage
cov2.2013CLX(X, Y)
Arguments
X |
an |
Y |
an |
Value
a (list) object of S3
class htest
containing:
- statistic
a test statistic.
- p.value
p
-value underH_0
.- alternative
alternative hypothesis.
- method
name of the test.
- data.name
name(s) of provided sample data.
References
Cai T, Liu W, Xia Y (2013). “Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings.” Journal of the American Statistical Association, 108(501), 265–277. ISSN 0162-1459, 1537-274X.
Examples
## CRAN-purpose small example
smallX = matrix(rnorm(10*3),ncol=3)
smallY = matrix(rnorm(10*3),ncol=3)
cov2.2013CLX(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(cov2.2013CLX(X, Y)$p.value < 0.05, 1, 0)
}
## print the result
cat(paste("\n* Example for 'cov2.2013CLX'\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=""))
[Package SHT version 0.1.8 Index]