mean2.1965Yao {SHT}

Two-sample Test for Multivariate Means by Yao (1965)

Description

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

H_0 : \mu_x = \mu_y\quad vs\quad H_1 : \mu_x \neq \mu_y

using the procedure by Yao (1965) via multivariate modification of Welch's approximation of degrees of freedoms.

Usage

mean2.1965Yao(X, Y)

Arguments

X

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

Y

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

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

Yao Y (1965). “An Approximate Degrees of Freedom Solution to the Multivariate Behrens Fisher Problem.” Biometrika, 52(1/2), 139. ISSN 00063444.

Examples

## CRAN-purpose small example
smallX = matrix(rnorm(10*3),ncol=3)
smallY = matrix(rnorm(10*3),ncol=3)
mean2.1965Yao(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(mean2.1965Yao(X,Y)$p.value < 0.05, 1, 0)
}

## print the result
cat(paste("\n* Example for 'mean2.1965Yao'\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=""))