Two-sample Mean Test with Maximum Pairwise Bayes Factor

Source: R/mean2.mxPBF.R

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mean2.mxPBF(X, Y, a0 = 0, b0 = 0, gamma = 1, nthreads = 1)

Arguments

X

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

Y

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

a0

shape parameter for inverse-gamma prior (default: 0).

b0

scale parameter for inverse-gamma prior (default: 0).

gamma

non-negative variance scaling parameter (default: 1).

nthreads

number of threads for parallel execution via OpenMP (default: 1).

Value

a (list) object of S3 class htest containing:

statistic

maximum of pairwise Bayes factor.

alternative

alternative hypothesis.

method

name of the test.

data.name

name(s) of provided sample data.

log.BF.vec

vector of pairwise Bayes factors in natural log.

Examples

if (FALSE) {
## empirical Type 1 error with BF threshold = 10
niter   = 1000
counter = rep(0,niter)  # record p-values
for (i in 1:niter){
  X = matrix(rnorm(100*10), ncol=10)
  Y = matrix(rnorm(200*10), ncol=10)
  
  counter[i] = ifelse(mean2.mxPBF(X,Y)$statistic > 10, 1, 0)
}

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