epval_Bai1996: Empirical Permutation-Based p-value of the Test Proposed by...
In highmean: Two-Sample Tests for High-Dimensional Mean Vectors

Description Usage Arguments Details Value Note References See Also Examples

Calculates p-value of the test for testing equality of two-sample high-dimensional mean vectors proposed by Bai and Saranadasa (1996) based on permutation.

1	epval_Bai1996(sam1, sam2, n.iter = 1000, seeds)

`sam1`	an n1 by p matrix from sample population 1. Each row represents a p-dimensional sample.
`sam2`	an n2 by p matrix from sample population 2. Each row represents a p-dimensional sample.
`n.iter`	a numeric integer indicating the number of permutation iterations. The default is 1,000.
`seeds`	a vector of seeds for each permutation or parametric bootstrap resampling iteration; this is optional.

See the details in apval_Bai1996.

A list including the following elements:

`sam.info`	the basic information about the two groups of samples, including the samples sizes and dimension.
`cov.assumption`	this output reminds users that the two sample populations have a common covariance matrix.
`method`	this output reminds users that the p-values are obtained using permutation.
`pval`	the p-value of the test proposed by Bai and Saranadasa (1996).

The permutation technique assumes that the distributions of the two sample populations are the same under the null hypothesis.

Bai ZD and Saranadasa H (1996). "Effect of high dimension: by an example of a two sample problem." Statistica Sinica, 6(2), 311–329.

apval_Bai1996

#library(MASS)
#set.seed(1234)
#n1 <- n2 <- 50
#p <- 200
#mu1 <- rep(0, p)
#mu2 <- mu1
#mu2[1:10] <- 0.2
#true.cov <- 0.4^(abs(outer(1:p, 1:p, "-"))) # AR1 covariance
#sam1 <- mvrnorm(n = n1, mu = mu1, Sigma = true.cov)
#sam2 <- mvrnorm(n = n2, mu = mu2, Sigma = true.cov)
# increase n.iter to reduce Monte Carlo error.
#epval_Bai1996(sam1, sam2, n.iter = 10)