zwl_test: High-dimensional two-sample test proposed by Zhang and Wang...

In highDmean: Testing Two-Sample Mean in High Dimension

Description

This function implements the test of equal mean for two-sample high-dimension data using the ZWL and ZWLm tests proposed by Zhang and Wang (2020).

Usage

zwl_test(X, Y, order = 0)

Arguments

X	The data matrix (n by p) from the first population.
Y	The data matrix (m by p) from the second population.
order	The order of center correction. Possible choices are 0, 2. To use the ZWLm test, set order = 0; to use the ZWL test, set order = 2. For moderate sample sizes, ZWLm is recommended.

Value

statistic

The value of the test statistic.

pvalue

The p-value of the test statistic based on the asymptotic normality established by Zhang and Wang (2020)

Tn

The average of the squared univariate t-statistics.

var

The estimated variance of Tn

References

Zhang, H. and Wang, H. (2020). Result consistency of high dimensional two-sample tests applied to gene ontology terms with gene sets. Manuscript in review.

Examples

# Generate a simulated two-sample dataset and apply the ZWL test
data <- buildData(n = 45, m =60, p = 300,
          muX = rep(0,300), muY = rep(0,300),
          dep = 'IND', S = 1, innov = rnorm)
zwl_test(data[[1]]$X, data[[1]]$Y, order = 2)

# Apply the ZWLm test to a GO term to see if the two groups are differentiately expressed.
# The data for the GO term were stored in GO_example.
zwl_test(GO_example$X, GO_example$Y, order = 0)
# Apply the ZWL test to the GO term
zwl_test(GO_example$X, GO_example$Y, order = 2)

highDmean documentation built on July 2, 2020, 3:15 a.m.