highDmean {highDmean}

highDmean: A package for testing of equal mean for two-sample high dimensional data

Description

This package is an implementation of the high-dimensional two-sample test proposed by Zhang and Wang (2020) "Result consistency of high dimensional two-sample tests applied to gene ontology terms with gene sets". It also implements the SKK test proposed by Srivastava, Katayama, and Kano (2013) "A two sample test in high dimensional data." These tests are particularly suitable for high dimensional data from two populations for which the classical multivariate Hotelling's T-square test fails due to sample sizes smaller than dimensionality. In this case, the ZWL and ZWLm tests proposed by Zhang and Wang (2020), referred to as zwl_test() in this package, provide a reliable and powerful test.

highDmean functions

The function zwl_test() conducts the ZWL and ZWLm test of equal mean for two-sample high dimensional data provided in matrices of dimension n by p and m by p, which are random samples from two populations. It returns the value of test statistic and p-value under the null hypothesis of equal means. The SKK_test() performs the SKK test and returns the value of test statistic and p-value. The buildData() function generates simulated high-dimensional data in the two-population setting with specified sample sizes, numbers of components, covariance structure, etc., and the functions zwl_sim() and SKK_sim() return test statistic values and p-values for lists of simulated data sets generated by buildData().