ZZZ2020.TS.2cNRT: Normal-reference-test with two-cumulant (2-c) matched...

View source: R/ZZZ2020.TS.2cNRT.R

ZZZ2020.TS.2cNRTR Documentation

Normal-reference-test with two-cumulant (2-c) matched $\chi^2$-approximation for two-sample problem proposed by Zhang et al. (2020)

Description

Zhang et al. (2020)'s test for testing equality of two-sample high-dimensional mean vectors with assuming that two covariance matrices are the same.

Usage

ZZZ2020.TS.2cNRT(y1, y2)

Arguments

y1

The data matrix (n_1 \times p) from the first population. Each row represents a p-dimensional observation.

y2

The data matrix (n_2 \times p) from the second population. Each row represents a p-dimensional observation.

Details

Suppose we have two independent high-dimensional samples:

\boldsymbol{y}_{i1},\ldots,\boldsymbol{y}_{in_i}, \;\operatorname{are \; i.i.d. \; with}\; \operatorname{E}(\boldsymbol{y}_{i1})=\boldsymbol{\mu}_i,\; \operatorname{Cov}(\boldsymbol{y}_{i1})=\boldsymbol{\Sigma},i=1,2.

The primary object is to test

H_{0}: \boldsymbol{\mu}_1 = \boldsymbol{\mu}_2\; \operatorname{versus}\; H_{1}: \boldsymbol{\mu}_1 \neq \boldsymbol{\mu}_2.

Zhang et al.(2020) proposed the following test statistic:

T_{ZZZ} = \frac{n_1n_2}{np}(\bar{\boldsymbol{y}}_1 - \bar{\boldsymbol{y}}_2)^\top \hat{\boldsymbol{D}}^{-1}(\bar{\boldsymbol{y}}_1 - \bar{\boldsymbol{y}}_2),

where \bar{\boldsymbol{y}}_{i},i=1,2 are the sample mean vectors, \hat{\boldsymbol{D}} is the diagonal matrix of sample covariance matrix. They showed that under the null hypothesis, T_{ZZZ} and a chi-squared-type mixture have the same limiting distribution.

Value

A list of class "NRtest" containing the results of the hypothesis test. See the help file for NRtest.object for details.

References

\insertRef

Zhang_2020HDNRA

Examples

library("HDNRA")
data("COVID19")
dim(COVID19)
group1 <- as.matrix(COVID19[c(2:19, 82:87), ]) ## healthy group
group2 <- as.matrix(COVID19[-c(1:19, 82:87), ]) ## COVID-19 patients
ZZZ2020.TS.2cNRT(group1,group2)



HDNRA documentation built on Oct. 30, 2024, 9:28 a.m.