View source: R/ZZZ2023.TSBF.2cNRT.R
ZZZ2023.TSBF.2cNRT | R Documentation |
Zhang et al. (2023)'s test for testing equality of two-sample high-dimensional mean vectors without assuming that two covariance matrices are the same.
ZZZ2023.TSBF.2cNRT(y1, y2, cutoff)
y1 |
The data matrix ( |
y2 |
The data matrix ( |
cutoff |
An empirical criterion for applying the adjustment coefficient |
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,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.(2023) proposed the following test statistic:
T_{ZZZ}=\frac{n_1 n_2}{np}(\bar{\boldsymbol{y}}_1-\bar{\boldsymbol{y}}_2)^{\top} \hat{\boldsymbol{D}}_n^{-1}(\bar{\boldsymbol{y}}_1-\bar{\boldsymbol{y}}_2),
where \bar{\boldsymbol{y}}_{i},i=1,2
are the sample mean vectors, and \hat{\boldsymbol{D}}_n=\operatorname{diag}(\hat{\boldsymbol{\Sigma}}_1/n+\hat{\boldsymbol{\Sigma}}_2/n)
with n=n_1+n_2
.
They showed that under the null hypothesis, T_{ZZZ}
and a chi-squared-type mixture have the same limiting distribution.
A list of class "NRtest"
containing the results of the hypothesis test. See the help file for NRtest.object
for details.
zhang2023twoHDNRA
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
ZZZ2023.TSBF.2cNRT(group1,group2,cutoff=1.2)
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