View source: R/ZWZ2023.TSBF.2cNRT.R
ZWZ2023.TSBF.2cNRT | R Documentation |
Zhu et al. (2023)'s test for testing equality of two-sample high-dimensional mean vectors without assuming that two covariance matrices are the same.
ZWZ2023.TSBF.2cNRT(y1, y2)
y1 |
The data matrix ( |
y2 |
The data matrix ( |
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.
Zhu et al. (2023) proposed the following test statistic:
T_{ZWZ}=\frac{n_1n_2n^{-1}\|\bar{\boldsymbol{y}}_1-\bar{\boldsymbol{y}}_2\|^2}{\operatorname{tr}(\hat{\boldsymbol{\Omega}}_n)},
where \bar{\boldsymbol{y}}_{i},i=1,2
are the sample mean vectors and \hat{\boldsymbol{\Omega}}_n
is the estimator of \operatorname{Cov}[(n_1n_2/n)^{1/2}(\bar{\boldsymbol{y}}_1-\bar{\boldsymbol{y}}_2)]
.
They showed that under the null hypothesis, T_{ZWZ}
and an F-type mixture have the same normal or non-normal limiting distribution.
A list of class "NRtest"
containing the results of the hypothesis test. See the help file for NRtest.object
for details.
zhu2022twoHDNRA
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
ZWZ2023.TSBF.2cNRT(group1, group2)
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