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The R package HDNRA includes the latest methods
based on normal-reference approach to test the equality of the mean
vectors of high-dimensional samples with possibly different covariance
matrices. HDNRA is also used to demonstrate the
implementation of these tests, catering not only to the two-sample
problem, but also to the general linear hypothesis testing (GLHT)
problem. This package provides easy and user-friendly access to these
tests. Both coded in C++ to allow for reasonable execution time using Rcpp. Besides Rcpp, the package has no
strict dependencies in order to provide a stable self-contained toolbox
that invites re-use.
There are:
Two real data sets in HDNRA
Seven normal-reference tests for the two-sample problem
Five normal-reference tests for the GLHT problem in
HDNRA
Four existing tests for the two-sample problem in
HDNRA
Five existing tests for the GLHT problem in HDNRA
You can install and load the most recent development version of
HDNRA from GitHub
with:
# Installing from GitHub requires you first install the devtools or remotes package
install.packages("devtools")
# Or
install.packages("remotes")
# install the most recent development version from GitHub
devtools::install_github("nie23wp8738/HDNRA")
# Or
remotes::install_github("nie23wp8738/HDNRA")
# load the most recent development version from GitHub
library(HDNRA)library(HDNRA)Package HDNRA comes with two real data sets:
# A COVID19 data set from NCBI with ID GSE152641 for the two-sample problem.
?COVID19
data(COVID19)
dim(COVID19)
#> [1] 87 20460
group1 <- as.matrix(COVID19[c(2:19, 82:87), ]) ## healthy group
dim(group1)
#> [1] 24 20460
group2 <- as.matrix(COVID19[-c(1:19, 82:87), ]) ## COVID-19 patients
dim(group2)
#> [1] 62 20460
# A corneal data set acquired during a keratoconus study for the GLHT problem.
?corneal
data(corneal)
dim(corneal)
#> [1] 150 2000
group1 <- as.matrix(corneal[1:43, ]) ## normal group
dim(group1)
#> [1] 43 2000
group2 <- as.matrix(corneal[44:57, ]) ## unilateral suspect group
dim(group2)
#> [1] 14 2000
group3 <- as.matrix(corneal[58:78, ]) ## suspect map group
dim(group3)
#> [1] 21 2000
group4 <- as.matrix(corneal[79:150, ]) ## clinical keratoconus group
dim(group4)
#> [1] 72 2000A simple example of how to use one of the normal-reference tests
ZWZ2023.TSBF.2cNRT using data set COVID19:
data("COVID19")
group1 <- as.matrix(COVID19[c(2:19, 82:87), ]) # healthy group1
group2 <- as.matrix(COVID19[-c(1:19, 82:87), ]) # patients group2
# The data matrix for tsbf_zwz2023 should be p by n, sometimes we should transpose the data matrix
ZWZ2023.TSBF.2cNRT(group1, group2)
#>
#> Results of Hypothesis Test
#> --------------------------
#>
#> Test name: Zhu et al. (2023)'s test
#>
#> Null Hypothesis: Difference between two mean vectors is 0
#>
#> Alternative Hypothesis: Difference between two mean vectors is not 0
#>
#> Data: group1 and group2
#>
#> Sample Sizes: n1 = 24
#> n2 = 62
#>
#> Sample Dimension: 20460
#>
#> Test Statistic: T[ZWZ] = 4.1877
#>
#> Approximation method to the 2-c matched chi^2-approximation
#> null distribution of T[ZWZ]:
#>
#> Approximation parameter(s): df1 = 2.7324
#> df2 = 171.7596
#>
#> P-value: 0.008672887A simple example of how to use one of the normal-reference tests
ZZG2022.GLHTBF.2cNRT using data set
corneal:
data("corneal")
dim(corneal)
#> [1] 150 2000
group1 <- as.matrix(corneal[1:43, ]) ## normal group
group2 <- as.matrix(corneal[44:57, ]) ## unilateral suspect group
group3 <- as.matrix(corneal[58:78, ]) ## suspect map group
group4 <- as.matrix(corneal[79:150, ]) ## clinical keratoconus group
p <- dim(corneal)[2]
Y <- list()
k <- 4
Y[[1]] <- group1
Y[[2]] <- group2
Y[[3]] <- group3
Y[[4]] <- group4
n <- c(nrow(Y[[1]]),nrow(Y[[2]]),nrow(Y[[3]]),nrow(Y[[4]]))
G <- cbind(diag(k-1),rep(-1,k-1))
ZZG2022.GLHTBF.2cNRT(Y,G,n,p)
#>
#> Results of Hypothesis Test
#> --------------------------
#>
#> Test name: Zhang et al. (2022)'s test
#>
#> Null Hypothesis: The general linear hypothesis is true
#>
#> Alternative Hypothesis: The general linear hypothesis is not true
#>
#> Data: Y
#>
#> Sample Sizes: n1 = 43
#> n2 = 14
#> n3 = 21
#> n4 = 72
#>
#> Sample Dimension: 2000
#>
#> Test Statistic: T[ZZG] = 159.7325
#>
#> Approximation method to the 2-c matched chi^2-approximation
#> null distribution of T[ZZG]:
#>
#> Approximation parameter(s): df = 6.1652
#> beta = 6.1464
#>
#> P-value: 0.0002577084Please note that the HDNRA project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.