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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: COVID19 and corneal.
Seven normal-reference tests for the two-sample problem: ts_zgzc2020(), ts_zz2022(), ts_zzz2020(), tsbf_zwz2023(), tsbf_zz2022(), tsbf_zzgz2021(), tsbf_zzz2023().
Five normal-reference tests for the GLHT problem in HDNRA: glht_zgz2017(), glht_zz2022(), glht_zzz2022(), glhtbf_zz2022(), glhtbf_zzg2022().
Four existing tests for the two-sample problem in HDNRA: ts_bs1996(), ts_sd2008(), tsbf_cq2010(), tsbf_skk2013().
Five existing tests for the GLHT problem in HDNRA: glht_fhw2004(), glht_sf2006(), glht_ys2012(), glhtbf_zgz2017(), ks_s2007().
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)Package HDNRA comes with two real data sets:
# A COVID19 data set from NCBI with ID GSE152641.
?COVID19
# A corneal data set acquired during a keratoconus study.
?cornealA simple example of how to use one of the normal-reference tests tsbf_zwz2023 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
tsbf_zwz2023(t(group1), t(group2))
#>
#>
#>
#> data:
#> statistic = 4.1877, df1 = 2.7324, df2 = 171.7596, p-value = 0.008673A simple example of how to use one of the normal-reference tests glhtbf_zzg2022 using data set corneal:
data("corneal")
p <- dim(corneal)[2]
k <- 4
Y <- list()
group1 <- as.matrix(corneal[1:43, ]) # normal
group2 <- as.matrix(corneal[44:57, ]) # unilateral suspect
group3 <- as.matrix(corneal[58:78, ]) # suspect
group4 <- as.matrix(corneal[79:150, ]) # clinical leratoconus
Y[[1]] <- t(group1)
Y[[2]] <- t(group2)
Y[[3]] <- t(group3)
Y[[4]] <- t(group4)
dim(Y[[1]])
#> [1] 2000 43
dim(Y[[2]])
#> [1] 2000 14
dim(Y[[3]])
#> [1] 2000 21
dim(Y[[4]])
#> [1] 2000 72
n <- c(43, 14, 21, 72)
G <- cbind(diag(k - 1), rep(-1, k - 1))
glhtbf_zzg2022(Y, G, n, p)
#>
#>
#>
#> data:
#> statistic = 159.73, df = 6.1652, beta = 6.1464, p-value = 0.0002577Please 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.
Health stats visible at Monitor.