The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Perform the Adaptable Regularized Hotelling’s T2 test (ARHT) proposed by Li et al. (2016). Both one- and two- sample mean test are available with various probabilistic alternative prior models. It contains a function to consistently estimate higher order moments of the population covariance spectral distribution using the spectral of the sample covariance matrix. In addition, it contains a function to sample from 3-variate chi-squared random vectors approximately with a given correlation matrix when the degrees of freedom are large.
You can install ARHT from github with:
# install.packages("devtools")
devtools::install_github("HaoranLi/ARHT")This is a basic example which shows you how to solve a common problem:
library(ARHT)
## basic example code
set.seed(10086)
# One-sample test
n1 = 300; p =500
dataX = matrix(rnorm(n1 * p), nrow = n1, ncol = p)
res1 = ARHT(dataX)
# Two-sample test
n2= 400
dataY = matrix(rnorm(n2 * p), nrow = n2, ncol = p )
res2 = ARHT(dataX, dataY, mu_0 = rep(0.01,p))
# Specify probabilistic alternative priors model
res3 = ARHT(dataX, dataY, mu_0 = rep(0.01,p),
prob_alt_prior = list(c(1/3, 1/3, 1/3), c(0,1,0)))
# Change Type 1 error calibration method
res4 = ARHT(dataX, dataY, mu_0 = rep(0.01,p),
Type1error_calib = "sqrt")
RejectOrNot = res4$ARHT_pvalue < 0.05Li, Haoran, Alexander Aue, Debashis Paul, Jie Peng, and Pei Wang. 2016. “An Adaptable Generalization of Hotelling’s T2 Test in High Dimension.” arXiv preprint arXiv:1609.08725.
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.