Perform the Adaptable Regularized Hotelling's T^2 test (ARHT) proposed by
Li et al., (2016)
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:
This is a basic example which shows you how to solve a common problem:
library(ARHT)## basic example codeset.seed(10086)# One-sample testn1 = 300; p =500dataX = matrix(rnorm(n1 * p), nrow = n1, ncol = p)res1 = ARHT(dataX)# Two-sample testn2= 400dataY = matrix(rnorm(n2 * p), nrow = n2, ncol = p )res2 = ARHT(dataX, dataY, mu_0 = rep(0.01,p))# Specify probabilistic alternative priors modelres3 = 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 methodres4 = ARHT(dataX, dataY, mu_0 = rep(0.01,p),Type1error_calib = "sqrt")RejectOrNot = res4$ARHT_pvalue < 0.05
Li, 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.
Haoran Li 2/27/2018
This is the initial submission of ARHT.