View source: R/meank_2019CPH.R
meank.2019CPH | R Documentation |
Given univariate samples X_1~,…,~X_k, it tests
H_0 : μ_1 = \cdots μ_k\quad vs\quad H_1 : \textrm{at least one equality does not hold}
using the procedure by Cao, Park, and He (2019).
meank.2019CPH(dlist, method = c("original", "Hu"))
dlist |
a list of length k where each element is a sample matrix of same dimension. |
method |
a method to be applied to estimate variance parameter. |
a (list) object of S3
class htest
containing:
a test statistic.
p-value under H_0.
alternative hypothesis.
name of the test.
name(s) of provided sample data.
cao_test_2019SHT
## CRAN-purpose small example tinylist = list() for (i in 1:3){ # consider 3-sample case tinylist[[i]] = matrix(rnorm(10*3),ncol=3) } meank.2019CPH(tinylist, method="o") # newly-proposed variance estimator meank.2019CPH(tinylist, method="h") # adopt one from 2017Hu ## Not run: ## test when k=5 samples with (n,p) = (10,50) ## empirical Type 1 error niter = 10000 counter = rep(0,niter) # record p-values for (i in 1:niter){ mylist = list() for (j in 1:5){ mylist[[j]] = matrix(rnorm(10*50),ncol=50) } counter[i] = ifelse(meank.2019CPH(mylist)$p.value < 0.05, 1, 0) } ## print the result cat(paste("\n* Example for 'meank.2019CPH'\n","*\n", "* number of rejections : ", sum(counter),"\n", "* total number of trials : ", niter,"\n", "* empirical Type 1 error : ",round(sum(counter/niter),5),"\n",sep="")) ## End(Not run)
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