View source: R/mean2_2011LJW.R
mean2.2011LJW | R Documentation |
Given two multivariate data X and Y of same dimension, it tests
H_0 : μ_x = μ_y\quad vs\quad H_1 : μ_x \neq μ_y
using the procedure by Lopes, Jacob, and Wainwright (2011) using random projection. Due to solving system of linear equations, we suggest you to opt for asymptotic-based p-value computation unless truly necessary for random permutation tests.
mean2.2011LJW(X, Y, method = c("asymptotic", "MC"), nreps = 1000)
X |
an (n_x \times p) data matrix of 1st sample. |
Y |
an (n_y \times p) data matrix of 2nd sample. |
method |
method to compute p-value. |
nreps |
the number of permutation iterations to be run when |
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.
lopes_more_2011SHT
## CRAN-purpose small example smallX = matrix(rnorm(10*3),ncol=10) smallY = matrix(rnorm(10*3),ncol=10) mean2.2011LJW(smallX, smallY) # run the test ## empirical Type 1 error niter = 1000 counter = rep(0,niter) # record p-values for (i in 1:niter){ X = matrix(rnorm(10*20), ncol=20) Y = matrix(rnorm(10*20), ncol=20) counter[i] = ifelse(mean2.2011LJW(X,Y)$p.value < 0.05, 1, 0) } ## print the result cat(paste("\n* Example for 'mean2.2011LJW'\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=""))
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