mean2.2011LJW {SHT} | R Documentation |
Two-sample Test for Multivariate Means by Lopes, Jacob, and Wainwright (2011)
Description
Given two multivariate data X
and Y
of same dimension, it tests
H_0 : \mu_x = \mu_y\quad vs\quad H_1 : \mu_x \neq \mu_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.
Usage
mean2.2011LJW(X, Y, method = c("asymptotic", "MC"), nreps = 1000)
Arguments
X |
an |
Y |
an |
method |
method to compute |
nreps |
the number of permutation iterations to be run when |
Value
a (list) object of S3
class htest
containing:
- statistic
a test statistic.
- p.value
p
-value underH_0
.- alternative
alternative hypothesis.
- method
name of the test.
- data.name
name(s) of provided sample data.
References
Lopes ME, Jacob L, Wainwright MJ (2011). “A More Powerful Two-sample Test in High Dimensions Using Random Projection.” In Proceedings of the 24th International Conference on Neural Information Processing Systems, NIPS'11, 1206–1214. ISBN 978-1-61839-599-3.
Examples
## 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=""))