mean2.2011LJW {SHT} | R Documentation |
Two-sample Test for Multivariate Means by Lopes, Jacob, and Wainwright (2011)
Description
Given two multivariate data and
of same dimension, it tests
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
-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
-value under
.
- 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=""))