[R] resampling for correlation and testing

[Benton, Paul]

Hello all R-er,

I'm trying to run a resampling method on some data. The current method I have takes 2+ days or a lot of memory . I was wondering if anyone has a better suggestion. 

Currently I take a matrix and get the correlation matrix from it. This will be called rho.A. Each element in this will be tested against the distribution from the resampled correlation B matrix. 

Some example code:

A<-matrix(rnorm(100), ncol=10)
B<-matrix(rnorm(100), ncol=10)

rho.A<-cor(A)

{
idx<-sample(1:10, 10)
idx
# [1]  8  4  5  7  1  9  2 10  6  3

rho.B<-cor(B[,idx])
} ## repeat this x time (currently 500)

## in essence we then have the following :
rho.arrayB<-array(runif((10*10)*500), dim=c(10,10,500))

## Then test if rho.A[1,1] come from the distribution of rho.B[1,1]
pvalueMat[1,1]<-wilcox.test(rho.array[1,1,] , rho.A[1,1])$p.value

However, my array size would be 2300 x 2300 x 500 which R won't let me even make as an empty structure. Any suggestion are more than welcomed !!

Cheers,

Paul