Hi Daren,
Here is another aproach a little bit faster taking into account that I'm
using your original matrices. My session info is at the end. I'm using a
2.4 GHz Core 2-Duo processor and 3 GB of RAM.
# Data
set.seed(123)
m1 <- matrix(rnorm(100000), ncol=100)
m2 <- matrix(rnorm(100000), ncol=100)
colnames(m1)=paste('m1_',1:100,sep="")
colnames(m2)=paste('m2_',1:100,sep="")
# Combinations
combs=expand.grid(colnames(m1),colnames(m2))
# ---------------
# Option 1
#----------------
system.time(apply(combs,1,function(x)
cor.test(m1[,x[1]],m2[,x[2]])$p.value)->pvalues1)
# user system elapsed
# 8.12 0.01 8.20
# ---------------
# Option 2
#----------------
require(Hmisc)
system.time(apply(combs,1,function(x)
rcorr(m1[,x[1]],m2[,x[2]])$P[2])->pvalues2)
# user system elapsed
# 7.00 0.00 7.02
HTH,
Jorge
# ------------- Session Info ----------------------------
R version 2.8.0 Patched (2008-11-08 r46864)
i386-pc-mingw32
locale:
LC_COLLATE=English_United States.1252;LC_CTYPE=English_United
States.1252;LC_MONETARY=English_United
States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
On Tue, Nov 25, 2008 at 11:55 PM, Daren Tan <[EMAIL PROTECTED]> wrote:
>
> My two matrices are roughly the sizes of m1 and m2. I tried using two apply
> and cor.test to compute the correlation p.values. More than an hour, and the
> codes are still running. Please help to make it more efficient.
>
> m1 <- matrix(rnorm(100000), ncol=100)
> m2 <- matrix(rnorm(10000000), ncol=100)
>
> cor.pvalues <- apply(m1, 1, function(x) { apply(m2, 1, function(y) {
> cor.test(x,y)$p.value }) })
>
> ______________________________________________
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> and provide commented, minimal, self-contained, reproducible code.
>
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