Your time is being taken up in cor.test because you are calling it
100,000 times. So grin and bear it with the amount of work you are
asking it to do.
Here I am only calling it 100 time:
m1 - matrix(rnorm(1), ncol=100)
m2 - matrix(rnorm(1), ncol=100)
Rprof('/tempxx.txt')
He might try rcorr from Hmisc instead. Using your test suite, it gives
about a 20% improvement on my MacPro:
m1 - matrix(rnorm(1), ncol=100)
m2 - matrix(rnorm(1), ncol=100)
Rprof('/tempxx.txt')
system.time(cor.pvalues - apply(m1, 1, function(x) { apply(m2, 1,
function(y) {
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(10), ncol=100)
m2 - matrix(rnorm(10),
On Wed, Nov 26, 2008 at 8:14 AM, jim holtman [EMAIL PROTECTED] wrote:
Your time is being taken up in cor.test because you are calling it
100,000 times. So grin and bear it with the amount of work you are
asking it to do.
Here I am only calling it 100 time:
m1 - matrix(rnorm(1),
Out of desperation, I made the following function which hadley beats me to it
:P. Thanks everyone for the great help.
cor.p.values - function(r, n) {
df - n - 2
STATISTIC - c(sqrt(df) * r / sqrt(1 - r^2))
p - pt(STATISTIC, df)
return(2 * pmin(p, 1 - p))
}
Date: Wed, 26 Nov 2008
You can do much better by doing the correlations as a matrix operation:
system.time({
+ m1-scale(m1)
+ m2-scale(m2)
+ r-crossprod(m1,m2)/100
+ df-100
+ tstat-sqrt(df)*r/sqrt(1-r^2)
+ p-pt(tstat,df)
+ })
user system elapsed
0.025 0.004 0.028
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(10), ncol=100)
m2 - matrix(rnorm(1000), ncol=100)
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