Here is what might be a slightly more efficient way to get to John's
question:
cor.pvals <- function(mat)
{
rows <- expand.grid(1:nrow(mat), 1:nrow(mat))
matrix(apply(rows, 1,
function(x) cor.test(mat[x[1], ], mat[x[2], ])$p.value),
ncol = nrow(mat))
}
HTH,
Marc Schwartz
On Fri, 2005-04-15 at 18:26 -0400, John Fox wrote:
> Dear Dren,
>
> How about the following?
>
> cor.pvalues <- function(X){
> nc <- ncol(X)
> res <- matrix(0, nc, nc)
> for (i in 2:nc){
> for (j in 1:(i - 1)){
> res[i, j] <- res[j, i] <- cor.test(X[,i], X[,j])$p.value
> }
> }
> res
> }
>
> What one then does with all of those non-independent test is another
> question, I guess.
>
> I hope this helps,
> John
> > -----Original Message-----
> > From: [EMAIL PROTECTED]
> > [mailto:[EMAIL PROTECTED] On Behalf Of Dren Scott
> > Sent: Friday, April 15, 2005 4:33 PM
> > To: [email protected]
> > Subject: [R] Pearson corelation and p-value for matrix
> >
> > Hi,
> >
> > I was trying to evaluate the pearson correlation and the
> > p-values for an nxm matrix, where each row represents a
> > vector. One way to do it would be to iterate through each
> > row, and find its correlation value( and the p-value) with
> > respect to the other rows. Is there some function by which I
> > can use the matrix as input? Ideally, the output would be an
> > nxn matrix, containing the p-values between the respective vectors.
> >
> > I have tried cor.test for the iterations, but couldn't find a
> > function that would take the matrix as input.
> >
> > Thanks for the help.
> >
> > Dren
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