Pearson corelation and p-value for matrix

system.time(c1 <- cor.pvals(X), gcFirst=TRUE)

system.time(c2 <- cor.pvalues(X), gcFirst=TRUE)

system.time(c3 <- cor.pval(X), gcFirst=TRUE)

From: John Fox

Dear Mark,

I think that the reflex of trying to avoid loops in R is 
often mistaken, and
so I decided to try to time the two approaches (on a 3GHz Windows XP
system).

I discovered, first, that there is a bug in your function -- 
you appear to
have indexed rows instead of columns; fixing that:

cor.pvals <- function(mat)
{
  cols <- expand.grid(1:ncol(mat), 1:ncol(mat))
  matrix(apply(cols, 1,
               function(x) cor.test(mat[, x[1]], mat[, 
x[2]])$p.value),
         ncol = ncol(mat))
}


My function is cor.pvalues and yours cor.pvals. This is for a 
data matrix
with 1000 observations on 100 variables:

R <- diag(100)
R[upper.tri(R)] <- R[lower.tri(R)] <- .5
library(mvtnorm)
X <- rmvnorm(1000, sigma=R)
dim(X)

[1] 1000  100

system.time(cor.pvalues(X))

[1] 5.53 0.00 5.53   NA   NA

system.time(cor.pvals(X))

[1] 12.66  0.00 12.66    NA    NA

I frankly didn't expect the advantage of my approach to be 
this large, but
there it is.

Regards,
 John

--------------------------------
John Fox
Department of Sociology
McMaster University
Hamilton, Ontario
Canada L8S 4M4
905-525-9140x23604
http://socserv.mcmaster.ca/jfox 
-------------------------------- 

-----Original Message-----
From: Marc Schwartz [mailto:MSchwartz at MedAnalytics.com] 
Sent: Friday, April 15, 2005 7:08 PM
To: John Fox
Cc: 'Dren Scott'; R-Help
Subject: RE: [R] Pearson corelation and p-value for matrix

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: r-help-bounces at stat.math.ethz.ch 
[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of 

Dren Scott

Sent: Friday, April 15, 2005 4:33 PM
To: r-help at stat.math.ethz.ch
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