Thanks Kenn. I will add your solution to my ever growing colection of functions
;-))
Monica
Date: Fri, 9 May 2008 18:35:30 +0300From: [EMAIL PROTECTED]: [EMAIL PROTECTED]:
Re: [R] applying cor.test to a (m, n) matrixCC: [EMAIL PROTECTED]'ve used the
following function (I wrote it some time ago so I don't remember any more why I
needed it, but I checked and it still works). I don't think you can get rid of
for loops altogether: if you look at the code of apply, you'll see some there
too.The argument STATS specifies those components of a function's output you
want to extract (if the component you're asking for has length >1 then you'll
get the first item). To extract ci's you will need an intermediate
function:cor.test2 <- function(...) { foo<-cor.test(...) ci<-foo$conf.int
foo$ci1<-ci[1] foo$ci2<-ci[2] foo}Then rrapply(my.data, FUN=cor.test2,
STATS=c("estimate", "p.value", "ci1", "ci2")) should do what you want.rrapply
<-function(x, FUN=t.test, STATS=c("statistic", "p.value"), ...){ # use
FUN=cor.test, STATS=c("estimate", "p.val!
ue") for matrix of correlations and p-values K <- ncol(x) RESU <- list()
my.fun <- FUN fuf<-my.fun(x[,2],x[,1], ...) if(!is.list(fuf)) {
STATS <- "A" my.fun <- function(x,y,...)
list(A=FUN(x,y,...)) } neimz <- colnames(x) for(i in
1:length(STATS)) { fof <- STATS[i] RESU[[fof]] <- matrix(NA,
ncol=K, nrow=K) colnames(RESU[[fof]]) <- neimz
rownames(RESU[[fof]]) <- neimz names(RESU)[i] <- fof } for(i
in 2:K) for(j in 1:(i-1)) { foo<-my.fun(x[,i],x[,j], ...) for(h
in 1:length(STATS)) { fof <- STATS[h]
RESU[[fof]][i,j]<- foo[[fof]] } } if(is.list(fuf)) RESU else
RESU$A}
On Fri, May 9, 2008 at 10:12 AM, Dimitris Rizopoulos <[EMAIL PROTECTED]> wrote:
have a look also at function rcor.test() from package
ltm.Best,Dimitris----Dimitris RizopoulosBiostatistical CentreSchool of Public
HealthCatholic University of LeuvenAddress: Kapucijnenvoer 35, Leuven,
BelgiumTel: +32/(0)16/336899Fax: +32/(0)16/337015Web:
http://med.kuleuven.be/biostat/
http://www.student.kuleuven.be/~m0390867/dimitris.htm----- Original Message
----- From: "Monica Pisica" <[EMAIL PROTECTED]>To: <r-help@r-project.org>Sent:
Thursday, May 08, 2008 9:05 PMSubject: [R] applying cor.test to a (m, n) matrix
Hi everybody,I would like to apply cor.test to a matrix with m rows and n
columns and get the results in a list of matrices , one matrix for p.val, one
for the statistic, one for the correlation and 2 for upper and lower confidence
intervals, something analog with cor() applied to a matrix.I have done my own
function to get a matrix of p.values and i suppose i can build similar
functions for all the others. But i have used for loops and i wonder if there
is any way to actually use one of the functions from the "apply" family to do
this in a quicker way.Here is my little function:cor.pval <- function(x, method
= c("pearson", "kendal", "spearman"), digit=8) {n <- dim(x)[2]pval <-
matrix(paste(rep("c", n*n), seq(1,n*n), sep = ""), n, n, byrow = T)for (i in
1:n) {for (j in 1:n){pval[i, j] <- cor.test(x[,i], x[,j], method =
method)$p.value } }pval <-
matrix(round(as.numeric(pval),digit), n, n, byrow = T)rownames(pval) <-
colnames(x)c!
olnames(pval) <- colnames(x)return(pval)}Thanks for any input,[EMAIL
PROTECTED] mailing listhttps://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do
read the posting guide http://www.R-project.org/posting-guide.htmland provide
commented, minimal, self-contained, reproducible code.Disclaimer:
http://www.kuleuven.be/cwis/email_disclaimer.htm
[EMAIL PROTECTED] mailing
listhttps://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting
guide http://www.R-project.org/posting-guide.htmland provide commented,
minimal, self-contained, reproducible code.
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