Hi,
Would be very glad for help on this problem. Using this code:
temp<-function(x, bins, tot) {
return(as.numeric(lapply(split(x, bins), wtest, tot)));
}wtest <- function(x, y) {
return(wilcox.test(x,y)$p.value);
}rs <- function(x, bins) {
binCount <- length(split(x[,1], bins));
tot <- as.numeric(x);
result<-matrix(apply(x, 2, temp, bins, tot),
nrow=binCount, byrow=F);
rownames(result)<-names(split(x[,1], bins));
colnames(result)<-colnames(x);
return(result);
}where x is a matrix and bins is the grouping vector which can be used to split every column in x I get
>rs(x, bins)
to take ~100 s to execute if x has 22000 rows, 2 columns and bins split these in to 226 arrays of similar length. Thats all right but, if I instead increase to 3 columns it takes ~300 s and with 50 columns it takes > 13 h to execute. I can not understand why execution time doesnt increase linearly with the amount of columns. Memory status is all fine and I never need to start swapping.
I tried to remove the temp function and use a for-loop to iterate over the columns instead of using apply but it does not solve my problem.
Thanx!
/Henning, [EMAIL PROTECTED]
______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
