Thank you to Jim and Moshe. I will try the Rprof option as well as the
running the function to run on columns instead. Thank you.
jim holtman wrote:
Use Rprof to see where time is being spent. If it is in FUN, then
there is probably no way to "optimize" outside of changing the way FUN
works. So the first thing is to decide where time is being spent.
On Sun, Aug 24, 2008 at 6:35 PM, Adaikalavan Ramasamy
<[EMAIL PROTECTED]> wrote:
Hi,
I calculating the output of a function when applied to pairs of row from a
single matrix or dataframe similar to how cor() and pairs() work. This is
the code that I have been using:
pairwise.apply <- function(x, FUN, ...){
n <- nrow(x)
r <- rownames(x)
output <- matrix(NA, nc=n, nr=n, dimnames=list(r, r))
for(i in 1:n){
for(j in 1:n){
if(i >= j) next()
output[i, j] <- FUN( x[i,], x[j,] )
}
}
return(output)
}
I realize that the output of the pairwise operation needs to be scalar. Here
is an example. The actual function and dataset I want to use is more
complicated and thus the function runs slow for large datasets.
m <- iris[ 1:5, 1:4 ]
pairwise.apply(m, sum)
1 2 3 4 5
1 NA 19.7 19.6 19.6 20.4
2 NA NA 18.9 18.9 19.7
3 NA NA NA 18.8 19.6
4 NA NA NA NA 19.6
5 NA NA NA NA NA
Can I use apply() or any of it's family to optimize the codes? I have tried
playing around with outer, kronecker, mapply without any sucess.
Any suggestions? Thank you.
Regards, Adai
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