Instead of rowMeans(m[,groups == uGroups[gg], drop=FALSE]), I used
apply(m[,groups == uGroups[gg], drop=FALSE], 1, mean), which really slow down
the processing. I modified your codes slightly so that I can use sd, median,
and mad also. Better and efficient approach ?> Date: Sun, 13 Jul 2008 18:21:24
-0700> From: [EMAIL PROTECTED]> To: [EMAIL PROTECTED]> Subject: Re: [R]
Computing row means for sets of 2 columns> CC: [EMAIL PROTECTED]> > m <-
matrix(1:40, ncol=4);> groups <- rep(1:2, each=2);> uGroups <- unique(groups);>
mMeans <- matrix(NA, nrow=nrow(m), ncol=length(uGroups));> for (gg in
seq(along=uGroups)) {> mMeans[,gg] <- rowMeans(m[,groups == uGroups[gg],
drop=FALSE]);> }> > (Preallocation of result matrix is more memory efficient
than using> cbind() or similar!)> > /Henrik> > On Sun, Jul 13, 2008 at 6:03 PM,
Daren Tan <[EMAIL PROTECTED]> wrote:> >> > Is there a better or more efficent
approach than this without the use of t() ?> >> >> (m <- matrix(1:40, n!
col=4)) [,1] [,2] [,3] [,4] [1,] 1 11 21 31 [2,] 2 12 22 32 [3,] 3 13 23 33
[4,] 4 14 24 34 [5,] 5 15 25 35 [6,] 6 16 26 36 [7,] 7 17 27 37 [8,] 8 18 28 38
[9,] 9 19 29 39[10,] 10 20 30 40> >> (groups <- rep(1:2, each=2))[1] 1 1 2 2>
>> (m.mean <- t(aggregate(t(m), by=list(groups), mean))) [,1] [,2]Group.1 1 2V1
6 26V2 7 27V3 8 28V4 9 29V5 10 30V6 11 31V7 12 32V8 13 33V9 14 34V10 15 35> >
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