You are mistaken. apply() is *not* vectorized. It is a disguised loop.
For true vectorization at the C level, the answer must be no, as the
whole point is to treat the argument as a whole object and hide the
iterative details.
However, as you indicated, you can always manually randomize the
indexing that is being iterated over and even write a function to do
it if you like; e.g. (warning: esentially untested and probably clumsy
as well as buggy)
randapply <- function(X, MARGIN, FUN,...)
{
d <- dim(X)
ix <- as.list(rep(TRUE,length(d)))
for(i in MARGIN) ix[[i]] <- sample(seq_len(d[i]),d[i])
X <- do.call("[", c(list(X), ix))
apply(X,MARGIN,FUN,...)
}
> a <- array(1:24,dim = 2:4)
> randapply(a, 3,mean)
[1] 9.5 21.5 15.5 3.5
> randapply(a,3,mean)
[1] 21.5 3.5 15.5 9.5
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Thu, Nov 10, 2016 at 5:06 AM, Thomas Chesney
<thomas.ches...@nottingham.ac.uk> wrote:
> Is there a way to use vectorization where the elements are evaluated in a
> random order?
>
> For instance, if the code is to be run on each row in a matrix of length nBuy
> the following will do the job
>
> for (b in sample(1:nBuy,nBuy, replace=FALSE)){
>
> }
>
> but
>
> apply(nBuyMat, 1, function(x))
>
> will be run I believe, in the same order each time (Row1, then Row2, then
> Row3 etc.)
>
> This is important for building agent based models (the classic explanation of
> this is probably Huberman & Glance's response to Nowak & May's 1992 Nature
> article - Evolutionary games and computer simulations,
> http://www.pnas.org/content/90/16/7716.abstract)
>
> Thank you,
>
> Thomas
> http://www.nottingham.ac.uk/~liztc/Personal/index.html
>
>
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