A couple of remarks on vQ's naive benchmark:
f.rep = function(n, m) replicate(n, rnorm(m))
I suppose you meant
f.rep = function(n, m) replicate(n, mean(rnorm(m)))
which doesn't make a substantial speed difference, though.
f.pat = function(n, m) colMeans(array(rnorm(n*m), c(n, m)))
system.time(f.pat(1000, 1000))
system.time(f.rep(1000, 1000))
makes me believe that there is no significant difference in efficiency
between the 'professionally-looking' replicate-based solution and the
'as fast as possible' pat's solution.
True, I get the same timing results on my machine. But then you
should also point out that the original for-loop:
f.for = function(n, m) { res <- numeric(n); for (i in 1:n) res[i] <-
mean(rnorm(m)); res }
is exactly as fast as replicate(). So apart from "looking more
professional", there isn't any difference between an explicit loop and
replicate().
Perhaps loops in R aren't always as slow (compared to matrix
operations) as one seemed to think. I ran into a similar issue with a
simple benchmark the other day, where a plain loop in Lua was faster
than vectorised code in R ...
I have to say, though, that like Patrick I assumed the goal was to
obtain a large number of replicates for relatively small sets of
random numbers, in which case the matrix solution is indeed faster
(though not as much as I would have thought):
> system.time(f.for(100000, 100))
user system elapsed
4.212 0.025 4.273
> system.time(f.rep(100000, 100))
user system elapsed
4.109 0.028 4.172
> system.time(f.pat(100000, 100))
user system elapsed
1.580 0.134 1.739
Best regards,
Stefan Evert
[ stefan.ev...@uos.de | http://purl.org/stefan.evert ]
PS: Don't feed trolls who say that Lua is better than R. ;-)
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