Alternate to for-loop
Wacek Kusnierczyk wrote:
Patrick Burns wrote:
If the goal is to "look" professional, then
'replicate' probably suits. If the goal is to
compute as fast as possible, then that isn't
the case because 'replicate' is really a 'for'
loop in disguise and there are other ways.
Here's one other way:
function (size, replicates, distfun, ...)
{
colMeans(array(distfun(size * replicates, ...), c(size,
replicates)))
}
a naive benchmark: f.rep = function(n, m) replicate(n, rnorm(m)) 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.
I think Wacek is largely correct. First off, a correction: the dimensions on the array if 'f.pat' should be c(m, n) rather than c(n, m). What I'm seeing on my machine is that the array trick seems always to be a bit faster, but only substantially faster if 'm' (that is, the number being summed) is smallish. That makes sense: loops are "slow" because of the overhead of doing the calling. When each call takes a lot of time, the overhead becomes insignificant. Patrick Burns patrick at burns-stat.com +44 (0)20 8525 0696 http://www.burns-stat.com (home of "The R Inferno" and "A Guide for the Unwilling S User")
vQ