as for practical reasons.
On reviewing some code written by novices to R, I came across:
crossprod(x, y)[1,1]
I thought, "That isn't a very S way of saying that, I wonder what the penalty is for using 'crossprod'." To my surprise the penalty was substantially negative. Handily the client had S-PLUS as well -- there the sign of the penalty was as I had expected, but the order of magnitude was off.
Here are the timings of 1 million computations on vectors of length 1000. This is under Windows, R version 1.9.1 and S-PLUS 6.2 (on the same machine).
Command R S-PLUS sum(x * y) 28.61 97.6 crossprod(x, y)[1,1] 6.77 2256.2
Another example is when computing the sums of the columns of a matrix. For example:
set.seed(1) jjm <- matrix(rnorm(600), 5)
Timings for this under Windows 2000 with R version 2.0.1 (on an old chip running at about 0.7Ghz) for 100,000 computations are:
apply(jjm, 2, sum) 536.59 colSums(jjm) 18.26 rep(1,5) %*% jjm 15.41 crossprod(rep(1,5), jjm) 13.16
(These timings seem to be stable across R versions and on at least one Linux platform.)
Andy Liaw showed another example of 'crossprod' being fast a couple days ago on R-help.
Questions for those with a more global picture of the code:
* Is the speed advantage of 'crossprod' inherent, or is it because more care has been taken with its implementation than the other functions?
* Is 'crossprod' faster than 'sum(x * y)' because 'crossprod' is going to BLAS while 'sum' can't?
* Would it make sense to (essentially) use 'crossprod' in 'colSums' and its friends at least for the special case of matrices?
Patrick Burns
Burns Statistics [EMAIL PROTECTED] +44 (0)20 8525 0696 http://www.burns-stat.com (home of S Poetry and "A Guide for the Unwilling S User")
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