I've uploaded a package colSums_1.0.tar.gz to CRAN /src/contrib/Devel. It
contains functions colSums, colMeans, colVars, colStdevs, rowSums, rowMeans,
rowVars, and rowStdevs. These do simple, fast arithmetic on columns/rows of a
matrix, or more generally across dimensions of an array, e.g. colSums(m)
apply(m, 2, sum) but faster. They should be compatible with the corresponding
S-Plus functions.
The core code was written by Doug Bates <bates at stat.wisc.edu>, as
package
"MatUtils", and posted to R-help on July 19, 2001. Many thanks to
Doug, Peter
Dalgaard <p.dalgaard at biostat.ku.dk>, Thomas Lumley <tlumley at
u.washington.edu>,
and Prof Brian Ripley <ripley at stats.ox.ac.uk> for their assistance; see
the
R-help thread "colSums in C".
- Brian Ripley has taken up the torch to improve these functions further and
put them in R-devel, hopefully (I think) for R-1.5.0. That's why my
package
is in /src/contrib/Devel; it should be obsolete by the next release of R! I
expect complete argument compatibility, though, since we have both tried to
be compatible with S-Plus.
- I detect NA's with a trick that Thomas Lumley suggested (it's not NA
or NaN
if x==x), which works really great for me, but may not be portable. If it
doesn't work for you, try modifying the #define line in colSums.c. I
would
like to hear about platforms where this fails.
- colVars is very naive, e.g. I'm probably exacerbating roundoff error when
mu >> sigma. I personally don't worry because in finance, mu
(return) is
never >> sigma (risk) :-). The S-Plus documentation for colVars claims
they
do something fancy with the "two-pass method described in Chan, Golub,
and
LeVeque (1983)" that I don't know anything about.
- I convert integers, logicals, and complex immediately to reals, which is
probably inefficient. Brian Ripley's version seems to do a better job.
- S-Plus does not have "rowStdevs" for reasons unknown, since it is
simply
defined as rowStdevs(x, ...) <- function(x, ...) sqrt(rowVars(x, ...)).
- For me, colSums is about 23x faster than apply (on a 400 x 40000 matrix).
--
-- David Brahm (brahm at alum.mit.edu)
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