Some comments
1) 'Writing R Extensions' has a chapter about looking into bottlenecks
('profiling'). Had you given a working example, I might have shown you
some results.
One thing which is clearly suboptimal is to grow components (in your case
by cbind) rather than allocate them initially and assign the column
needed.
2) You are using matrix algebra extensively, and for that you want to use
an optimized BLAS. We provide such on CRAN for Windows' users, ready for
download. This can speed up crossprod() and tcrossprod() severalfold (amd
more on a platform where threaded BLAS and multi-core CPUS are available).
3) I've referred to 'S Programming' already this morning. In its chapter
on efficiency, it shows that for some problems writing C or Fortran is the
way to get real efficiency. Yours would appear to be one in which writing
Fortran to call LAPACK routines to do this would take only a few minutes.
On Wed, 30 Apr 2008, steven wilson wrote:
Dear list members;
The code given below corresponds to the PCA-NIPALS (principal
component analysis) algorithm adapted from the nipals function in the
package chemometrics. The reason for using NIPALS instead of SVD is
the ability of this algorithm to handle missing values, but that's a
different story. I've been trying to find a way to improve (if
possible) the efficiency of the code, especially when the number of
components to calculate is higher than 100. I've been working with a
data set of 500 rows x 700 variables. The code gets really slow when
the number of PC to calculate is for example 600 (why that number of
components?....because I need them to detect outliers using another
algorithm). In my old laptop running Win XP and R 2.7.0 (1GB RAM) it
takes around 6 or 7 minutes. That shouldn't be a problem for one
analysis, but when cross-validation is added the time increases
severely.....Although there are several examples on the R help list
giving some with 'hints' to improve effciency the truth is that I
don't know (or I can't see it) the part of the code that can be
improved (but it is clear that the bottle neck is the for and while
loops). I would really appreciate any ideas/comments/etc...
Thanks
#################################################################
pca.nipals <- function(X, ncomp, iter = 50, toler = sqrt(.Machine$double.eps))
# X...data matrix, ncomp...number of components,
# iter...maximal number of iterations per component,
# toler...precision tolerance for calculation of components
{
Xh <- scale(X, center = TRUE, scale = FALSE)
nr <- 0
T <- NULL; P <- NULL # matrix of scores and loadings
for (h in 1:ncomp)
{
th <- Xh[, 1]
ende <- FALSE
while (!ende)
{
nr <- nr + 1
ph <- t(crossprod(th, Xh) * as.vector(1 /
crossprod(th)))
ph <- ph * as.vector(1/sqrt(crossprod(ph)))
thnew <- t(tcrossprod(t(ph), Xh) *
as.vector(1/(crossprod(ph))))
prec <- crossprod(th-thnew)
th <- thnew
if (prec <= (tol^2)) ende <- TRUE
if (it <= nr) ende <- TRUE # didn't converge
}
Xh <- Xh - tcrossprod(th)
T <- cbind(T, th); P <- cbind(P, ph)
nr <- 0
}
list(T = T, P = P)
}
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--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
