On Thu, 11 Sep 2008, Markus Schmidberger wrote:

Hi,

I found different results calculating the rowMeans by the function rowMeans() and a simple for-loop. The differences are very low. But after this

Indeed, but the C code (rowMeans) is likely to be more accurate as it uses an extended-precision accumulator.

calculation I will start some optimization algorithms (BFGS or CG) and there I get huge differences (from the small changes in the beginning or start values, I changed nothing else in the code).
How I can avoid these differences between sum-loops and sum-functions?

You cannot. What you can do is work on making what you do with these inputs numerically stable: unless you do so your end results will have very little value. (For example, are you finding different local minima, in which case you need to decide how to treat that possibility?)

I suggest reading an introductory book on Numerical Analysis, or

Monahan, J. F. (2001) Numerical Methods of Statistics. Cambridge: Cambridge. Chapter 2.

or

Press,W. H., Teukolsky, S. A., Vetterling, W. T. and Flannery, B. P. (2007) Numerical Recipes. The Art of Scientific Programming. Third Edition. Cambridge. Section 1.1 (I think).

Attached a small testcode using data form Bioconductor.

Best
Markus


library(affy)
data(affybatch.example)
mat <- exprs(affybatch.example)[1:100,1:3]
mat <- exp(1)*mat
mat <- asinh(mat)

rowM1<- rowMeans(mat)

t=rep(0,100) # Vektor mit 0en
for(i in 1:100){
 for(j in 1:3)
     t[i] <- t[i] + mat[i,j]
}
rowM2 <- t/3

m1 <- mat - rowM1
m2 <- mat -rowM2

print(m1-m2)

sessionInfo()
R version 2.7.1 (2008-06-23)
i386-pc-mingw32

locale:
LC_COLLATE=German_Germany.1252;LC_CTYPE=German_Germany.1252;LC_MONETARY=German_Germany.1252;LC_NUMERIC=C;LC_TIME=German_Germany.1252

attached base packages:
[1] tools stats graphics grDevices utils datasets methods [8] base other attached packages: [1] affy_1.18.2 preprocessCore_1.2.0 affyio_1.8.0 [4] Biobase_2.0.1 --
Dipl.-Tech. Math. Markus Schmidberger

Ludwig-Maximilians-Universität München
IBE - Institut für medizinische Informationsverarbeitung,
Biometrie und Epidemiologie
Marchioninistr. 15, D-81377 Muenchen
URL: http://ibe.web.med.uni-muenchen.de Mail: Markus.Schmidberger [at] ibe.med.uni-muenchen.de
Tel: +49 (089) 7095 - 4599

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Brian D. Ripley,                  [EMAIL PROTECTED]
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
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