"Liaw, Andy" <[EMAIL PROTECTED]> writes: > I tried the following on an Opteron 248, R-1.9.0 w/Goto's BLAS: > > > y <- matrix(rnorm(14000*1344), 1344) > > x <- matrix(runif(1344*503),1344) > > system.time(fit <- lm(y~x)) > [1] 106.00 55.60 265.32 0.00 0.00 > > The resulting fit object is over 600MB. (The coefficient compoent is a 504 > x 14000 matrix.) > > If I'm not mistaken, SAS sweeps on the extended cross product matrix to fit > regression models. That, I believe, in usually faster than doing QR > decomposition on the model matrix itself, but there are trade-offs. You > could try what Prof. Bates suggested.
Hmm. Shouldn't be all that much faster, but it will produce the Type I SS as you go along, whereas R probably wants to fit the 15 different models. I'm still surprised that R/S-PLUS manages to use a full 15 minutes on a single response variable. It might be due to the singularities -- the SAS code indicated that there was a nesting issue with the "A" factor in the last 4-factor interaction. If so, a reformulation of the model might help. -- O__ ---- Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
