As Frank mentioned in his reply, expecting to estimate tens of thousands of fixed-effects parameters in a logistic regression is optimistic. You could start with a generalized linear mixed model instead
library(lme4) fm1 <- glmer(resp ~ 1 + (1|f1) + (1|f2) + (1|f1:f2), mydata, binomial)) If you have difficulty with that it might be best to switch the discussion to the r-sig-mixed-mod...@r-project.org mailing list. On Sat, May 22, 2010 at 2:19 PM, Robin Jeffries <rjeffr...@ucla.edu> wrote: > I would like to run a logistic regression on some factor variables (main > effects and eventually an interaction) that are very sparse. I have a > moderately large dataset, ~100k observations with 1500 factor levels for one > variable (x1) and 600 for another (X2), creating ~19000 levels for the > interaction (X1:X2). > > I would like to take advantage of the sparseness in these factors to avoid > using GLM. Actually glm is not an option given the size of the design > matrix. > > I have looked through the Matrix package as well as other packages without > much help. > > Is there some option, some modification of glm, some way that it will > recognize a sparse matrix and avoid large matrix inversions? > > -Robin > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.