[R] Regression with sparse matricies

Douglas Bates bates at stat.wisc.edu
Sun May 23 14:16:54 CEST 2010

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

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-Models at R-project.org mailing list.

On Sat, May 22, 2010 at 2:19 PM, Robin Jeffries <rjeffries at 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]]
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