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

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