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.
