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
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
On 05/22/2010 02:19 PM, Robin Jeffries 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
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