regressions with many nested categorical covariates
- by eric
I have a few hundred thousand measurements where the dependent
variable is a probability, and would like to use logistic regression.
However, the covariates I have are all categorical, and worse, are all
nested. By this I mean that if a certain measurement has "city -
Phoenix" then obviously it is certain to have "state - Arizona" and
"country - U.S." I have four such factors - the most granular has
some 20k levels, but if need be I could do without that one, I think.
I also have a few non-nested categorical covariates (only four or so,
with maybe three different levels each).
What I am most interested in
is prediction - given a new observation in some city, I would like to
know the relevant probability/dependent variable. I am not interested
as much in the related inferential machinery - standard deviations,
etc - at least as of now. I am hoping I can afford to be sloppy.
However, I would love to have that information unless it requires
methods that are more computationally expensive.
Does anyone have any advice on how to attack this? I have looked into
mixed effects, but am not sure it is what I am looking for.