Dear all,
I would like to run a simple regression model y~x1+x2+x3+...
The problem is that I have a lot of independent variables (xi) -- around
one hundred -- and that some of them are categorical with a lot of
categories (like, for example, ZIP code). One straightforward way would be
to (a) transform all categorical variables into 1/0 dummies and (b) enter
all the variables into an lm model. But I'm not sure whether this is very
efficient, especially since the analysis is exploratory in nature and I
expect that many of the xi will have no significant impact on y.
Is there a R library that can handle such a setting? I have read about
"Hierarchical Bayesian variance components models" that have been used with
ZIP data (www.jstor.org/stable/10.2307/4129723), but I'm not sure to which
extent there is a function in R to do that in a straightforward manner.
Thanks,
Michael
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