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 [[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.