Well, the best approach is not to model so many fixed effects. But, if you must, there are a few options. First, have you considered treating them as random effects and using a mixed effects linear model?
If you must build such a large model matrix for the fixed effects, the best thing to do is to use some functions in the Matrix namespace to use sparse matrices. For instance, fm <- Matrix:::lm.fit.sparse(sparse.model.matrix(~data$yourFactor), data$yourOutcomeVariable) where data$yourFactor is the factor variable with the postal IDs and data$yourOutcomeVariable is the DV for the regression. -----Original Message----- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Roy Lowrance Sent: Sunday, March 21, 2010 8:01 PM To: r-help@r-project.org Subject: [R] fixed effects regression Hi All: I am trying to move a model from Stata to R. It is a linear regression model with about 90,000 indicator variables. What is the best approach to follow in R? - Roy [[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. ______________________________________________ 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.
