[R] fixed effects regression
HDoran at air.org
Mon Mar 22 15:08:16 CET 2010
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.
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Roy Lowrance
Sent: Sunday, March 21, 2010 8:01 PM
To: r-help at r-project.org
Subject: [R] fixed effects regression
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?
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