See package "glmnet". -- Bert
Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Tue, Jun 6, 2017 at 8:10 AM, Ravi Varadhan <ravi.varad...@jhu.edu> wrote: > More principled would be to use a lasso-type approach, which combines > selection and estimation in one fell swoop! > > > > Ravi > > ________________________________ > From: Ravi Varadhan > Sent: Tuesday, June 6, 2017 10:16 AM > To: r-help@r-project.org > Subject: Subject: [R] glm and stepAIC selects too many effects > > > If AIC is giving you a model that is too large, then use BIC (log(n) as the > penalty for adding a term in the model). This will yield a more parsimonious > model. Now, if you ask me which is the better option, I have to refer you to > the huge literature on model selection. > > Best, > > Ravi > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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 -- To UNSUBSCRIBE and more, see 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.
