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.varadhan at 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 at 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]]
>
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