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
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