New Variant of Same Question: bias corrected logit estimates

Ben Bolker · 2013-04-12T20:28:28Z

Paul Johnson writes: > > Dear R-sig-mixed: > > I was struck today by the way the Internet has accelerated research. > At one time, it might have taken a month or two to track down the > articles on this problem and conclude I need to ask for advice. Now, > however, I realize the need within hours. > > Recall the question that started us debating a few days ago was a > logistic regression in which OP noticed the mis-match between the > predicted probability of success and

Ben Bolker

Fri, Apr 12, 2013 1:28 PM

Paul Johnson <pauljohn32 at ...> writes:

I don't really know the answer to the full question, but I would
venture this:

  * There is no explicit bias-reduction capacity built into the
fixed-effects estimation component of glmer
 * I'm aware of Firth's algorithm and have used the R implementations
but haven't read the paper/don't know the details
 * glmer does handle some of the typical problems with 'rare events'
by doing shrinkage across random effects, but if the events are
rare in the *entire* data set (and not just in individual/small/
undersample regions), I don't think that will help
 * Vince Dorie and Andrew Gelman's blme package, or Jarrod Hadfield's
MCMCglmm package, could be used with more or less informative priors
to achieve a degree of shrinkage.

  I don't know whether there's a clever way to adapt glmer
itself to do shrinkage/bias correction on a single sample.

  Hopefully others with more knowledge will chime in.

New Variant of Same Question: bias corrected logit estimates

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