[RsigME] general GLMM questions
Ben Bolker
bolker at ufl.edu
Mon May 12 17:45:15 CEST 2008
BEGIN PGP SIGNED MESSAGE
Hash: SHA1
 lme4specific questions:

 6. Behavior of glmer: Does glmer really use AGQ, or just Laplace?
 Both? pp. 2832 of the "Implementation" vignette in lme4 suggest that
 a Laplace approximation is used, but I can't figure out whether this
 is an additional approximation on top of the AGQ/Laplace approximation
 of the integral over the random effects used in "ordinary" LMM. When
 I fit a GLMM with the different methods, the fitted objects are not
 identical but all the coefficients seem to be. (I have poked at the
 code a bit but been unable to answer this question for myself
 ... sorry ...)

> To answer this question I must again, I regret, distinguish between
> the CRAN version of the lme4 package and the Rforge development
> version of lme4.

> In the Rforge version the only method for generalized linear mixed
> models and for nonlinear mixed models is direct optimization of the
> Laplace approximation to the deviance. One of the Summer of Code
> projects that Google has funded for the R Foundation (see
> http://code.google.com/soc/2008/rf/about.html) is implementation of
> the Adaptive GaussHermite Quadrature approximation to the deviance.
> That will be implemented in the development version (i.e. the Rforge
> version) of the lme4 package. [snip]
~ Great!
~ A minor feature request: does it make sense to update the
documentation and code of glmer to make it clear that it does
*not* do AGQ at the moment? I guess that depends how soon you
would expect the GSoC code to come online ...
> I realize that it is somewhat irritating and confusing to readers of
> this list to have descriptions of the Rforge version of the package
> contrasted with the CRAN version. It is natural to expect that the
> Rforge version should be the version on CRAN. The reason that I have
> not yet released the Rforge to CRAN is because of problems with the
> mcmcsamp function in the Rforge version. If I moved the Rforge
> version to CRAN then code from Harald Baayen's book and probably code
> from Gelman and Hill's book would no longer work. Software versions
> can be changed much more readily than can editions of a book. I think
> there is a way around the problem with mcmcsamp but I won't be able to
> say for sure until it is coded and tested, which will take time. I
> don't want to predict exactly how much time  I always manage to
> underestimate drastically.
~ If (just hypothetically speaking) I were writing a review that
would be published in 6 months or so, do you have a recommendation?
(Would you still trust GLMM/mcmcsamp results from the CRAN version?)
 (The glmmML package claims to fit via Laplace or GaussHermite
 quadrature (with nonadaptive, but adjustable, number of quad points
  so it's at least theoretically possible?)

> Yes. I think the adaptive part is important (in fact, I know it is
> important) and probably more important than the distinction between
> the Laplace approximation and GaussHermite quadrature. That is, you
> gain more from using the Laplace approximation at the conditional
> modes of the random effects (which is the "adaptive" part) than
> increasing the number of GaussHermite quadrature points at the
> marginal modes. The tricky bit of AGQ or Laplace is determining the
> conditional modes and that part is already done.
~ OK, I was confused about the distinction in the meaning of
"adaptive" (as you pointed out previously on rhelp ...) I think
I have it now.
https://stat.ethz.ch/pipermail/rhelp/2007March/128043.html
~ thanks for your help!
~ Ben Bolker
BEGIN PGP SIGNATURE
Version: GnuPG v1.4.6 (GNU/Linux)
Comment: Using GnuPG with Mozilla  http://enigmail.mozdev.org
iD8DBQFIKGYLc5UpGjwzenMRAoBnAJ0R7w6FHL8uTaz3OKmMvJWHByS0tACdEy4x
ABU30kHrB/eNoXcHMvJJ4LE=
=whoY
END PGP SIGNATURE
More information about the Rsigmixedmodels
mailing list