[18621] in s-news-athena
Re: [S] Summary: generalized linear mixed models
daemon@ATHENA.MIT.EDU (Douglas Bates)
Tue Jul 13 16:01:35 1999
To: Melanie Wigg <mbwigg@navigator.math.uwaterloo.ca>
Cc: s-news@wubios.wustl.edu
From: Douglas Bates <bates@stat.wisc.edu>
Date: 13 Jul 1999 14:55:17 -0500
In-Reply-To: Melanie Wigg's message of "Tue, 13 Jul 1999 13:55:30 -0400 (EDT)"
Message-Id: <6rso6stjdm.fsf@verdi.stat.wisc.edu>
As one of the developers of the NLME library, I thought I would
contribute my bit to this discussion.
Your respondents are quite correct - the NLME 3.0 library does not
have facilities for generalized linear mixed models (although I would
take exception to Ming Ji's statement "BTW, GLMM is far more
general and difficult than NLME." - that is simply not true).
During a recent visit, Bill Venables took the opportunity to educate
us regarding generalized linear models. He and Jose' Pinheiro and I
have come to the conclusion that the hooks to implement the Penalized
Quasi-Likelihood (PQL) method for GLMM's are there in the NLME
library. It would not be trivial but it would be possible to combine
pieces of the glm and lme functions to do PQL estimation. Neither
Jose' nor I will likely have time to do that in the near future. Even
if we did have time, it might not be the best idea as the PQL method
gets some bad press in the GLMM literature due to biases.
I think the preferred method for estimating GLMM's is that in the new
PROC NLMIXED of SAS version 7. That PROC used an adaptive Gaussian
quadrature for the integration with respect to the random-effects that
is part of the definition of the likelihood. Interestingly enough,
the methods in this SAS PROC are based on a paper by Pinheiro and
Bates about approximations to the log-likelihood for the nonlinear
mixed-effects model although, at the time we wrote that, we were not
thinking of generalized linear mixed models.
There are some problems with implementing a method like the adaptive
Gaussian quadrature in S. Within each iteration of an optimization
there are parts of the calculation that are not easily vectorized. It
is likely that a language like omega (http://www.omegahat.org/) would
be more effective there.
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