generalized linear mixed models with a beta distribution

Thanks for responding Doug. I'm sure SAS just hasn't gotten around to
releasing their code yet.

lme4 does have a leg up on GLIMMIX in other areas, though.
The latest SAS release (9.2) is now able to compute the Laplace
approximation of the likelihood, but it will only fit an overdispersion
parameter using pseudo-likelihoods which can't be used for model selection.
I'm not sure what lme4 is doing differently through the quasi-distributions
that allows this, but it's enormously useful.

Jeff

-----Original Message-----
From: dmbates at gmail.com [mailto:dmbates at gmail.com] On Behalf Of Douglas
Bates
Sent: Thursday, February 26, 2009 3:50 PM
To: Jeff Evans
Cc: r-help at r-project.org
Subject: Re: [R] generalized linear mixed models with a beta distribution

Has there been any follow up to this question? I have found myself

wondering

the same thing: How then does SAS fit a beta distributed GLMM? It also

fits

the negative binomial distribution.

When SAS decides to open-source their code we'll be able to find out.

Both of these would be useful in glmer/lmer if they aren't 'illegal' as
Brian suggested. Especially as SAS indicates a favorable delta BIC of over
1000 when I fit the beta to my data (could be the beginning of a great
song..) versus my original binomial fit.

Definitions of generalized linear mixed models are not entirely
straightforward, at least for me.  I'm making some progress but, as
always, it is slower than one would like it to be.