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:

[hidden email] [mailto:

[hidden email]] On Behalf Of Douglas

Bates

Sent: Thursday, February 26, 2009 3:50 PM

To: Jeff Evans

Cc:

[hidden email]
Subject: Re: [R] generalized linear mixed models with a beta distribution

On Thu, Feb 26, 2009 at 12:04 PM, Jeff Evans <

[hidden email]> wrote:

> 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.

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