generalized linear mixed models with a beta distribution

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generalized linear mixed models with a beta distribution

Jeff Evans-5
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.

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.

Jeff Evans
Michigan State University

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Re: generalized linear mixed models with a beta distribution

Douglas Bates-2
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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Re: generalized linear mixed models with a beta distribution

Jeff Evans-5
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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Re: generalized linear mixed models with a beta distribution

dave fournier
In reply to this post by Jeff Evans-5
You can fit this kind of model (and negative binomial) and more
difficult mixed models   with AD Model Builder's random effects module
which is now freely available at

     http://admb-project.org/


--
David A. Fournier
P.O. Box 2040,
Sidney, B.C. V8l 3S3
Canada
Phone/FAX 250-655-3364
http://otter-rsch.com

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Re: generalized linear mixed models with a beta distribution

Ben Bolker
In reply to this post by Jeff Evans-5

Jeff Evans-5 wrote
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
Sorry, but I wouldn't necessarily take comfort from this.  I must confess
that I can't keep the distinctions between marginal pseudo/quasi-likelihoods
in my head, but on what grounds are you confident that the number that
lme4 produces can be used for model selection? (I would guess that)
Some people would be happy using QAIC based on pseudo-likelihoods, some people wouldn't
be happy with anything other than a true likelihood (or approximation thereof).

  This discussion is probably better for r-sig-mixed-models ...

  Ben Bolker