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

From: Prof Brian Ripley <ripley_at_stats.ox.ac.uk>
Date: Wed, 12 Mar 2008 19:42:16 +0000 (GMT)

glmmPQL can fit the same GLM families as glm() can -- it does not list _any_ .

Howver, the beta distribution does not give a GLM family and hence your subject line is strictly about a non-existent concept. I'm presuming that you want to model the logit of the mean of a beta by a random effects model -- it is unclear what you want to do with the other parameter.

Note that the beta does fit into the framework of package gamlss, but I am not aware of an option for random effects in that framework.

On Wed, 12 Mar 2008, Craig A Faulhaber wrote:

> Greetings,
>
> I am interested in using a generalized linear mixed model with data that
> best fits a beta distribution (i.e., the data is bounded between 0 and 1
> but is not binomial). I noticed that the beta distribution is not
> listed as an option in the "family objects" for glmmPQL or lmer. I
> found a thread on this listserve from 2006 ("[R] lmer and a response
> that is a proportion") that indicated that there was no package

https://stat.ethz.ch/pipermail/r-help/2006-December/121567.html

> available for mixed effects models with a beta distribution at that
> time. This thread also indicated that package betareg did not allow
> inclusion of random effects.

But it did suggest modelling this in nlme via a variance specification, and that remains a good suggestion.

> Does anyone know of a package or code for a generalized linear mixed
> model that allows a beta distribution? Transforming my data might allow
> me to use another family, but I would rather not transform the data if
> possible. Thanks for your help!
>
> Sincerely,
> Craig Faulhaber

-- 
Brian D. Ripley,                  ripley_at_stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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