generalized linear mixed models with a beta distribution
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