[R] mixed effects models - negative binomial family?

[Ben Bolker]

Constantinos Antoniou <antoniou <at> central.ntua.gr> writes:

> 
> Hello all,
> 
> I would like to fit a mixed effects model, but my response is of the  
> negative binomial (or overdispersed poisson) family. The only (?)  
> package that looks like it can do this is glmm.ADMB (but it cannot  
> run on Mac OS X - please correct me if I am wrong!) [1]
> 
> I think that glmmML {glmmML}, lmer {Matrix}, and glmmPQL {MASS} do  
> not provide this "family" (i.e. nbinom, or overdispersed poisson). Is  
> there any other package that offers this functionality?

 You'll probably get more complete/informed information
shortly, but ... you may not be able to get a negative
binomial distribution per se, but other versions
of "overdispersed Poisson" are indeed possible.  glmmPQL
will let you use the quasipoisson family, which allows for
overdispersion in a phenomenological way; more mechanistically,
observation-level random effects on the scale of the
linear predictor (log for a GLMM with family=poisson)
lead to a lognormal-Poisson distribution, which has similar
properties to the NB.  I suspect you can do this in lmer
(lme4 package), which does GLMMs if you specify the family
argument.

See:

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=11393830&dopt=Abstract
(analysis done in SAS but probably completely feasible in R at this point)

  Ben