[R-sig-ME] Negative binomial mixed effects modeling

[David Atkins]

My preference would be to use MCMCglmm, which will fit an over-dispersed 
Poisson GLMM (ie, an extra observation-level random-effect is included 
to capture the over-dispersion).

If you specifically need to fit a negative-binomial mixed-effects 
model... then I don't have anything to add over the other's points.

cheers, Dave


David Zajanc wrote:
 > Hi,
 >
 >
 >
 > I am looking into ways to fit a negative binomial mixed effects model to
 > our data, but have not found any obvious methods in searching through
 > the archives of this list.  I noticed suggestions for using the
 > quasipoisson distribution, but our dataset appears better suited for the
 > negative binomial model, based on past analyses using GLM.  It appears
 > that "glmer" of package "lme4" is not currently capable of fitting a
 > negative binomial model, though please correct me if I'm wrong about
 > this.  Any feedback or suggestions would be welcomed.


-- 
Dave Atkins, PhD
Research Associate Professor
Department of Psychiatry and Behavioral Science
University of Washington
datkins at u.washington.edu

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