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

Ben Bolker bolker at ufl.edu
Wed May 12 06:11:58 CEST 2010

David Duffy wrote:
> On Tue, 11 May 2010, 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.
> If you have only a "single level" of random effects eg simple clusters, 
> then gamm() [mgcv] or spm() [SemiPar].  

  I don't see how this works -- gamm() will do the family options from
glm (including negative.binomial() from MASS with a *fixed*
overdispersion/theta/k parameter, but not? estimating the overdispersion
as part of the fitting process?), spm() doesn't seem to have a NB option?

  I would love to be wrong on this and corrected on it.

  Anyone should feel free to update <http://glmm.wikidot.com/faq> if
they have a better way, or a worked example ...

More information about the R-sig-mixed-models mailing list