[R] Summary: GLMMs: Negative Binomial family in R

nflynn@ualberta.ca nflynn at ualberta.ca
Wed Apr 13 18:57:16 CEST 2005

Here is a summary of responses to my original email (see my query at the
bottom).  Thank you to Achim Zeileis , Anders Nielsen, Pierre Kleiber  and Dave
Fournier who all helped out with advice.  I hope that their responses will help
some of you too.

Check out
glm.nb() from package MASS fits negative binomial GLMs.

For known theta, you can plug negative.binomial(theta) into glmmPQL()
for example. (Both functions are also available in MASS.)

Look at package zicounts for zero-inflated Poisson and NB models. For
these models, there is also code available at
which also hosts code for hurdle models.

Consider using the function supplied in the post:
for fitting negative binomial mixed effects models.


Check out these recent postings to the R list:
*this refers to the  random effects module of AD Model Builderthat can be called
from R via the driver functon glmm.admb(). Their example problem fits the model
with a negative binomial. The function can be downloaded from

My Original Query

Greetings R Users!

I have a data set of count responses for which I have made repeated observations
on the experimental units (stream reaches) over two air photo dates, hence the
mixed effect.  I have been using Dr. Jim Lindsey's GLMM function found in his
"repeated" measures package with the "poisson" family.

My problem though is that I don't think the poisson distribution is the right
one to discribe my data which is overdispersed; the variance is greater than
the mean.  I have read that the "negative binomial" regression models can
account for some of the differences among observations by adding in a error
term that independent of the the covariates.

I haven't yet come across a mixed effects model that can use the "negative
binomial" distribution.

If any of you know of such a function - I will certainly look forward to hearing
from you!  Additionally, if any of you have insight on zero-inflated data, and
testing for this, I'd be interested in your comments too.  I'll post a summary
of your responses to this list.

Best Regards,
Nadele Flynn, M.Sc. candidate.
University of Alberta

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