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

From: <nflynn_at_ualberta.ca>
Date: Thu 14 Apr 2005 - 02:57:16 EST


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   http://pscl.stanford.edu/content.html
which also hosts code for hurdle models.


Consider using the function supplied in the post: https://stat.ethz.ch/pipermail/r-help/2005-March/066752.html for fitting negative binomial mixed effects models.


Check out these recent postings to the R list: http://finzi.psych.upenn.edu/R/Rhelp02a/archive/48429.html http://finzi.psych.upenn.edu/R/Rhelp02a/archive/48646.html
*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 http://otter-rsch.com/admbre/examples/nbmm/nbmm.html




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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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Thu Apr 14 03:02:05 2005

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