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