[R-sig-ME] Mixed model and negative binomial distribution
David Atkins
datkins at u.washington.edu
Thu Oct 4 07:20:59 CEST 2012
Seth--
You might take a look at a paper we wrote that is a tutorial on mixed
model "count regression" -- including negative binomial and various
zero-altered models. It also has a fairly extensive R code "appendix".
The draft / data / R code can be found:
http://depts.washington.edu/cshrb/newweb/statstutorials.html
It's the following paper:
Atkins, D. C., Baldwin, S., Zheng, C., Gallop, R. J., & Neighbors, C.
(in press). A tutorial on count regression and zero-altered count models
for longitudinal addictions data.
Finally, you can fit a type of over-dispersed Poisson model using
glmer() in lme4, or a true negative binomial mixed model using glmmADMB
package. (I *think* an example of the latter is in the R code, though
we focused mostly on lme4 and MCMCglmm.)
Hope that helps.
cheers, Dave
--
Dave Atkins, PhD
University of Washington
datkins at u.washington.edu
http://depts.washington.edu/cshrb/
August 1 - October 30:
Universitat Zurich
Psychologisches Institut
Klinische Psychologie
Binzmuhlestrasse 14/23
CH-8050 Zurich
+41 44 635 71 75
Dear mixed-model brain trust:
I am comparing snag (dead tree) densities 1 year and 5 years after
silvicultural treatment in forest plots to densities prior to treatment. In
nlme, my model is
lme(snagnum~treatment, random=(~1|plot), correlation=corExp(form=~year)).
{Treatment is a factor with values of Pre/1-year post/5-year post}. This
gives reasonable output, but I'm having a niggling doubt that I should be
using something akin to a negative binomial distribution, since about half
of the values are zeros (i.e., many plots had no snags prior to treatment,
and did not gain additional snags as a result of treatment). Can anyone
suggest an appropriate package and associated syntax for doing this mixed
model based on an alternative probability density function?
--Seth W. Bigelow
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