[R] Negative binomial GLMMs in R
Hans.Skaug@mi.uib.no
Hans.Skaug at mi.uib.no
Wed Mar 23 16:00:31 CET 2005
Dear R-users,
A recent post (Feb 16) to R-help inquired about fitting
a glmm with a negative binomial distribution.
Professor Ripley responded that this was a difficult problem with the
simpler Poisson model already being a difficult case:
https://stat.ethz.ch/pipermail/r-help/2005-February/064708.html
Since we are developing software for fitting general nonlinear random
effects models we thought this might be an interesting challenge.
We contacted Professor Ripley who kindly directed us to the epilepsy data
in Venables & Ripley section 10.4 (4th ed.). While V&B did not actually
fit a negative binomial to these data they did refer to evidence
of overdispersion in the response. Fortunately Booth et al. (2003) did
attempt to fit this model with a negative binomial which gave us something
to which we could compare our results. Booth et al. fitted two forms of
the model a simpler one and a more complicated model. They reported some
difficulty fitting the more complicated model. We found that we could
reliably fit (MLE) both the complicated and simpler model in 20 seconds
or less (although the more complicated turns out to be overparameterized)
Using the random effects module of AD Model Builder we have developed
a shared library (Windows dll) that can be called from R via the driver
function glmm.admb(). The function can be downloaded from
http://otter-rsch.com/admbre/examples/nbmm/nbmm.html
The two models of Booth et al are fit by the commands:
glmm.admb(y~Base*trt+Age+Visit,random=~1,group="subject",data=epil2)
glmm.admb(y~Base*trt+Age+Visit,random=~Visit,group="subject",data=epil2)
I will be happy to receive feedback on the function glmm.admb().
Best regards,
Hans Skaug
Reference:
Booth J.G.; Casella G.; Friedl H.; Hobert J.P, Negative binomial loglinear
mixed models.
Statistical Modelling, October 2003, vol. 3, no. 3, pp. 179-191
More information about the R-help
mailing list