[R] glmmML vs. lmer - fitting overdispersed Poisson outcome.

[Sam Field]

Group,

I have count data with one observation per subject.  I would like to fit 
a glmm to these data in order to account for overdispersion in the 
outcome. The lmer() function does not appear to be able to handle data 
that have only one observation per-cluster id, even though separate 
variance components for the count and normal portions of the outcome are 
identifiable.  The glmmML() function does not seem to have problems with 
this (as the code below illustrates).

library(lme4)
library(glmmML)
u <- rnorm(100)
y <- rpois(100,exp(u))
id <- seq(1:100)
summary(glmmML(y~1,cluster=as.factor(id),family =poisson))
summary(lmer(y~1+(1|id),family='poisson'))  

I would like to use lmer() rather then glmmML(). Does anybody know of a 
way of getting the lmer() function to work with these kinds of data? 

Sam


-- 
Samuel H. Field
Division of Internal Medicine - University of Pennsylvania
CHERP - Philadelphia VA Medical Center
3900 Woodland Ave (9 East)
Philadelphia, PA 19104
(215) 823-5800 EXT. 6155 (Office)
(215) 823-6330 (Fax)