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

[Martin Henry H. Stevens]

Hi Sam,
I don't understand how you can estimate a separate id variance other  
than the residual variance, with one observation per subject. I would  
think the proper model would be
glm(y ~ 1, family='poisson)
Cheers,
Hank

On Oct 15, 2007, at 12:05 PM, Sam Field wrote:

> 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)
>
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Dr. Hank Stevens, Associate Professor
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