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) ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
