Dear R community,

I have two questions regarding fitting GLMM using maximum likelihood method.
The first one arises from trying repeat an analysis in the Breslow and
Clayton 1993 JASA paper. Model 3 of the epileptic dataset has two random
effects, one subject specific, and one observation specific. Thus if we
count random effects, there are more parameters than observations. When I
try to run the following code, I get an error saying: "Error in
mer_finalize(ans) : q = 295 > n = 236".

require (lme4)
require (glmmAK)
data(epilepticBC)
dat = epilepticBC
dat$rand=1:nrow(dat)
dat$V4=dat$visit==4
formula1 = Seizure ~ Base + Trt + I(Trt*Base) + Age + V4
fit=lmer (update (formula1, .~. + (1|id) + (1|rand)), family=poisson,
data=dat, nAGQ=1)

Is it true that there is no way to fit such a model in an ML analysis? In
other words, is there a way to approximate the likelihood of fixed effects
and variance components without relying on estimates of random effects?

The second question is that when it is possible to obtain MLE of a GLMM
model, how can I obtain an estimated variance of the variance component
estimates using lmer or other functions?

Thank you very much for your help!

Youyi Fong

-------------------------------------------------------------------------------------
Youyi Fong, Graduate Student, Department of Biostatistics
University of Washington, Box 357232, Seattle, WA 98195

	[[alternative HTML version deleted]]


