Are you trying to fit a Poisson GLMM with Gamma random effects? I don't
think you can do that using (g)lmer, which assumes a Gaussian
distribution for the random effects. You might have a look at the hnlmix
function in Jim Lindsey's repeated package. Or you could use Bayesian
methods in JAGS, BUGS
I'm trying to figure out how to carry out a Poisson regression fit to
longitudinal data with a gamma distribution with unknown shape and
scale parameters.
I've tried the 'lmer4' package's glmer() function, which fits the
Poisson regression using:
library('lme4')
fit5<- glmer(seizures ~ time
2 matches
Mail list logo
