Dear list
I'm trying to get some unadjusted estimates and 95% CI for a set of correlated count data (due to repeated measures on the same cluster) . To do this I was trying to run an over-dispersed poisson model using a glmer and MCMCglmm.
I want to use MCMCglmm as that's the package I wish to use for my main analysis. However, it seems to over-estimate the variance meaning that the mean value I get from the intercept only model y = XB + Var/2 (ch2 jarrod hadfield's course notes) is slightly greater than the actual mean. For example, if I fit the model
priortr <- list(R=list(V=1, nu=0.001))
mcmc.c11.cf2 <- MCMCglmm(totflct ~ 1, family="poisson", prior = priortr, data=uc11,
nitt = 100000, burnin = 10000, thin = 90)
summary(mcmc.c11.cf2)
I'm ignoring the random effect and assuming the additive over-dispersion term will capture all the extra variance. For a count rate of 4.69 in the data I get 4.79 and for a count of 5.2 I get 5.52. On the other hand, if I use glmer including a per observation random effect I get the correct means
re.uc12.cf <- glmer(totflct ~ (1|obs), family=poisson, data=uc12)
summary(re.uc12.cf)?
Is there something I missing here?
Regards
John Hodsoll