Dear Jarrod
Thanks for your reply. I was using 4, but all give a similar answer
1. 4.796014
2. 4.792395
3. 4.798754
4 4.790677
As I said I'm not sure I'm missing something obvious?
Cheers
John
-----Original Message-----
From: Jarrod Hadfield [mailto:j.hadfield at ed.ac.uk]
Sent: 04 March 2014 12:07
To: Hodsoll, John
Cc: 'r-sig-mixed-models at r-project.org'
Subject: Re: [R-sig-ME] MCMCglmm variance estimates Poisson distribution
Dear John,
How are you calculating the posterior expectation:
1/
posterior.mode(exp(mcmc.c11.cf2$Sol+mcmc.c11.cf2$VCV/2))
2/
mean(exp(mcmc.c11.cf2$Sol+mcmc.c11.cf2$VCV/2))
3/
exp(posterior.mode(mcmc.c11.cf2$Sol)+posterior.mode(mcmc.c11.cf2$VCV/2))
4/
exp(mean(mcmc.c11.cf2$Sol)+mean(mcmc.c11.cf2$VCV/2))
If it is not by method 1/ try that and see if there is less of a discrepancy.
Cheers,
Jarrod
Quoting "Hodsoll, John" <john.hodsoll at kcl.ac.uk> on Tue, 4 Mar 2014
11:25:17 +0000:
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