[R-sig-ME] MCMCglmm variance estimates Poisson distribution

[Jarrod Hadfield]

Hi John,

Perhaps the output from:

summary(mcmc.c11.cf2)

and

summary(re.uc12.cf)

will shed some light?  Also is there a reason that the data frames  
differ in each (uc11 and uc12)?

Failing something `obvious' then it must be the prior. How many  
observations is this based on?

Cheers,

Jarrod


Quoting "Hodsoll, John" <john.hodsoll at kcl.ac.uk> on Tue, 4 Mar 2014  
16:40:11 +0000:

> 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
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>
>
>
>
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> Scotland, with registration number SC005336.
>
>
>
>



-- 
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.