MCMCglmm variance estimates Poisson distribution
Hi Jarrod
> is there a reason that the data frames differ in each (uc11 and uc12)?
Yes. Two different baseline conditions, cut and paste error.
With the same data frame
summary(mcmc.c11.cf2)
Iterations = 10001:99911
Thinning interval = 90
Sample size = 1000
DIC: 7489.396
R-structure: ~units
post.mean l-95% CI u-95% CI eff.samp
units 0.7111 0.6276 0.7912 1000
Location effects: totflct ~ 1
post.mean l-95% CI u-95% CI eff.samp pMCMC
(Intercept) 1.211 1.160 1.263 1000 <0.001 ***
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
posterior.mode(exp(mcmc.c11.cf$Sol+mcmc.c11.cf$VCV/2))
(Intercept)
4.7921
exp(mean(mcmc.c11.cf2$Sol)+mean(mcmc.c11.cf2$VCV/2))
[1] 4.793908
And for glmer...
Generalized linear mixed model fit by the Laplace approximation
Formula: totflct ~ (1 | obs)
Data: uc11
AIC BIC logLik deviance
6205 6216 -3100 6201
Random effects:
Groups Name Variance Std.Dev.
obs (Intercept) 0.083672 0.28926
Number of obs: 1607, groups: obs, 168
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.50622 0.02535 59.43 <2e-16 ***
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
__
for which exp(1.50622 + 0.083672/2) = 4.70232
I take your point re prior. Number of observations is 1607 so I thought this should be sufficient to limit the influence of the prior?
Cheers
John
From: Jarrod Hadfield <j.hadfield at ed.ac.uk>
Sent: 04 March 2014 17:13
To: Hodsoll, John
Cc: 'r-sig-mixed-models at r-project.org'
Subject: RE: [R-sig-ME] MCMCglmm variance estimates Poisson distribution
Sent: 04 March 2014 17:13
To: Hodsoll, John
Cc: 'r-sig-mixed-models at r-project.org'
Subject: RE: [R-sig-ME] MCMCglmm variance estimates Poisson distribution
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 >> >> > > > > -- > The University of Edinburgh is a charitable body, registered in > Scotland, with registration number SC005336. > > > > -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.