[R-sig-ME] MCMCglmm for binomial models?

[ONKELINX, Thierry]

Dear Bob,

Notice that MCMCglmm always estimates 'units' to take overdispersion
into account. So the glmer model and the MCMCglmm model are not the
same.

Furthermore, you did not specify the priors. I'm not sure if the default
work well for all types of models.

HTH,

Thierry


------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
team Biometrie & Kwaliteitszorg
Gaverstraat 4
9500 Geraardsbergen
Belgium

Research Institute for Nature and Forest
team Biometrics & Quality Assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium

tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be
www.inbo.be

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey
  

> -----Oorspronkelijk bericht-----
> Van: r-sig-mixed-models-bounces at r-project.org 
> [mailto:r-sig-mixed-models-bounces at r-project.org] Namens Bob Farmer
> Verzonden: maandag 20 september 2010 14:18
> Aan: David Duffy; r-sig-mixed-models at r-project.org
> Onderwerp: Re: [R-sig-ME] MCMCglmm for binomial models?
> 
> Thanks for the reply.  Unfortunately, I'm not sure that 
> that's the whole story.  Upping the iterations to achieve an 
> effective sample size of 1000 (output pasted below) still 
> leads to some differences between the two models (total 
> runtime on a quad-core Windows box is under 2 minutes, in 
> case you want to try).  In fact, running it a second time 
> shooting for an n.eff of 2000 (thin = 325) leads to even more 
> divergent estimates (e.g. mean.int == -72.96).  Again, 
> assuming that the glmer() output in this case is a `gold 
> standard', maybe there's another puzzle piece missing?
> 
> Thanks again.
> --Bob
> 
> 
> #################now binomial from a reference dataset
> gm3 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
>               family = binomial, data = cbpp) 
> mc3<-MCMCglmm(cbind(incidence, size - incidence) ~ period,
>   random = ~ herd,
>   family = "multinomial2",
>   data = cbpp, verbose = FALSE,
>   nitt = 750E3, thin = 650, burnin = 100E3
> )
> summary(gm3)@coefs
> summary(mc3)
> 
> Leads to:
> 
> > summary(gm3)@coefs
>               Estimate Std. Error   z value     Pr(>|z|)
> (Intercept) -1.3985351  0.2278906 -6.136871 8.416284e-10
> period2     -0.9923347  0.3053852 -3.249452 1.156274e-03
> period3     -1.1286754  0.3260491 -3.461673 5.368286e-04
> period4     -1.5803739  0.4288037 -3.685542 2.282169e-04
> 
> Versus:
> 
> > summary(mc3)
> 
>  Iterations = 749351
>  Thinning interval  = 100001
>  Sample size  = 1000
> ....
>             post.mean l-95% CI u-95% CI eff.samp pMCMC
> (Intercept)   -1.6102  -2.2092  -0.8357     1000 0.002 **
> period2       -1.2476  -2.2146  -0.1284     1000 0.020 *
> period3       -1.3743  -2.3387  -0.2746     1000 0.008 **
> period4       -1.9677  -3.2814  -0.8065     1000 0.008 **
> ---
> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> 
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> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> 

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