[R-sig-ME] MCMCglmm: priors for ordinal regression

[Jarrod Hadfield]

Dear Massimo,

Do you mean the chain did not converge or the chain did not mix?  
Generally the former is rare, and is usually only seen with  
ordinal/categorical data with complete (or near complete) separation.   
Sometimes a prior that constrains the linear predictor away from  
extreme values on the logit/probit scale can fix this with a  
relatively minor prior influence on inferences made on the data scale.  
Sometimes not. Its not clear to me what the motivation is behind your  
prior - is it that the sum of your variance components is close to  
100? If so I would be careful. Use pl=TRUE in your call to MCMCglmm  
and make sure your latent variables are in the range -7 to 7.

Cheers,

Jarrod





Quoting "m.fenati at libero.it" <m.fenati at libero.it> on Wed, 4 Jul 2012  
16:48:18 +0200 (CEST):

>
> Dear R user,
> I have some problems about prior definition in MCMCglmm ordinal  
> regression. I've tried to use what Jarrod wrote about not  
> informative priors for ordinal probit but my model did not converge:
>
>
> prior=list(R=list(V= 1, fix=1), G=list(G1=list(V=1, nu=0)))
>
>
> where "..left the default prior for the fixed effects (not  
> explicitly specified)..".
>
>
> Then, in order to have however a similar uniform distribution for  
> the latent variable, I set prior for fixed effect  as "mu=0" and  
> "(co)variance=100":
>
>
> priorB<-rnorm(1000, 0, sqrt(100))
> priorMB<-1:1000
> for(i in 1:1000){
>   priorMB[i]<-mean(pnorm(priorB[i]+rnorm(1000,0,sqrt(100))))
>    }
> hist(priorMB)
>
>
> The model converge well but I've some dobts. Is it correct or not?
>
>
> Thank you very much for any suggestions or comments.
>
>
> Best regards
>
>
> Massimo
> 	[[alternative HTML version deleted]]
>
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>



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