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

[m.fenati at libero.it]

Dear Jarrod,
thank you for your fast answer.
Yes, I had converegence (presence of trend of the time series). Unfortunately, 
I have ordinal data with near complete separation.
My aim is to set a poorly informative or uninformative priors for fixed effect 
in order to improve the chain convergence. Then I set piorB=list(mu=c(rep(0,6)),
V=diag(6)*(100)). The choice of V=100 is not based on other logical or 
numerical reasons. 
I try to display the posterior distribution of latent variable (pl=T), but I 
had a wide range of -25 + 25.....
How can I do? Could you help me to choose the right prior?

Thank in advance

Massimo
 


>----Messaggio originale----
>Da: j.hadfield a ed.ac.uk
>Data: 08/07/2012 12.20
>A: "m.fenati a libero.it"<m.fenati a libero.it>
>Cc: <r-sig-mixed-models a r-project.org>
>Ogg: Re: [R-sig-ME] MCMCglmm: priors for ordinal regression
>
>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 a libero.it" <m.fenati a 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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