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

[m.fenati at libero.it]

Thank you again for your great suggestions.

Regards

Massimo



>----Messaggio originale----
>Da: j.hadfield a ed.ac.uk
>Data: 12/07/2012 15.20
>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
>
>Hi,
>
>If the prior variance on your fixed effects is V+1 where V is the sum  
>of the variance components (including the residual) then the marginal  
>prior on the fixed effects is as flat as possible on the probability  
>interval (0,1). However, you have to set up the contrasts correctly.
>
>If you still get numerical problems I'm afraid you will have to find  
>another way of doing the analysis. I have no solution, and no one has  
>suggested any:
>
>https://stat.ethz.ch/pipermail/r-sig-mixed-models/2012q1/017976.html
>
>Cheers,
>
>Jarrod
>
>
>
>Quoting "m.fenati a libero.it" <m.fenati a libero.it> on Mon, 9 Jul 2012  
>12:44:59 +0200 (CEST):
>
>> 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]]
>>>>
>>>> _______________________________________________
>>>> R-sig-mixed-models a 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.
>
>
>