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 at ed.ac.uk
Data: 08/07/2012 12.20
A: "m.fenati at libero.it"<m.fenati at libero.it>
Cc: <r-sig-mixed-models at 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 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
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