
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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