Dear list members,

I fit a random intercept morel with lme4 first, then I tried to fit the same
model with MCMCglmm. I didn't specify the prior and ran 300000 iterations.
The model still didn't converge as the autocorrelation is very high (>0.4).
May I ask (1) do I have to specify my own prior? and (2) what is the
usually/average number of iterations in Bayesian multilevel model? 300000
seems quite large a number. Do I have to rerun the model if the previous one
doesn't converge?

Best with thanks.

>data(Contraception,package="mlmRev")
>library(lme4)
>fm1 <-
glmer(use~urban+age+livch+(1|district),data=Contraception,family=binomial,nAGQ=7)

>MC1 <-
MCMCglmm(use~urban+age+livch,random=~district,data=Contraception,family="categorical",nitt=300000,burnin=240000,thin=100)
>summary(MC1)

 Iterations = 240001:299901
 Thinning interval  = 100
 Sample size  = 600

 DIC: 47.28245

 G-structure:  ~district

         post.mean l-95% CI u-95% CI eff.samp
district      3242      953     6490      132

 R-structure:  ~units

      post.mean l-95% CI u-95% CI eff.samp
units     38025    13640    57500    85.53

 Location effects: use ~ urban + age + livch

            post.mean l-95% CI u-95% CI eff.samp  pMCMC
(Intercept)  -197.901 -262.707 -130.966    92.68 <0.002 **
urbanY         86.448   53.374  119.831   131.85 <0.002 **
age            -3.139   -5.439   -1.200   166.36 <0.002 **
livch1        128.027   76.737  177.583   126.78 <0.002 **
livch2        159.752  102.289  222.468   109.28 <0.002 **
livch3+       155.254   92.145  216.916   123.80 <0.002 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

>autocorr(MC1$Sol)
<snip>

-- 
Wincent Ronggui HUANG
Sociology Department of Fudan University
PhD of City University of Hong Kong
http://asrr.r-forge.r-project.org/rghuang.html

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