Thanks for the clarification.

Cheers

On 4 April 2011 00:02, Jarrod Hadfield <j.hadfield@ed.ac.uk> wrote:

> Hi Ronggui,
>
> Fixing the variance at 1 ADDS 1 to the level-1 error rather than replacing
> pi^2/3.
>
> Cheers,
>
> Jarrod
>
>
>
> Quoting Wincent <ronggui.huang@gmail.com> on Sun, 3 Apr 2011 23:59:26
> +0800:
>
>  Hi Professor Hadfield, I see.
>>
>> In the multilevel logistic model, the level-1 error variance is fixed
>> pi^2/3, which I perhaps should follow.
>>
>> Thank you very much
>>
>> BTW, the MCMCglmm is a great package.
>>
>> Regards,
>> Ronggui
>>
>> On 3 April 2011 23:53, Jarrod Hadfield <j.hadfield@ed.ac.uk> wrote:
>>
>>  Hi Wincent,
>>>
>>> The model you ran is meaningless, because with categorical data or
>>> ordinal
>>> data the residual variance cannot be estimated from the data. The best
>>> option is to fix the residual variance at some value (I use 1):
>>>
>>>  MC1<-MCMCglmm(use~urban+age+livch,random=~district,data=Contraception,family="categorical",prior=list(R=list(V=1,
>>> fix=1), G=list(G1=list(V=1, nu=0))), slice=T)
>>>
>>> I have left the prior on the district variance flat and improper, and I
>>> have used slice sampling (slice=T) instead of Metropolis-Hastings updates
>>> because the chain mixes faster:
>>>
>>> summary(MC1)
>>>
>>>  Iterations = 3001:12991
>>>  Thinning interval  = 10
>>>  Sample size  = 1000
>>>
>>>  DIC: 2391.208
>>>
>>>
>>>  G-structure:  ~district
>>>
>>>        post.mean l-95% CI u-95% CI eff.samp
>>> district    0.3333   0.1407   0.5777    459.9
>>>
>>>
>>>  R-structure:  ~units
>>>
>>>     post.mean l-95% CI u-95% CI eff.samp
>>> units         1        1        1        0
>>>
>>>
>>>  Location effects: use ~ urban + age + livch
>>>
>>>           post.mean l-95% CI u-95% CI eff.samp  pMCMC
>>> (Intercept)  -2.02718 -2.38427 -1.69340    559.7 <0.001 ***
>>> urbanY        0.87874  0.61785  1.16036    640.9 <0.001 ***
>>> age          -0.03233 -0.05013 -0.01523    531.4 <0.001 ***
>>> livch1        1.32248  0.96341  1.66027    578.6 <0.001 ***
>>> livch2        1.63986  1.24274  2.05330    542.6 <0.001 ***
>>> livch3+       1.61311  1.18382  2.00119    512.2 <0.001 ***
>>>
>>> Note that you can rescale the estimates to approximate what they would be
>>> if the residual variance was zero (i.e. the glmer default):
>>>
>>> c2<-((16*sqrt(3))/(15*pi))^2
>>>
>>> posterior.mode(MC1$Sol/sqrt(1+c2))
>>> (Intercept)      urbanY         age      livch1      livch2     livch3+
>>> -1.69345255  0.72259159 -0.03015856  1.14554895  1.38585993  1.34449154
>>>
>>> which are closer to the ML estimates.
>>>
>>> Cheers,
>>>
>>> Jarrod
>>>
>>>
>>>
>>>  Quoting Wincent <ronggui.huang@gmail.com> on Sun, 3 Apr 2011 20:32:27
>>> +0800:
>>>
>>>  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
>>>>
>>>>       [[alternative HTML version deleted]]
>>>>
>>>>
>>>>
>>>>
>>>
>>> --
>>> The University of Edinburgh is a charitable body, registered in
>>> Scotland, with registration number SC005336.
>>>
>>>
>>>
>>>
>>
>> --
>> Wincent Ronggui HUANG
>> Sociology Department of Fudan University
>> PhD of City University of Hong Kong
>> http://asrr.r-forge.r-project.org/rghuang.html
>>
>>
>
>
> --
> The University of Edinburgh is a charitable body, registered in
> Scotland, with registration number SC005336.
>
>
>


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

	[[alternative HTML version deleted]]


