[R-sig-ME] No residual variance using MCMCglmm

Fri Jul 11 18:13:11 CEST 2014

Hi Celine,

Is this problem happening when individual is NOT fitted as a random  
term? If so, can you post the counts and I'll take a look? I'm not  
sure whether you can have attachments on the list. If not you can  
paste in the output from

paste("counts<-c(", paste(counts, collapse=","), ")", sep="")

into the body of the text, where counts are the data (surely there is  
a better way of doing this?).

Cheers,

Jarrod

Quoting Celine Teplitsky <teplitsky at mnhn.fr> on Fri, 11 Jul 2014  
18:00:32 +0200:

> Hi Jarrod,
>
> I actually have 254 observations (152 individuals), and I left the  
> default prior
>
> And indeed, the chain doesn't look very nice. But I can't get what  
> is the prpoblem....
>
> Cheers
>
> Celine
>
>> Hi Celine,
>>
>> There is more variance than you expect (0.68/0.52 = 1.31X), but  
>> this might be consistent with chance if sample size is small. For  
>> example if n=30 you expect var(x)/mean(x) > 1.31 in about 10% of  
>> cases if lambda=0.52. For n=30 I would expect values of zero for  
>> the units variance to have some support in the posterior  
>> (conditional on the prior of course). For sample sizes of around  
>> 100 I would expect the posterior to be well away from zero. How  
>> many data do you have?
>>
>> From a model perspective having a units variance of zero is not a  
>> problem per se. From the perspective of MCMCglmm it will mean the  
>> chain will not mix (if it is always exactly zero) or mix slowly (if  
>> it is near zero).
>>
>> Cheers,
>>
>> Jarrod
>>
>>
>>
>>
>>
>>
>>
>>
>> Quoting Céline Teplitsky <teplitsky at mnhn.fr> on Fri, 11 Jul 2014  
>> 16:21:49 +0200:
>>
>>> Hi Jarrod,
>>>
>>> many thanks for your answer. I've been trying to understand better  
>>> the idea behind the models before answering, but I'd like to be  
>>> sure I got this right.
>>>
>>> In the data set I have
>>> var(y)=0.68
>>> mean(y)=0.52
>>> and if I run a model with only intercept and residual, I get an  
>>> intercept of -0.81, so that the expected variance would be 0.44,  
>>> suggesting the data could be a bit overdispersed. But the residual  
>>> in this model is collapsing on 0.
>>>
>>> In your latest version of the course notes, you mention p37" if  
>>> the residual was zero, then e would be a vector of zero and the  
>>> model would conform to the standard Poisson glm." So do I get this  
>>> right that no residual in a Poisson model is ok, just an indicator  
>>> of no overdispersion, but is not per se a problem?
>>>
>>> Many thanks again for your help
>>>
>>> Cheers
>>>
>>> Celine
>>>
>>> Le 23/06/2014 21:22, Jarrod Hadfield a écrit :
>>>> Hi Céline,
>>>>
>>>> Zero residual variance with (truncated) Poisson response would  
>>>> imply that the data are under-dispersed with respect to the  
>>>> (truncated) Poisson model. You could check this by comparing the  
>>>> variance of the data with the expected variance given the  
>>>> intercept.
>>>>
>>>>
>>>> Cheers,
>>>>
>>>> Jarrod
>>>>
>>>>
>>>>
>>>> Quoting Céline Teplitsky <teplitsky at mnhn.fr> on Fri, 20 Jun 2014  
>>>> 14:39:33 +0200:
>>>>
>>>>> Dear all,
>>>>>
>>>>> I have recently bumped twice in the same issue running glmm in  
>>>>> MCMCglmm: the posterior distribution of residual collapses on 0.  
>>>>> While I have often seen it for other effects (e.g ID) and  
>>>>> interpreted it as evidence of non existence / non significance  
>>>>> of these effects, I can not get why residual variance would not  
>>>>> be well defined.
>>>>>
>>>>> More specifically, with priors V=1, nu=0.02, I was trying to  
>>>>> estimate additive genetic variance in age at first breeding. I  
>>>>> first tried a Poisson distribution and the posterior  
>>>>> distribution of the residual looked more or less ok, although  
>>>>> not perfectly bell shaped. Then I thought as age at first  
>>>>> breeding could not be zero, that a zero truncated Poisson might  
>>>>> be better but then the posterior distribution of residual  
>>>>> variance totally collapses on zero. As I thought it could be due  
>>>>> to over parametrisation, I rerun the model with only intercept  
>>>>> but results were the same.
>>>>>
>>>>> Is it a problem with the variables distributions not really  
>>>>> fitting the distribution I'm specifying? Any help would be  
>>>>> greatly appreciated!
>>>>>
>>>>> Many thanks in advance
>>>>>
>>>>> Celine
>>>>>
>>>>> -- 
>>>>>
>>>>> Celine Teplitsky
>>>>> UMR 7204 - CESCO
>>>>> Département Ecologie et Gestion de la Biodiversité
>>>>> CP 51
>>>>> 55 rue Buffon 75005 Paris
>>>>>
>>>>> Webpage : http://www2.mnhn.fr/cersp/spip.php?rubrique96
>>>>> Fax : (33-1)-4079-3835
>>>>> Phone: (33-1)-4079-3443
>>>>>
>>>>> _______________________________________________
>>>>> R-sig-mixed-models at r-project.org mailing list
>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>>
>>>>>
>>>>
>>>>
>>>
>>> -- 
>>>
>>> Celine Teplitsky
>>> UMR 7204 - CESCO
>>> Département Ecologie et Gestion de la Biodiversité
>>> CP 51
>>> 55 rue Buffon 75005 Paris
>>>
>>> Webpage : http://www2.mnhn.fr/cersp/spip.php?rubrique96
>>> Fax : (33-1)-4079-3835
>>> Phone: (33-1)-4079-3443
>>>
>>>
>>>
>>
>>
>>
>> -- 
>> The University of Edinburgh is a charitable body, registered in
>> Scotland, with registration number SC005336.
>>
>>
>>
>
>
>
> -- 
>
> Celine Teplitsky
> UMR 5173 MNHN-CNRS-P6 'Conservation des espèces, restauration et suivi des
> populations'
> Muséum National d'Histoire Naturelle
> CRBPO, 55, Rue Buffon, CP51, 75005 Paris, France
>
> Fax : (33-1)-4079-3835
> Phone: (33-1)-4079-3443
>
>
>
>

-- 
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.