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

[Celine Teplitsky]

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