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

[Jarrod Hadfield]

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.