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

[Céline Teplitsky]

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