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

[Jarrod Hadfield]

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
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>


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