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

Jarrod Hadfield j.hadfield at ed.ac.uk
Mon Jun 23 21:22:13 CEST 2014

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.



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
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