

   Ok, thank you very much. Only one last question, how can I compare the random 
effects of two different models? The term has a zero variance, so I guess I can 
remove it, but I want to make sure.


   Thanks again!
 
   Iker




________________________________
De: "ONKELINX, Thierry" <Thierry.ONKELINX@inbo.be>

Enviado: mié,22 junio, 2011 21:09
Asunto: RE: [R-sig-ME] Is it worth testing for overdispersion?


Yes, that's what I mean.

Douglas Bates recommends it instead of the quasi-models. MCMCglmm() uses that 
concept in every glmm standard.

If there is overdispersion, just leave the observation level random effect in 
the model. Otherwise you can remove it.


________________________________


Verzonden: woensdag 22 juni 2011 18:40
Aan: ONKELINX, Thierry
Onderwerp: Re: [R-sig-ME] Is it worth testing for overdispersion?



   You mean adding a factor with as many levels as number of observations and 
testing it as a random effect? Are you sure that's a good overdispersion test? 
And what can I do if it's actually overdispersed?

   Thanks. Iker




________________________________
 De: "ONKELINX, Thierry" <Thierry.ONKELINX@inbo.be>

"r-sig-mixed-models@r-project.org" <r-sig-mixed-models@r-project.org>
Enviado: mar,21 junio, 2011 14:13
Asunto: RE: [R-sig-ME] Is it worth testing for overdispersion?

Dear Iker,

You can add an observation  level random effect to the model. If there is 
overdisperion in your dataset, then this random effect will have a non-zero
variance. You can compare models with and without this observation level random 
effect to test for overdisperion.

Best regards,

Thierry

> -----Oorspronkelijk bericht-----
> Van: r-sig-mixed-models-bounces@r-project.org [mailto:r-sig-mixed-models-
> bounces@r-project.org] Namens Iker Vaquero Alba
> Verzonden: dinsdag 21 juni 2011 13:53
> Aan: r-sig-mixed-models@r-project.org
> Onderwerp: [R-sig-ME] Is it worth testing for overdispersion?
> 
> 
> 
>    Dear list:
> 
>    I read some time ago in one of the posts that the option of implementing
> quasi- families in lmer had been removed. Is that right?
>    I´ve been a lot of time trying to find a way to test for overdispersion in 
>lmer
> without success. And now that I read that I won't be able to use a 
>quasi-poisson
> distribution for my data in case the model is overdispersed assuming poisson
> errors, I wonder wether it's really worth testing for overdispersion, and what
> alternatives do I have in case my model is overdispersed.
> 
[[elided Yahoo spam]]
> 
>     Iker Vaquero
> 
>     [[alternative HTML version deleted]]
	[[alternative HTML version deleted]]


