[R-sig-ME] glmmadmb Negative binomial dispersion parameter

[Highland Statistics Ltd]

> ----------------------------------------------------------------------
>
> Message: 1
> Date: Tue, 23 Feb 2016 15:02:48 +0100
> From: Marte Lilleeng <mlilleeng at gmail.com>
> To: r-sig-mixed-models at r-project.org
> Subject: [R-sig-ME] glmmadmb Negative binomial dispersion parameter
> Message-ID:
> 	<CAM-hW5cun5sXzyf-WrCLF2CGJjfQeD1nTnnccAZ213kbSqDSvA at mail.gmail.com>
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>
> Hello everyone.
> I wonder if you can help me with the following questions?
>
> 1) How is the Negative binomial dispersion parameter calculated in glmmadmb?
>
> 2) Is there,as for mixed effects poisson models (glmer), a *rule of thumb*
> for when the dispersion parameter is representing trouble (overdispersion)?
> I learned from Zuur and Ileno that for a mixed effects poisson mod the
> dispersion is ok as long as it is not above 1.3-1.4(calculated this way;
>
> E <- residuals (modelname)
>
> pb <- length(fixef(modelname)+1) # +1 due to random intercept variance
>
> overdisp <- sum(E^2)/(nrow(dataset)-pb)
>
> 3) How do you recommend to do the model validation for glmmadmb with
> negative binomial error structure?
>
> Best regards from
> "New to mixed models with NB", Marte Lilleeng
>
> 	[[alternative HTML version deleted]]
>
>
I forgot to mention that the more and more I look at the NB GLM(M) the 
less I like it. You should only go for a NB GLM(M) is the cause of the 
overdispersion is large variation. If there is something else that is 
causing overdispersion (e.g. non-linear patterns, zero inflation, 
missing covariate, wrong link function), then the parameter theta is 
going to consume that information. And I still need to see the first 
data set for which the NB GLM(M) gives predictions with decent 
confidence intervals.

Maybe you want to have a look at the generalized Poisson GLM(M)...it 
tends to perform better than the NB GLMM. But the problem is that you 
may have to go Bayesian in that case.

Alain








-- 
Dr. Alain F. Zuur

First author of:
1. Beginner's Guide to GAMM with R (2014).
2. Beginner's Guide to GLM and GLMM with R (2013).
3. Beginner's Guide to GAM with R (2012).
4. Zero Inflated Models and GLMM with R (2012).
5. A Beginner's Guide to R (2009).
6. Mixed effects models and extensions in ecology with R (2009).
7. Analysing Ecological Data (2007).

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