[R-sig-ME] glmmadmb Negative binomial dispersion parameter
Highland Statistics Ltd
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Tue Feb 23 19:51:10 CET 2016
> 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
> <CAM-hW5cun5sXzyf-WrCLF2CGJjfQeD1nTnnccAZ213kbSqDSvA at mail.gmail.com>
> Content-Type: text/plain; charset="UTF-8"
> 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
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.
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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