[R-sig-ME] Dealing with overdispersion with glmmadmb beta distribution

Ben Bolker bbolker at gmail.com
Mon Jul 9 15:24:56 CEST 2012

Heather Kharouba <kharouba at ...> writes:

> In the analysis I'm doing, I'm interested in testing the importance of a
> factor (with 5 levels). The response variable varies continuously from 0
> to 1 so I've used a beta distribution with glmmadmb:
> m1<-glmmadmb(AUC~variables+log_area+model+taxa+(1|study), family="Beta",
> verbose=TRUE, debug=TRUE, data=mat);
> Call:
> glmmadmb(formula = AUC2 ~ variables + log_area + model2 + taxa +
>     (1 | study), data = mat, family = "Beta", verbose = TRUE,
>     debug = TRUE)
> Beta dispersion parameter: 14.503 (std. err.: 0.35671)
> The dispersion parameter is 14.503 suggesting the model is overdispersed.
> A recent suggestion from http://glmm.wikidot.com/faq and
> r-sig-mixed-models archives to account for overdispersion is to include
> individual-level random effects. However, if I include this additional
> random effect, I get the following error:

   You don't need to include an additional parameter etc. to allow
for overdispersion in a Beta model, because the Beta model already
incudes a dispersion parameter.  (Of the standard models, only
the Poisson and the binomial need the possibility of an overdispersion
factor: Gaussian, Gamma, negative binomial, ... all include an overdispersion
parameter, and Bernoulli (binomial with size=1) can't identify

  Ben Bolker

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