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Zero-inflated binomial (ZIB) models in glmmADMB:, warnings and errors

1 message · Highland Statistics Ltd

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I would certainly include the plot effect, and cluster effect within plot. .....it imposes a correlation structure on your data.
Not sure whether I understand the color thing though.

If your binomial GLMM is overdispersed then you should try to figure out why this is. Common causes are:

1. Outliers
2. Non-linear patterns
3. Wrong link function
4. Wrong distribution
5. Too many zeros
6. Dependency structure that has not been included...or included in the wrong way.

For 2...plot your residuals vs each continuous covariate. If 5 is the cause, then yes...a zero inflated binomial is an option.
For 4 you may want to consider the beta-binomial distribution, see for example the book from Ben Bolker, or our 'Beginner's Guide to GLM & GLMM'; it
contains code to fit a beta-binomial GLMM in JAGS. A zero inflated version of a beta-binomial GLMM/GAMM requires similar code.


As to your question how many zeros means zero inflated models.....it all depends. I have data sets with 70% of zeros...and a
Poisson/NB GLM still do the job...and I have data sets where 25% of zeros already means ZIP. Same holds for ZIB.


I would strongly suggest to do this in JAGS.

Have fun

Alain