# How to deal with zero-inflated proportional data in GLMM?

I have proportional data, i.e. number of individuals out of 6 that choose a certain option in a multiple choice experiment, so there are just 7 possible outcomes for each option: 0/6; 1/6; 2/6; 3/6; 4/6; 5/6; 6/6.

I had several replicates and did several trials on each replicate. In order to find out which option was preferred (i.e. which option was chosen more frequently), I applied a GLMM with binomial distribution. Turned out that I had overdispersion (dispersion factor = 2) because there were too many zeros.

Now my question is how to go on with my analysis? Do I have to choose another type of distribution (in this case, which one? Beta, negative binomial, etc.?). And could you recommend any function in R to do so? I’m pretty new to GLM analysis.

• With the glmmTMB R-package your response can follow a zero-inflated binomial distribution with separate linear predictors for the zero-inflation probability and the probability $p$ of the non-zeroinflated component of the distribution. Both linear predictors can include random effects. – Jarle Tufto Sep 19 '19 at 14:23
• Thanks for your comment @JarleTufto! I already had a look at the glmmTMB package and it seems to offer quite a lot of possibilities. Remains the question how to specify the function for the zero-inflated part and how to interpret it, do you have any recommendations? – Ricarda Sep 19 '19 at 19:34