Which GLM(M) to use for proportional data? I have proportional data that takes any value from 0 and 1. However, there are certainly an over-abundance of 0's, and the rest of the values (there are few) tend to be close to zero.
I'm wondering with this knowledge what is the appropriate GLM to use?
Thanks in advance.
 A: If you know proportions and their denominators (in your case, "number of a certain type of hatches per number of pupae"), then a binomial response is (in my opinion) the most principled/sensible thing to do.

*

*Lots of zeros are expected when the mean proportion is low; it's still possible that you need a zero-inflated binomial model, but unlikely (Warton 2005).

*When the mean of a binomial is low, a Poisson model with an offset gives nearly identical results (see here; more precisely, the probability should be low everywhere (e.g. if you have a few combinations of covariates that lead to higher probabilities, that could mess things up).

*As always you should check for overdispersion after fitting the model and if necessary do something appropriate (quasi-likelihood, observation-level random effects, beta-binomial model ...)


Warton, David I. “Many Zeros Do Not Mean Zero Inﬂation: Comparing the Goodness-of-FIt of Parametric Models to Multivariate Abundance Data.” Environmetrics 16 (2005): 275–89. https://doi.org/10.1002/env.702.
