Binomial GLMM Assumptions

[jsocolar]

You might find this thread useful:

Differing posterior predictive checks for logistic binomial model with and without addition-terms

Hi all, Not so much an issue as double-checking expected behaviour: I’m fitting a logistic binomial model where the response variable is the sum of how many times a target picture was looked in a certain time period (out of how many looks there were at all pictures during that period). This kind of response variable falls under “addition-terms” according to the brms documentation. The model estimates are plausible and the fit is good, but the posterior predictive check isn’t as good as if I fi…

To be nitpicky about vocabulary for a moment, note that you do not expect homoscedasticity of residuals in a binomial model. For a binomial response, the analog to checking residuals in a Guassian model for normality and homoscedasticity is checking the binomial response for over- (or under-) dispersion.