Cumulative, ordered logistic, models with Dirichlet cutpoints

I’m trying to model some ordinal response data (Likert data) with repeated measures. I’d like to use a random effects model with brms to capture variation among questions and respondents, etc. However, I find I cannot completely reduce my divergent iterations to 0.

I want to follow the advice of Michael Betancourt in this tutorial. Specifically, he recommends using a Dirichlet prior for the cutpoints. As I understand it, this is handy because it can be very hard to estimate the cutpoints when there are very few observations in one category (e.g. almost nobody scores something as 1 out of 7).

It seems like brms uses normal priors for each cutpoint, and allows random effects. Meanwhile rstanarm::stan_polr uses a Dirichlet prior for cutpoints, but no random effects. Is it possible to have both? Or does this require adapting Michael’s code and writing Stan directly?


For users reading this thread, we have openend an issue about this feature on github: