# Cumulative, ordered logistic, models with Dirichlet cutpoints
**URL:** https://discourse.mc-stan.org/t/cumulative-ordered-logistic-models-with-dirichlet-cutpoints/11095
**Category:** brms
**Created:** [September 23, 2019, 4:10pm UTC](https://discourse.mc-stan.org/t/cumulative-ordered-logistic-models-with-dirichlet-cutpoints/11095 "2019-09-23T16:10:32Z")
**Posts on this page:** 2
**Page:** 1
### Author:  [@aammd](https://discourse.mc-stan.org/u/aammd)
#### Post date: [September 23, 2019, 4:10pm UTC](https://discourse.mc-stan.org/t/cumulative-ordered-logistic-models-with-dirichlet-cutpoints/11095/1 "2019-09-23T16:10:32Z")
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](https://betanalpha.github.io/assets/case_studies/ordinal_regression.html#22_surgical_cut). 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?
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### Author:  [@paul.buerkner](https://discourse.mc-stan.org/u/paul.buerkner)
#### Post date: [September 24, 2019, 8:44am UTC](https://discourse.mc-stan.org/t/cumulative-ordered-logistic-models-with-dirichlet-cutpoints/11095/2 "2019-09-24T08:44:51Z")
For users reading this thread, we have openend an issue about this feature on github: [https://github.com/paul-buerkner/brms/issues/762](https://github.com/paul-buerkner/brms/issues/762)