My question concerns a specific functionality of brms in the context of defining linear mixed models. The model I am trying to define aims at predicting a bimodal metric variable. The intercept of this model can be assumed to closely follow a gamma distribution, which I therefore used as a prior with the following syntax:

prior = set_prior(“gamma(25, 0.02)”, class = “Intercept”)

This works well. However, I also expect that about 5% of the observations of the dependent variable should be 0, which is not modeled by this gamma distribution. I therefore wanted to include this information in the prior. My question is whether this is possible with the current version of brms? My (naive?) approach so far was to try to define a gamma hurdle distribution instead of a gamma distribution, but I am not sure how to define such a prior; something like

prior = set_prior(“gamma_hurdle(25, 0.02, 0.05)”, class = “Intercept”)

obviously does not work.

- Operating System: Windows 10
- brms Version: 2.9.0