# Choosing priors for a brms hurdle model
**URL:** https://discourse.mc-stan.org/t/choosing-priors-for-a-brms-hurdle-model/16445
**Category:** brms
**Tags:** priors, prior-choice, specification
**Created:** [July 8, 2020, 11:04am UTC](https://discourse.mc-stan.org/t/choosing-priors-for-a-brms-hurdle-model/16445 "2020-07-08T11:04:07Z")
**Posts on this page:** 3
**Page:** 1
### Author:  [@jobi](https://discourse.mc-stan.org/u/jobi)
#### Post date: [July 8, 2020, 11:04am UTC](https://discourse.mc-stan.org/t/choosing-priors-for-a-brms-hurdle-model/16445/1 "2020-07-08T11:04:07Z")
I am fitting a hurdle gamma model to some data. I understand that the default prior for the hurdle is suspicious of values close to 0 and 1 and expects values around 0.5. However, I believe that around 0.5-10% of values are positive and I am open to this being arbitrarily close to 0.
Whilst I am comfortable with ‘greek’ equation-based formulations of Bayesian models, I am not yet used to converting this to brms/Stan code. That means I’m struggling to interpret both the output of ‘prior\_summary’ and ‘stancode’.
I suppose there are two questions:
1. My current guess is to change the logistic regression distribution for “b\_hu\_Intercept” by putting in `prior = prior(logistic(3,1), class = "Intercept", dpar = "hu")`. Does this seem appropriate? Can I improve on this prior?
2. More generally, are there any tips for people used to equation formulations of Bayesian models to better understand brms and/or Stan models? I have seen that the package greta offers a diagrammatic representation of Bayesian models ([reference](https://cran.r-project.org/web/packages/greta/vignettes/get_started.html#plotting)) that I find easier to interpret than ‘prior\_summary’. I am expecting the answer here to be ‘learn Stan and read the code’!
I have attached an example [simulated\_data.csv](https://discourse.mc-stan.org/uploads/short-url/57hyd7wkqgABpGFRWyE1wgjiyy9.csv) (743.7 KB) dataset and example code to help focus the discussion.
```
brms_fit <- brms::brm(bf(y ~ 1 + (1 | x_1) + (1 | x_2) + (1 | group),
shape ~ 1 + (1 | x_1) + (1 | x_2) + (1 | group),
hu ~ 1 + (1 | x_1) + (1 | x_2) + (1 | group)),
family = hurdle_gamma(),
iter = 100,
cores = 8, chains = 8,
data = simulated_data)
```
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### Author:  [@martinmodrak](https://discourse.mc-stan.org/u/martinmodrak)
#### Post date: [July 18, 2020, 11:30am UTC](https://discourse.mc-stan.org/t/choosing-priors-for-a-brms-hurdle-model/16445/2 "2020-07-18T11:30:22Z")
Sorry it looks like your question fell through. Maybe @Guido_Biele is not busy and can answer?
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### Author:  [@Guido\_Biele](https://discourse.mc-stan.org/u/Guido_Biele)
#### Post date: [July 19, 2020, 10:37am UTC](https://discourse.mc-stan.org/t/choosing-priors-for-a-brms-hurdle-model/16445/3 "2020-07-19T10:37:31Z")
Hi,
I’ve only access to my phone for the next days, so here are a few short hints:
- it’s useful to look at the Stan code, ‘@model’ in the brmsfit object, together with the output of prior\_summary. If you start with a simple model (no random effects) the brms-generated Stan models can be self-explanatory.
- you can use the functions make\_stancode and get\_prior to output Stan models and priors before compiling the model.
- to check the effects of priors (prior predictive check) you can run a model with the option sample\_prior = “only”. Doing this is in my experience important when you.are dealing with models that use link functions that are not “identify”. It’s worth having a look at how brms models shape and rate of the gamma.