I am struggling to set reasonable weak priors for my lognormal brms model. I read that prior predictive simulation is a good way to play with different prior values and define reasonable ones for publishing.

Model

```
model = brm(bf(received_treatment_hours ~ treatment_method + offer + ( 1 | region)), data = data_no_zeros, family = lognormal(), cores = 3, chains = 3, sample_prior = "only", prior = prior)
```

*Prior knowledge about the outcome variable is that it ranges from 1-130 hours and 98% of patients has this value under 30 hours. Thus, weakly prior should exclude differences over 50 hours or something. I also know that my priors are on log() scale.*

Model default priors using get_prior() argument

```
## prior class coef group resp dpar nlpar bound
## 1 b
## 2 b offer
## 3 b treatment_methodB
## 4 b treatment_methodC
## 5 b treatment_methodD
## 6 b treatment_methodE
## 7 b treatment_methodF
## 8 student_t(3, 2, 10) Intercept
## 9 student_t(3, 0, 10) sd
## 10 sd region
## 11 sd Intercept region
## 12 student_t(3, 0, 10) sigma
```

I tried different values for class b prior, finishing with very low value

```
prior = c(
prior(student_t(3, 0, 0.01), class = b),
prior(student_t(3, 0, 10), class= sd),
prior(student_t(3, 8, 10), class = Intercept))
```

But the result still looks unreasonable, including extremely wide CIs.

```
plot(conditional_effects(model, method = "predict"), points = TRUE, ask = FALSE)
```

**What am I doing wrong? How to get prior predictive simulation plots with reasonable CI widths?**

Data and Rmd file: https://www.dropbox.com/sh/2kwpmxip99hvkes/AAAn90mmKmiPdUzJrF2zUE0sa?dl=0