brms-priors.R

library(brms)
library(tidyverse)

m1 <- rstanarm::stan_glm(mpg~wt, data=mtcars)
prior_summary(m1)

# see what priors brms will let you set
get_prior(mpg~wt, data=mtcars)
# then then mimic beta priors from rstanarm (inc rstanarm autoscaling)
m2 <- brm(mpg~wt, data=mtcars, sample_prior="only", 
          prior = c(prior_string("normal(0,15.4)", class="b"),
                    prior = prior_string("normal(0,60.27)", class="Intercept" )))
samps <- tidybayes::add_fitted_samples(tibble(wt=1:2), m2)