Creating a prior function that sets up "default priors" that are scaled based on the data --rstanarm priors---

Basically, I want to be able to build a def_prior() function that mimics rstanarm priors so that I can use them in brms, it would allow me to run this

m_hibbs <- brm(vote ~ 1 + growth, # 1 + is by default
               data = hibbs,
               family = gaussian(), # default
               prior = def_priors(),
               seed = 123

and the function would under the hood would define the following priors:

 c(prior(normal(52, 14), #mean(hibbs$vote), 2.5* sd(hibbs$vote)
                      class = Intercept),
                prior(normal(0, 10), #2.5 * sd(hibbs$vote)/sd(hibbs$growth)
                      class = b,
                      coef = growth),
                prior(exponential(0.18), #1/sd(hibbs$vote)
                      class = sigma))

It’s ok if it’s not straightforward, and I’m not afraid of looking at brms source code if there are any pointers there. But is it even possible?

brms already does something similar at least for the intercept and sigma:

brms::default_prior(speed ~ dist, cars)
#>                  prior     class coef group resp dpar nlpar lb ub       source
#>                 (flat)         b                                       default
#>                 (flat)         b dist                             (vectorized)
#>  student_t(3, 15, 5.9) Intercept                                       default
#>   student_t(3, 0, 5.9)     sigma                             0         default

Created on 2024-04-04 with reprex v2.1.0

You can take a look at the .default_prior function code to see how it does it. The final function to which all is passed eventually is def_scale_prior.brmsterms

So in principle it should be possible to replicate that behavior, but the caveat is that you wouldn’t just be able to pass a def_priors() function to the prior argument. You will need to pass the formula and the data to def_priors(formula, data)

Actually, by exploiting some magic with environments, you could make it work. Here’s a functioning example:

def_prior <- function() {
  # get the environment of the brm parent call and relevant variables
  env <- sys.frame(1)
  bterms <- env$bterms
  data <- env$data
  # get the response variable
  mframe <- model.frame(bterms, data)
  y <- unname(model.response(mframe))
  preds <- model.matrix(bterms$formula, mframe) # remove intercept
  # intercept prior
  sd_y <- sd(y)
  mean <- mean(y)
  prior <- paste0("normal(", round(mean, 2), ", ", round(2.5 * sd_y, 2), ")")
  prior <- set_prior(prior, class = "Intercept")
  # effects prior (first column is intercept so skip it)
  for (i in 2:ncol(preds)) {
    sd <- sd(preds[,i])
    prior_pred <- paste0("normal(", 0, ", ", round(2.5 * sd_y/sd, 2), ")")
    prior <- prior + set_prior(prior_pred, class = 'b', coef = colnames(preds)[i])
  # sigma prior
  prior_sigma <- paste0("exponential(", round(1/sd_y, 2), ")")
  prior <- prior + set_prior(prior_sigma, class = "sigma")

a <- brm(speed ~ dist, cars, 
               prior = def_prior(),
               empty = TRUE)

               prior     class coef group resp dpar nlpar lb ub  source
              (flat)         b                                  default
     normal(0, 0.51)         b dist                                user
 normal(15.4, 13.22) Intercept                                     user
   exponential(0.19)     sigma                             0       user

I was quite surprised this works, but it was fun to figure out! Of course, you will need to be careful about figuring out when centering is used, the presence of random effects, whether sigma is predicted (it changes the link function to log), but you could use this as a starting point.

Not sure how robust or reliable this approach would be. Perhaps @paul.buerkner could chime in