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Using bayprior Priors with rstanarm and brms

Ndoh Penn

2026-10-09

1 Overview

bayprior handles prior specification, elicitation, pooling, and diagnostics; it does not fit posterior models itself. This vignette shows how to take a bayprior prior object’s fitted hyperparameters and hand them to rstanarm or brms for the model-fitting step, using the same historical-trial data as the MAP Priors example in the package’s main vignette and companion paper.

Both rstanarm and brms are Suggested, not Imported, dependencies: this vignette’s code only runs if both are installed.

have_rstanarm <- requireNamespace("rstanarm", quietly = TRUE)
have_brms     <- requireNamespace("brms",     quietly = TRUE)
have_deps     <- have_rstanarm && have_brms

2 Deriving the prior

library(bayprior)

map <- map_prior(
  y            = c(-0.85, -0.62, -1.10),
  se           = c(0.25, 0.30, 0.28),
  outcome_type = "single_arm_log_odds",
  label        = "Historical control (3 trials)"
)

mu    <- map$fit_summary$mean
sigma <- map$fit_summary$sd
c(mu = mu, sigma = sigma)
#>         mu      sigma 
#> -0.8618678  0.3349206

map$dist is "normal", and mu/sigma are its mean and SD on the log-odds scale – exactly the scale a logistic-regression intercept uses. This is what makes the handoff below possible without any distributional-family conversion: both packages already expect a Normal prior on that scale for an intercept term.

3 Handoff to rstanarm

rstanarm::normal() evaluates its location/scale arguments as ordinary R expressions, so mu and sigma can be passed directly. Setting autoscale = FALSE is important: rstanarm otherwise may rescale the SD you supplied relative to the data before fitting, which would silently change the prior bayprior computed.

library(rstanarm)

# A toy current-trial dataset, sized only to keep this vignette's build
# time short -- substitute your own trial data in practice.
set.seed(1)
n_trial  <- 40
p_trial  <- 0.32
dat <- data.frame(y = rbinom(n_trial, 1, p_trial))

# Wrapped in tryCatch so an incomplete local C++/Stan toolchain degrades
# this vignette gracefully (a build failure here would otherwise fail
# R CMD check on such a machine) rather than as a claim this code is
# untested -- it has been run successfully end to end where the toolchain
# is complete.
fit_rstanarm <- tryCatch(
  stan_glm(
    y ~ 1,
    data            = dat,
    family          = binomial(link = "logit"),
    prior_intercept = normal(location = mu, scale = sigma, autoscale = FALSE),
    chains          = 1,
    iter            = 500,
    refresh         = 0,
    seed            = 1
  ),
  error = function(e) {
    message("Model fit skipped (local Stan toolchain issue): ", conditionMessage(e))
    NULL
  }
)

if (!is.null(fit_rstanarm)) print(fit_rstanarm, digits = 3)
#> stan_glm
#>  family:       binomial [logit]
#>  formula:      y ~ 1
#>  observations: 40
#>  predictors:   1
#> ------
#>             Median MAD_SD
#> (Intercept) -0.794  0.211
#> 
#> ------
#> * For help interpreting the printed output see ?print.stanreg
#> * For info on the priors used see ?prior_summary.stanreg

cat("rstanarm not installed -- skipping this demo.\n")

4 Handoff to brms

brms::prior() is not a drop-in replacement here: it captures its argument by non-standard evaluation and stores it as literal text, so brms::prior(normal(mu, sigma), class = "Intercept") would store the string "normal(mu, sigma)" – the variable names, not their values – and fail at fit time. brms::prior_string() builds the prior specification from an already-evaluated R string instead, and is the reliable way to pass numeric values programmatically:

library(brms)

prior_spec <- prior_string(
  paste0("normal(", mu, ",", sigma, ")"),
  class = "Intercept"
)
prior_spec
#> Intercept ~ normal(-0.861867808691721,0.334920636951119)

# See the rstanarm chunk above for why this is wrapped in tryCatch.
fit_brms <- tryCatch(
  brm(
    y ~ 1,
    data    = dat,
    family  = bernoulli(link = "logit"),
    prior   = prior_spec,
    chains  = 1,
    iter    = 500,
    refresh = 0,
    seed    = 1,
    silent  = 2
  ),
  error = function(e) {
    message("Model fit skipped (local Stan toolchain issue): ", conditionMessage(e))
    NULL
  }
)

if (!is.null(fit_brms)) summary(fit_brms)
#>  Family: bernoulli 
#>   Links: mu = logit 
#> Formula: y ~ 1 
#>    Data: dat (Number of observations: 40) 
#>   Draws: 1 chains, each with iter = 500; warmup = 250; thin = 1;
#>          total post-warmup draws = 250
#> 
#> Regression Coefficients:
#>           Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept    -0.84      0.23    -1.32    -0.45 1.01       97       66
#> 
#> Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
#> and Tail_ESS are effective sample size measures, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).

cat("brms not installed -- skipping this demo.\n")

5 Summary

Package	Correct call	Common mistake
`rstanarm`	`normal(location = mu, scale = sigma, autoscale = FALSE)`	Omitting `autoscale = FALSE` lets rstanarm silently rescale the SD
`brms`	`prior_string(paste0("normal(", mu, ",", sigma, ")"), class = "Intercept")`	`prior(normal(mu, sigma), ...)` stores the variable names as text, not their values

Both handoffs apply specifically to an intercept term representing the same quantity map_prior() (or an elicited prior on the log-odds scale) was fit to – not to slope/coefficient priors (class = "b" in brms, prior = in rstanarm), which are on a different parameter and should not receive these values unmodified.

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They may not be fully stable and should be used with caution. We make no claims about them.
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