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Using priorsense with JAGS

library(R2jags)
library(posterior)
library(priorsense)

To use priorsense with a JAGS model, the log prior and log likelihood evaluations should be added to the model code.

model_string <- "
model {
  for(n in 1:N) {
    y[n] ~ dnorm(mu, tau)
    log_lik[n] <- likelihood_alpha * logdensity.norm(y[n], mu, tau)
  }
  mu ~ dnorm(0, 1)
  sigma ~ dnorm(0, 1 / 2.5^2) T(0,)
  tau <- 1 / sigma^2
  lprior <- prior_alpha * logdensity.norm(mu, 0, 1) + logdensity.norm(sigma, 0, 1 / 2.5^2)
}
"

Using R2jags::jags() to fit the model.

model_con <- textConnection(model_string)
data <- example_powerscale_model()$data

set.seed(123)

# monitor parameters of interest along with log-likelihood and log-prior
variables <- c("mu", "sigma", "log_lik", "lprior")

jags_fit <- jags(
  data,
  model.file = model_con,
  parameters.to.save = variables,
  n.chains = 4,
  DIC = FALSE,
  quiet = TRUE,
  progress.bar = "none"
  )

Then the priorsense functions will work as usual.

powerscale_sensitivity(jags_fit)

Sensitivity based on cjs_dist
Prior selection: all priors
Likelihood selection: all data

 variable prior likelihood                     diagnosis
       mu 0.753      0.524 potential prior-data conflict
    sigma 0.503      0.468 potential prior-data conflict

powerscale_plot_dens(jags_fit)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.