Using priorsense with JAGS

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

set.seed(123)

priorsense is compatible with models fit with either jagsUI or R2jags.

Consider the univariate normal model with unknown mu and sigma available viaexample_powerscale_model("univariate_normal"). In JAGS, lprior and log_lik variables can be defined as below. By also defining separate lprior_mu and lprior_sigma variables, it will be possible to check the sensitivity for each prior separately.

model <- example_powerscale_model("univariate_normal", language = "jags")

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

  lprior_mu <- logdensity.norm(mu, 0, 1)
  lprior_sigma <- logdensity.norm(sigma, 0, 1 / 2.5^2)
  lprior <- lprior_mu + lprior_sigma
}

R2jags::jags() or jagsUI::jags() can be used to fit the model. Ensure that the required variables are monitored.

model_con <- textConnection(model$model_code)
data <- model$data

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

fit <- R2jags::jags(
  data = 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(fit)

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

 variable prior likelihood                           diagnosis
       mu 0.376      0.524 potential prior-likelihood conflict
    sigma 0.252      0.468 potential prior-likelihood conflict

powerscale_sensitivity(fit, prior_selection = "sigma")

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

 variable prior likelihood diagnosis
       mu 0.003      0.524         -
    sigma 0.006      0.468         -

powerscale_sensitivity(fit, prior_selection = "mu")

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

 variable prior likelihood                           diagnosis
       mu 0.379      0.524 potential prior-likelihood conflict
    sigma 0.258      0.468 potential prior-likelihood conflict

powerscale_plot_dens(fit)