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library(nimble)
library(priorsense)

priorsense is compatible with models fit with nimble

Consider the univariate normal model with unknown mu and sigma available viaexample_powerscale_model("univariate_normal"). In NIMBLE, 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(language = "nimble")

{
    for (n in 1:N) {
        y[n] ~ dnorm(mu, sd = sigma)
        log_lik[n] <- dnorm(y[n], mu, sd = sigma, log = TRUE)
    }
    mu ~ dnorm(0, sd = 1)
    sigma ~ dnorm(0, sd = 2.5)
    lprior_mu <- dnorm(mu, 0, sd = 1, log = TRUE)
    lprior_sigma <- dnorm(sigma, 0, sd = 2.5, log = TRUE)
    lprior <- lprior_mu + lprior_sigma
}

Instantiate and compile the model as usual.

inits <- list(
  mu = 0,
  sigma = 1
)

model <- nimbleModel(
  model$model_code, # the nimble model code
  data = model$data,
  inits = inits,
  constants = list(N = model$data$N)
)

cmodel <- compileNimble(model)

mcmc <- buildMCMC(
  cmodel,
  monitors = c(
    "mu",
    "sigma",
    "lprior",
    "lprior_mu",
    "lprior_sigma",
    "log_lik"
  )
)

cmcmc <- compileNimble(mcmc, project = cmodel)

For priorsense compatibility, it is easiest to save the draws as a coda::mcmc.list object, by specifying samplesAsCodaMCMC = TRUE. Otherwise, the resulting object can be transformed to a posterior::draws_df object, with posterior::as_draws_df.

fit <- runMCMC(
  cmcmc,
  niter = 20000,
  nburnin = 5000,
  nchains = 4,
  thin = 15,
  setSeed = c(123, 456, 789, 101112),
  samplesAsCodaMCMC = TRUE # alternatively, coerce the output using `posterior::as_draws_df`
)

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 0.399      0.578 potential prior-likelihood conflict
    sigma 0.323      0.607 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 0.005      0.578         -
    sigma 0.009      0.607         -

powerscale_sensitivity(fit, prior_selection = "mu")

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

 variable prior likelihood                           diagnosis
       mu 0.404      0.578 potential prior-likelihood conflict
    sigma 0.332      0.607 potential prior-likelihood conflict

powerscale_plot_dens(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.