Check Binary Prior Predictive Behaviour — check_binary_prior

Simulates replicated binary outcomes from the declared prior specification and prepared design without calling a Bayesian fitting backend.

Usage

check_binary_prior_predictive(
  specification,
  draws = 500,
  seed = 1,
  plausible_rate = c(0.01, 0.99),
  boundary_probability = c(0.01, 0.99),
  extreme_contrast = 0.8,
  maximum_degenerate_participant_fraction = 0.5,
  maximum_boundary_mass = 0.5,
  maximum_extreme_probability = 0.25
)

Arguments

specification: A gp3bayes_binary_model_specification.
draws: Number of prior predictive data sets.
seed: Non-negative integer random-number seed.
plausible_rate: Increasing lower and upper limits for plausible overall and condition-specific event rates.
boundary_probability: Probability thresholds used to identify prior mass close to zero and one.
extreme_contrast: Absolute probability-scale condition contrast considered extreme.
maximum_degenerate_participant_fraction: Maximum participant fraction allowed to have all-zero or all-one replicated outcomes in a draw.
maximum_boundary_mass: Maximum fraction of row probabilities allowed beyond the declared boundary thresholds in a draw.
maximum_extreme_probability: Maximum acceptable fraction of prior predictive draws violating each criterion.

Value

A gp3bayes_binary_prior_predictive_check containing replicated summaries, structured checks, thresholds, and the seed.

Details

Failure does not select or alter priors automatically. It indicates that the declared priors and design generate outcomes that require substantive review. This check assesses prior implications, not posterior adequacy or model fit.

Examples

simulation <- simulate_hierarchical_binary_data(
  n_participants = 12,
  trials_per_participant = 8,
  seed = 2026
)

contract <- create_model_contract(
  family = "binary",
  outcome_col = "selected",
  participant_col = "participant_id",
  item_col = "item_id",
  trial_col = "trial_id",
  condition_col = "condition"
)

prepared <- prepare_hierarchical_binary_data(
  simulation$data,
  contract,
  condition_levels = c("control", "treatment")
)

specification <- specify_binary_model(
  prepared,
  baseline = 0.35
)

check_binary_prior_predictive(
  specification,
  draws = 100,
  seed = 2027
)
#> <gp3bayes_binary_prior_predictive_check>
#>   Adequate: TRUE
#>   Draws: 100
#>   Failed checks: 0
#>   Backend: none
#>   Fit performed: FALSE