Run simulation-based calibration for known-item IRT ability scoring — run_eyeprocess_irt_ability

Simulates abilities from the declared normal prior, responses from the known item-response model, and posterior draws from the same grid-based scoring algorithm used by `eyeprocess_irt_eap_score()`. This validates computational calibration of the scoring workflow under the declared generative model; it does not establish empirical adequacy or construct validity.

Usage

run_eyeprocess_irt_ability_sbc(
  items,
  replications = 200L,
  posterior_draws = 99L,
  theta_grid = seq(-5, 5, length.out = 401),
  prior_mean = 0,
  prior_sd = 1,
  interval = 0.95,
  seed = 20260811L,
  D = 1
)

Arguments

items: Item-parameter data frame or item collection.
replications: Number of simulation or validation replications.
posterior_draws: Number of posterior draws generated per SBC replication.
theta_grid: Grid of latent-trait values used for numerical scoring or integration.
prior_mean: Mean of the normal latent-trait prior.
prior_sd: Standard deviation of the normal latent-trait prior.
interval: Central posterior interval probability used for coverage assessment.
seed: Random-number seed for reproducible execution.
D: Logistic scaling constant.