Validate Parameter Recovery via Monte Carlo Simulation

Performs Monte Carlo simulations to assess parameter recovery of the LCA model. Useful for validating estimator performance.

validate_recovery(true_params, n = 500, n_items = 2, n_sims = 100, seed = NULL)

Arguments

true_params: Named numeric vector of true parameters. For no-DK model: c(gg=, gk=, kk=, gamma=) For DK model: c(gg=, gk=, gd=, kk=, dg=, dk=, dd=, gamma=)
n: Integer. Sample size per simulation. Default 500.
n_items: Integer. Number of items. Default 2.
n_sims: Integer. Number of Monte Carlo simulations. Default 100.
seed: Optional integer. Random seed for reproducibility.

Value

Data frame with one row per parameter containing columns: parameter (name), true_value, mean_estimate, bias (mean estimate minus true), rmse (root mean squared error), and se (Monte Carlo standard deviation of estimates).

Examples

if (FALSE) { # \dontrun{
# Validate no-DK model recovery
results <- validate_recovery(
  c(gg = 0.35, gk = 0.30, kk = 0.35, gamma = 0.25),
  n = 500, n_sims = 50
)
print(results)

# Validate DK model recovery
results_dk <- validate_recovery(
  c(
    gg = 0.25, gk = 0.15, gd = 0.10, kk = 0.20,
    dg = 0.10, dk = 0.10, dd = 0.10, gamma = 0.25
  ),
  n = 500, n_sims = 50
)
} # }