validate_recovery: Validate Parameter Recovery via Monte Carlo Simulation

In guess: Adjust Estimates of Learning for Guessing

Validate Parameter Recovery via Monte Carlo Simulation

Description

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

Usage

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

## Not run: 
# 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
)

## End(Not run)

guess documentation built on Aug. 3, 2026, 9:07 a.m.