compute_criterion: Compute Performance Criteria for a Single Parameter...

In irtsim: Monte Carlo Simulation-Based Sample-Size Planning for Item Response Theory

Compute Performance Criteria for a Single Parameter (Internal)

Description

Given a vector of per-iteration estimates and the true parameter value, computes bias, empirical SE, MSE, RMSE, coverage, and Monte Carlo SEs following Morris et al. (2019).

Usage

compute_criterion(estimates, true_value, ci_lower = NULL, ci_upper = NULL)

Arguments

estimates	Numeric vector of per-iteration parameter estimates. May contain NAs (non-converged iterations), which are excluded.
true_value	Single numeric value. The data-generating (true) parameter value.
ci_lower	Optional numeric vector (same length as estimates). Lower bounds of confidence intervals. If NULL, coverage is not computed.
ci_upper	Optional numeric vector (same length as estimates). Upper bounds of confidence intervals. If NULL, coverage is not computed.

Value

A named list with elements:

bias

Mean estimate minus true value.

empirical_se

Sample standard deviation of estimates (n-1 denominator).

mse

Mean squared error: mean((estimate - true_value)^2).

rmse

Root mean squared error: sqrt(mse).

coverage

Proportion of CIs containing the true value, or NULL if CIs not provided. NAs in CIs are excluded from the denominator.

mcse_bias

Monte Carlo SE of bias: empirical_se / sqrt(K).

mcse_mse

Monte Carlo SE of MSE: sd((est - true)^2) / sqrt(K).

References

Morris, T. P., White, I. R., & Crowther, M. J. (2019). Using simulation studies to evaluate statistical methods. Statistics in Medicine, 38(11), 2074–2102. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.8086")}

irtsim documentation built on April 24, 2026, 1:07 a.m.