estimate_robust_scale: Robust DIF scale estimation via IRLS

In aiDIF: Differential Item Functioning for AI-Scored Assessments

Robust DIF scale estimation via IRLS

Description

Estimates a robust location parameter for the vector of IRT scaling functions using iteratively re-weighted least squares (IRLS) with the bi-square loss. This is the core estimation engine of aiDIF.

Usage

estimate_robust_scale(
  mle,
  alpha = 0.05,
  scale_by = "pooled",
  tol = 1e-07,
  maxit = 100L
)

Arguments

mle	A validated mle list.
alpha	Significance level controlling the bi-square tuning parameter k = z_{1-\alpha/2}. Default 0.05.
scale_by	Scaling denominator; passed to compute_scaling_fn. Default "pooled".
tol	Convergence tolerance. Default 1e-7.
maxit	Maximum IRLS iterations. Default 100.

Value

A list of class rdif_fit with elements:

est

Estimated robust scale parameter.

weights

Bi-square item weights.

rho_value

Value of objective at solution.

n_iter

Number of iterations used.

k

Tuning parameter used.

y

Raw scaling function values.

vcov_est

Covariance matrix of y at solution.

dif_test

Wald item-level DIF test (data.frame).

dtf_test

Wald test of differential test functioning.

Examples

dat <- simulate_aidif_data(n_items = 5, seed = 1)
fit <- estimate_robust_scale(dat$human)
print(fit$est)

aiDIF documentation built on April 22, 2026, 1:10 a.m.