wald_dtf_test: Wald test of differential test functioning (DTF)
In aiDIF: Differential Item Functioning for AI-Scored Assessments
View source: R/robust_engine.R
wald_dtf_test R Documentation
Wald test of differential test functioning (DTF)
Description
Tests H0: mean(y) - theta = 0, i.e. whether the robust
scale estimate differs significantly from the naive mean.
A significant result indicates meaningful DTF.
Usage
wald_dtf_test(y, theta, weights, Vcov_H0, Vcov_raw, k)
Arguments
y
Scaling function values.
theta
Robust scale estimate.
weights
Bi-square weights from IRLS.
Vcov_H0
Covariance of y under H0 (theta plugged in).
Vcov_raw
Covariance of y (no theta substitution).
k
Bi-square tuning parameter.
Value
A one-row data.frame.
aiDIF documentation built on April 22, 2026, 1:10 a.m.
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View source: R/robust_engine.R
| wald_dtf_test | R Documentation |
Wald test of differential test functioning (DTF)
Description
Tests H0: mean(y) - theta = 0, i.e. whether the robust scale estimate differs significantly from the naive mean. A significant result indicates meaningful DTF.
Usage
wald_dtf_test(y, theta, weights, Vcov_H0, Vcov_raw, k)
Arguments
y |
Scaling function values. |
theta |
Robust scale estimate. |
weights |
Bi-square weights from IRLS. |
Vcov_H0 |
Covariance of y under H0 (theta plugged in). |
Vcov_raw |
Covariance of y (no theta substitution). |
k |
Bi-square tuning parameter. |
Value
A one-row data.frame.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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