rt_risk: Calibrated risk index for repeat test-takers

In retestR: Model-Based Anomaly Detection for Repeat Test-Takers

Calibrated risk index for repeat test-takers

Description

Combines evidence statistics into 'T = sum(z)' and calibrates it by parametric bootstrap: 'n_null' complete replicate administrations are simulated under the fitted no-misconduct model (same persons, forms, covariates; attempt-1 ability drawn from each person's posterior; honest growth; honest response times), and the full evidence pipeline is rerun on each. Because the null distribution comes from the same pipeline, the correlation between evidence sources is accounted for, and 'p_value' is an honest false-positive rate for an honest repeater.

Usage

rt_risk(fit, evidence = NULL, n_null = 10, alpha = 0.01, seed = NULL)

Arguments

fit	An 'rt_fit'.
evidence	Which statistics to combine: any of '"gain"', '"exposed"', '"rt"'. The default combines 'exposed' and 'rt' (when response times are available). 'z_gain' is always reported but not combined by default: in known-truth simulations it mostly repeats the exposed-item signal with extra noise, and adding it lowered detection at a fixed false-positive rate.
n_null	Null replicates (each is a full administration).
alpha	Flagging level.
seed	Optional seed.

Value

An 'rt_risk' data frame: evidence columns, 'T', per-component empirical p-values, 'p_value', 'q_value' (Benjamini-Hochberg), 'flag'.

Examples

sim <- rt_simulate(n_persons = 200, form_exposed = 20, form_new = 10, seed = 1)
fit <- rt_fit(sim)
risk <- rt_risk(fit, n_null = 2, alpha = 0.01, seed = 1)
risk
table(flagged = risk$flag, preknowledge = sim$truth$preknowledge)

retestR documentation built on Oct. 8, 2026, 5:08 p.m.