rt_evidence: Evidence statistics for each repeater

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

Evidence statistics for each repeater

Description

Each statistic is a posterior-predictive z-score, positive in the suspicious direction:

'z_gain'

Attempt-2 total score against the distribution predicted from attempt 1 plus the fitted expected growth for this person's covariates. A large gain after long study and remediation is expected; the same gain after two weeks is not.

'z_exposed'

Attempt-2 score on exposed items against the prediction from attempt 1, expected growth and attempt-2 new items. Gains concentrated on exposed items are the signature of preknowledge.

'z_rt'

Mean log-speed on exposed minus new items (lognormal RT model with known time intensities); present when response times are.

Usage

rt_evidence(fit)

Arguments

fit	An 'rt_fit'.

Value

Data frame: 'person', 'S1', 'S2', 'z_gain', 'z_exposed', and 'z_rt'.

Examples

sim <- rt_simulate(n_persons = 200, form_exposed = 20, form_new = 10, seed = 1)
fit <- rt_fit(sim)
ev <- rt_evidence(fit)
# preknowledge shows up on exposed items and in speed, not only in the gain
aggregate(ev[c("z_gain", "z_exposed", "z_rt")],
          list(preknowledge = sim$truth$preknowledge), mean)

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