rt_fit: Fit the expected-gain model

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

Fit the expected-gain model

Description

Marginal maximum likelihood on a theta grid, in two stages: (1) the attempt-1 ability distribution of repeaters, N(mu1, s1); (2) growth 'theta2 = theta1 + X beta + N(0, sigma_growth)' from attempt-1 responses and attempt-2 responses to **new items only**. Because exposed items never enter the growth model, preknowledge cannot inflate the expected gain, and conditioning on the full attempt-1 likelihood handles regression to the mean.

Usage

rt_fit(
  data,
  growth = ~log(days_between) + remediation,
  grid = seq(-5, 5, by = 0.2)
)

Arguments

data	An 'rt_data' (or 'rt_sim') object.
growth	One-sided formula for mean growth, evaluated in 'data$persons'.
grid	Theta grid.

Value

An 'rt_fit' object.

Examples

sim <- rt_simulate(n_persons = 200, form_exposed = 20, form_new = 10, seed = 1)
fit <- rt_fit(sim)
fit    # growth coefficients: intercept, log(days_between), remediation

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