Fit from precomputed quadrature summaries

Fits the mixed-subjects 2PL objective from quadrature/count summaries rather than raw response matrices. This lower-level interface is useful when the human, paired LLM, and generated LLM summaries have already been linked onto a common scale outside the package.

Usage

fit_mixed_subjects_from_quadrature(
  q_observed,
  q_predicted,
  q_generated,
  lambda = 1,
  initial_pars = NULL,
  slope_lower = 1e-04,
  slope_upper = NULL,
  control = list(maxit = 500)
)

Arguments

q_observed

Quadrature summary for observed human responses. Usually returned by mixed_subjects_quadrature(), but a raw counts object returned by summarize_expected_counts() is also accepted.

q_predicted

Quadrature summary for paired LLM responses/predictions on the labeled human rows.

q_generated

Quadrature summary for generated or unlabeled LLM responses.

lambda

Power-tuning parameter in [0, 1].

initial_pars

Starting item parameters in slope-intercept form. If omitted, q_observed$irt_pars is used when available.

slope_lower

Lower bound for discrimination parameters during optimization. Use NULL for no lower bound.

slope_upper

Upper bound for discrimination parameters during optimization. Use NULL (the default) for no upper bound.

control

Control list passed to stats::optim().

Value

An object of class "mixedsubjects_fit".

Examples

pars <- data.frame(a = c(1, 1.2), d = c(0, -0.5))
resp <- matrix(c(1, 0, 0, 1), nrow = 2, byrow = TRUE)
q <- mixed_subjects_quadrature(resp, item_pars = pars, N_quad = 5)
fit_mixed_subjects_from_quadrature(q, q, q, lambda = 0.5)$item_pars
#>    item         a             d           b
#> 1 Item1 0.0001000  2.446106e-06 -0.02446106
#> 2 Item2 0.1999931 -1.564259e-02  0.07821564