Diagnose lambda values over a grid

Fits fit_mixed_subjects() or fit_mixed_subjects_split() over a set of candidate lambda values. The returned summary reports the fitted mixed-subjects objective and the observed human expected-count loss for each candidate. This is a sensitivity diagnostic, not a valid tuning rule.

Usage

diagnose_lambda_grid(
  lambda_grid,
  observed,
  predicted,
  generated,
  split = FALSE,
  ...
)

Arguments

lambda_grid

Numeric vector of lambda values in [0, 1].

observed, predicted, generated

Response matrices passed to fit_mixed_subjects().

split

Logical; if TRUE, call fit_mixed_subjects_split().

...

Additional arguments passed to the selected fitting function.

Value

A list with summary, lowest_observed_loss_lambda, and all fitted model objects.

Examples

set.seed(3)
pars <- data.frame(a = c(1, 1.2, 0.9), d = c(0, -0.5, 0.3))
observed <- simulate_2pl(rnorm(30), pars)
predicted <- observed
generated <- simulate_2pl(rnorm(80), pars)
tuned <- diagnose_lambda_grid(
  c(0, 0.5),
  observed, predicted, generated,
  initial_pars = pars, n_quad = 5, control = list(maxit = 30)
)
tuned$summary
#>   lambda mixed_loss observed_loss convergence
#> 1    0.0   1.654606      1.654606           0
#> 2    0.5   1.793915      1.684653           0