multimodal_m4_recovery: Evaluate deterministic M4 parameter and state recovery

In eyeprocess: Harmonize Eye-Tracking, Pupillometry, Biometrics, and Psychometric Process Data

Evaluate deterministic M4 parameter and state recovery

Description

Recovery aligns latent-state labels before scoring multichannel state effects, and emphasizes posterior-probability calibration, occupancy, and transition recovery rather than treating MAP classification accuracy as the primary criterion. By default the function returns a small five-scenario design and does not launch expensive fitting.

Usage

multimodal_m4_recovery(
  simulation = NULL,
  fit = NULL,
  scenarios = c("clear", "weak", "null", "trait_conditioned", "nuisance_confounded"),
  run = FALSE,
  simulation_args = list(n_person = 60L, n_item = 10L),
  fit_args = list()
)

Arguments

simulation	Optional single M4 simulation.
fit	Optional already-fitted M4 model corresponding to 'simulation'.
scenarios	Deterministic development battery used when 'run = TRUE'.
run	Whether to execute the small recovery battery. Default 'FALSE'.
simulation_args	Named arguments forwarded to simulation.
fit_args	Named arguments forwarded to 'fit_multimodal_m4()'.

Value

An 'eye_multimodal_m4_recovery'.

eyeprocess documentation built on Sept. 28, 2026, 5:08 p.m.