process_validation_design: Define an empirical process-validation design

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

Define an empirical process-validation design

Description

The design is intentionally explicit. It records measurement conditions under which recovery, uncertainty, convergence, and failure behavior will be evaluated. It does not imply that every combination is appropriate for every estimator.

Usage

process_validation_design(
  n_persons = c(50L, 150L, 500L),
  n_trials = c(10L, 30L, 80L),
  missingness = c(0, 0.05, 0.15, 0.3),
  sampling_rate_hz = c(60, 120, 300, 1000),
  aoi_error = c("low", "moderate", "severe"),
  calibration_error = c(0, 0.5, 1),
  pupil_dropout = c(0, 0.1, 0.3),
  heterogeneity = c("low", "moderate"),
  model_misspecification = c(FALSE, TRUE),
  replications = 100L,
  seed = 1L,
  label = "process_validation"
)

Arguments

n_persons	Participant counts.
n_trials	Trial/item counts per participant.
missingness	Proportion of generic process observations made missing.
sampling_rate_hz	Nominal sampling rates.
aoi_error	AOI uncertainty regimes.
calibration_error	Calibration-error regimes in user-defined units.
pupil_dropout	Pupil-specific dropout proportions.
heterogeneity	Participant-heterogeneity regimes.
model_misspecification	Logical regimes indicating deliberate mismatch.
replications	Monte Carlo replications per condition.
seed	Master simulation seed.
label	Optional design label.

Value

An 'eye_process_validation_design' object.

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