--- title: "Synthetic Advanced Pupillometry Gallery" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Synthetic Advanced Pupillometry Gallery} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} knitr::opts_chunk$set(collapse = TRUE, comment = "#>", fig.width = 7, fig.height = 4.5) ``` ```{r} library(gp3bayes) sim <- simulate_advanced_pupil_timecourse( n_participants = 14, trials_per_participant = 4, time_points = 35, heteroskedastic_strength = 0.7, ar = 0.5, outlier_fraction = 0.02, missing_fraction = 0.06, seed = 3080 ) ``` # Observed trajectories ```{r} plot_advanced_pupil_simulation(sim, observed = TRUE) ``` # Stored latent mean ```{r} plot_advanced_pupil_simulation(sim, observed = FALSE) ``` # Temporal dependence ```{r} temporal <- audit_pupil_temporal_dependence(sim$data) plot_pupil_temporal_dependence(temporal) ``` # Missingness ```{r} missing <- create_pupil_missingness_spec(response = "model") measurement <- create_pupil_measurement_model( baseline_error = "baseline_se", luminance_error = "luminance_se", response_error = "pupil_se" ) spec <- specify_advanced_pupil_timecourse_model( sim$data, covariates = c("baseline_pupil", "luminance"), measurement_model = measurement, missingness_model = missing, autocorrelation = "none" ) missing_audit <- audit_pupil_missingness(spec) plot_pupil_missingness(missing_audit) measurement_audit <- audit_pupil_measurement_model(spec) plot_pupil_measurement_uncertainty(measurement_audit) ``` # Computational audit ```{r} plot_pupil_model_complexity(spec) ``` # Binocular example ```{r} bi <- simulate_binocular_pupil_timecourse( n_participants = 10, trials_per_participant = 4, time_points = 31, seed = 3081 ) prep <- prepare_binocular_pupil_timecourse(bi$data) audit_binocular_pupil_readiness(prep) ``` The gallery is intentionally backend-free so it can be built during package checks without compiling Stan. Full posterior plot examples are shown in the modelling vignettes with fitting code disabled by default.