The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Robust and Distributional Pupil Models

Why separate location and residual scale?

A Gaussian pupil model with constant residual standard deviation assumes the unexplained variability is similar across the full time course and conditions. gp3bayes 0.5 can instead declare the residual scale as constant, condition-dependent, time-dependent, or condition-by-time dependent. The Student-t option provides a robust observation distribution without automatically labelling individual observations as invalid outliers.

sim <- simulate_advanced_pupil_timecourse(
  n_participants = 10,
  trials_per_participant = 4,
  time_points = 31,
  heteroskedastic_strength = 0.8,
  outlier_fraction = 0.03,
  seed = 3001
)
plot_advanced_pupil_simulation(sim)

gaussian_constant <- specify_pupil_distribution("gaussian", "constant")
gaussian_time <- specify_pupil_distribution("gaussian", "condition_time")
student_time <- specify_pupil_distribution("student", "condition_time")

pupil_distribution_table(gaussian_constant)
#>     family residual_scale robust distributional
#> 1 gaussian       constant  FALSE          FALSE
pupil_distribution_table(gaussian_time)
#>     family residual_scale robust distributional
#> 1 gaussian condition_time  FALSE           TRUE
pupil_distribution_table(student_time)
#>    family residual_scale robust distributional
#> 1 student condition_time   TRUE           TRUE

Candidate specifications are explicit

spec_constant <- specify_advanced_pupil_timecourse_model(
  sim$data,
  family = "gaussian",
  residual_scale = "constant",
  autocorrelation = "none"
)

spec_distributional <- specify_advanced_pupil_timecourse_model(
  sim$data,
  family = "gaussian",
  residual_scale = "condition_time",
  autocorrelation = "none"
)

spec_robust <- specify_advanced_pupil_timecourse_model(
  sim$data,
  family = "student",
  residual_scale = "condition_time",
  autocorrelation = "none"
)

Student-t robustness and residual ARMA are deliberately not combined in the governed 0.5 interface. They should be treated as distinct modelling hypotheses and compared against the declared predictive target.

Posterior residual-scale trajectory

fit_distributional <- fit_advanced_pupil_model_backend(
  spec_distributional,
  backend = "cmdstanr",
  cores = 2
)

sigma <- estimate_pupil_residual_scale(fit_distributional)
pupil_residual_scale_table(sigma)
plot_pupil_residual_scale(sigma)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.