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The sensitivity layer records alternative analysis states without selecting the scenario that produces the largest effect.
sim <- simulate_pupil_timecourse(
n_participants = 5,
trials_per_participant = 4,
sampling_frequency = 20,
time_window = c(-0.6, 1.4),
include_gaze = TRUE,
include_luminance = TRUE,
seed = 73
)
contract <- create_pupil_contract(
outcome_col = "pupil_mm",
participant_col = "participant_id",
trial_col = "trial_id",
condition_col = "condition",
time_col = "event_time",
pupil_unit = "millimetres",
sampling_frequency = 20,
blink_col = "blink",
interpolation_col = "interpolated",
gaze_x_col = "gaze_x",
gaze_y_col = "gaze_y",
luminance_col = "luminance",
baseline_window = c(-0.6, 0),
pfe_corrected = FALSE
)
prepared <- prepare_pupil_timecourse(sim$data, contract)
spec_for_sensitivity <- specify_pupil_timecourse_model(
prepared,
autocorrelation = "none",
smooth_basis_dimension = 5
)
suite <- create_pupil_sensitivity_suite(
spec_for_sensitivity,
baseline_windows = list(c(-0.6, -0.1), c(-0.4, -0.1)),
baseline_window_operation = "subtract",
baseline_operations = c("none", "subtract"),
interpolation_policy = c("retain", "exclude_flagged"),
gaze_adjustment = c("none", "declared_covariates"),
luminance_adjustment = c("none", "declared_covariate"),
smooth_basis_dimensions = c(5, 7),
autocorrelation = c("none", "ar1"),
analysis_windows = list(c(0.2, 1.0))
)
pupil_sensitivity_table(suite)
#> scenario_id axis value
#> 1 S001 baseline_window -0.6,-0.1
#> 2 S002 baseline_window -0.4,-0.1
#> 3 S003 baseline_operation none
#> 4 S004 baseline_operation subtract
#> 5 S005 interpolation_policy retain
#> 6 S006 interpolation_policy exclude_flagged
#> 7 S007 gaze_adjustment none
#> 8 S008 gaze_adjustment declared_covariates
#> 9 S009 luminance_adjustment none
#> 10 S010 luminance_adjustment declared_covariate
#> 11 S011 smooth_basis_dimension 5
#> 12 S012 smooth_basis_dimension 7
#> 13 S013 autocorrelation none
#> 14 S014 autocorrelation ar1
#> 15 S015 analysis_window 0.2,1scenario_id <- pupil_sensitivity_table(suite)$scenario_id[1]
scenario <- materialize_pupil_sensitivity_scenario(suite, scenario_id)
scenario$scenario_id
#> [1] "S001"
scenario$specification
#> <gp3bayes_pupil_model_specification>
#> Family: Gaussian pupil time-course
#> Formula: .pupil_model ~ .condition + s(.event_time, by = .condition, k = 5) + (1 | .participant)
#> Temporal structure: smooth
#> Condition trajectory: TRUE
#> Autocorrelation: none
#> Outcome unit: millimetres
#> Baseline: subtract
#> Unrestricted formula: FALSE
#> Fit performed: FALSEEach scenario can be fitted and reduced to the same declared
estimand. compare_pupil_sensitivity_estimands() then places
those estimands side by side. It does not identify a winner.
PFE and luminance are handled as measurement-context variables. The 0.4 foundation can audit them and compare explicitly declared adjusted/unadjusted specifications, but it does not invent a universal PFE correction or a Bayesian Open-DPSM replacement.
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.