bootstrap_gazepoint_metrics: Bootstrap uncertainty intervals for performance metrics

In gp3ml: Governance-First Predictive Modelling for 'Gazepoint' Research

Bootstrap uncertainty intervals for performance metrics

Description

Bootstrap uncertainty intervals for performance metrics

Usage

bootstrap_gazepoint_metrics(
  task,
  truth,
  prediction = NULL,
  probability = NULL,
  threshold = 0.5,
  bootstrap = 1000L,
  conf_level = 0.95,
  seed = 1L
)

Arguments

task	A governed gp3ml_task object.
truth	Observed outcome values.
prediction	Predicted classes or numeric values.
probability	Predicted positive-class probabilities.
threshold	Probability threshold for classification.
bootstrap	Number of bootstrap replicates.
conf_level	Confidence level for percentile intervals.
seed	Deterministic random seed.

Value

A gp3ml_metric_uncertainty object containing point estimates, percentile intervals, bootstrap draws, resampling settings, and the governed task.

Examples

example_data <- data.frame(
  participant_id = rep(sprintf("P%02d", 1:12), each = 2),
  trial_id = sprintf("T%02d", 1:24),
  stimulus_id = rep(c("S01", "S02"), 12),
  condition = rep(c("A", "B"), 12),
  fixation_duration = 180 + seq_len(24),
  pupil_change = sin(seq_len(24) / 3),
  stringsAsFactors = FALSE
)
example_data$quality_status <- factor(
  c(
    "pass", "review", "pass", "review", "review", "pass",
    "review", "pass", "pass", "review", "review", "pass",
    "review", "pass", "review", "pass", "pass", "review",
    "pass", "review", "review", "pass", "pass", "review"
  ),
  levels = c("pass", "review")
)
task <- declare_gazepoint_task(
  data = example_data,
  outcome = "quality_status",
  purpose = "Predict predefined recording-quality review status",
  task_type = "classification",
  unit_id = "trial_id",
  participant_id = "participant_id",
  stimulus_id = "stimulus_id",
  generalization_target = "new_participants",
  positive = "review"
)
probability <- seq(
  0.20,
  0.80,
  length.out = nrow(example_data)
)
predicted <- factor(
  ifelse(probability >= 0.5, "review", "pass"),
  levels = levels(example_data$quality_status)
)
uncertainty <- bootstrap_gazepoint_metrics(
  task = task,
  truth = example_data$quality_status,
  prediction = predicted,
  probability = probability,
  bootstrap = 10L,
  seed = 101L
)
uncertainty

gp3ml documentation built on Aug. 23, 2026, 5:11 p.m.