bootstrap_gazepoint_metrics_by_unit: Generalization-target-aligned bootstrap uncertainty

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

Generalization-target-aligned bootstrap uncertainty

Description

Resamples observations or declared clusters while preserving every row that belongs to a sampled cluster. Repeated cluster draws duplicate all associated rows. The returned object records the resampling unit and must not be described as uncertainty for another unit.

Usage

bootstrap_gazepoint_metrics_by_unit(
  task,
  truth,
  prediction = NULL,
  probability = NULL,
  participant_id = NULL,
  stimulus_id = NULL,
  unit = c("observation", "participant", "stimulus", "participant_and_stimulus"),
  bootstrap = 1000L,
  conf_level = 0.95,
  seed = 1L,
  threshold = 0.5,
  stratify_observations = TRUE
)

## S3 method for class 'gp3ml_target_uncertainty'
print(x, ...)

Arguments

task	Governed task.
truth	Observed outcomes.
prediction	Predicted classes or numeric outcomes.
probability	Positive-class probabilities.
participant_id	Participant identifiers for participant-based methods.
stimulus_id	Stimulus identifiers for stimulus-based methods.
unit	Resampling unit.
bootstrap	Number of replicates.
conf_level	Percentile interval level.
seed	Deterministic seed.
threshold	Classification threshold.
stratify_observations	Whether the observation-level classification bootstrap preserves class counts.
x	An object returned by the corresponding gp3ml constructor, evaluator, summarizer, or validator.
...	Additional arguments passed to the print method.

Value

A gp3ml_target_uncertainty object.

Examples

data <- simulate_gazepoint_governed_data(12L, 4L, 1L, 404L)
task <- create_gazepoint_synthetic_task(data, "recording_quality", "new_participants")
probability <- seq(0.15, 0.85, length.out = nrow(data))
prediction <- factor(
  ifelse(probability >= 0.5, "review", "pass"),
  levels = levels(data$quality_status)
)
uncertainty <- bootstrap_gazepoint_metrics_by_unit(
  task,
  truth = data$quality_status,
  prediction = prediction,
  probability = probability,
  participant_id = data$participant_id,
  unit = "participant",
  bootstrap = 20L,
  seed = 404L
)
uncertainty

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