Audit split-half reliability for Gazepoint pupil outcomes — audit_gazepoint_pupil

Create a split-half reliability audit for trial-level or window-level pupil outcomes. The helper is intended for publication-readiness checks when pupil features are interpreted as stable participant-level outcomes or individual difference measures.

Usage

audit_gazepoint_pupil_reliability(
  data,
  outcome_cols = NULL,
  participant_col = NULL,
  trial_col = NULL,
  split_col = NULL,
  by_cols = NULL,
  split_method = c("odd_even", "first_second"),
  aggregate_function = c("mean", "median"),
  correlation_method = c("pearson", "spearman"),
  min_trials_per_split = 2,
  name = "gazepoint_pupil_reliability"
)

Arguments

data: A data frame containing trial-level or window-level pupil outcomes.
outcome_cols: Character vector of pupil outcome columns. If NULL, common pupil outcome columns are detected automatically.
participant_col: Participant/subject column. If NULL, common participant columns are detected automatically.
trial_col: Trial/order column. If NULL, common trial columns are detected automatically when available. If no trial column is available, row order within participant is used.
split_col: Optional pre-existing split column. If supplied, it must have exactly two non-missing levels.
by_cols: Optional grouping columns for separate reliability audits, such as "condition" or "window".
split_method: Split method used when split_col = NULL. Options are "odd_even" and "first_second".
aggregate_function: Function used to aggregate trial-level values within participant and split. Options are "mean" and "median".
correlation_method: Correlation method for split-half association. Options are "pearson" and "spearman".
min_trials_per_split: Minimum number of non-missing outcome values required in each split for a participant to contribute to the reliability estimate.
name: Character label stored in the audit object.

Value

A list with class gp3_pupil_reliability_audit.