Prepare Gazepoint time-course data for cluster permutation testing — prepare_gazepoint_timecourse_test

Prepares a participant-level, condition-level time-course data set for a conservative two-condition cluster-based permutation prototype. The helper aggregates repeated observations to one value per participant, condition, and time bin, then checks that the resulting data form a complete within-subject time grid.

Usage

prepare_gazepoint_timecourse_test_data(
  data,
  outcome_col,
  time_col,
  condition_col,
  participant_col,
  condition_a = NULL,
  condition_b = NULL,
  time_bin_width = NULL,
  aggregation = c("mean", "median"),
  require_complete = TRUE
)

Arguments

data: A data frame.
outcome_col: Name of the numeric outcome column.
time_col: Name of the numeric time column.
condition_col: Name of the condition column.
participant_col: Name of the participant identifier column.
condition_a: Optional first condition level.
condition_b: Optional second condition level.
time_bin_width: Optional numeric time-bin width. If supplied, time is binned using floor(time / time_bin_width) * time_bin_width.
aggregation: Aggregation rule for repeated rows within participant, condition, and time. Currently "mean" or "median".
require_complete: Logical. If TRUE, require a complete participant by condition by time grid.

Value

A data frame with columns participant, condition, time, and value, with class gazepoint_timecourse_test_data.

Details

This function is intended for exploratory time-course inference on already preprocessed Gazepoint-derived signals. It does not perform blink correction, artefact correction, baseline correction, filtering, or physiological interpretation.