Audit Gazepoint experimental design balance — audit_gazepoint_design

Create a publication-level audit of observed design balance across subjects, conditions, and optional stimulus/trial identifiers.

Usage

audit_gazepoint_design_balance(
  data,
  subject_col = "subject",
  condition_col = "condition",
  unit_cols = c("media_id", "trial_global"),
  expected_conditions = NULL,
  min_units_per_condition = 1L,
  max_condition_ratio = 2,
  require_all_conditions_per_subject = TRUE
)

Arguments

data: A data frame containing trial-level, window-level, or sample-level Gazepoint-derived data.
subject_col: Subject/participant identifier column.
condition_col: Experimental condition column.
unit_cols: Optional columns defining the repeated unit to count within each subject and condition, such as media, trial, block, or window.
expected_conditions: Optional character vector of expected condition labels.
min_units_per_condition: Minimum number of observed units expected per subject-condition cell.
max_condition_ratio: Maximum allowed ratio between a subject's largest and smallest non-zero condition counts.
require_all_conditions_per_subject: Logical. If TRUE, flag subjects who do not have all expected or observed conditions.

Value

A list with class gp3_design_balance_audit containing overview, subject_summary, condition_summary, cell_summary, imbalance_summary, flagged_cells, and settings tables.