Prepare pupil-window data for confirmatory mixed models — prepare_gazepoint_pupil_window_model

Prepare pupil-window summaries or pupil trial-feature tables for confirmatory window-level modelling. The function standardises subject, condition, window, trial/media identifiers, outcome, valid-sample counts, total-sample counts, valid-sample proportions, weights, and model-readiness status columns.

Usage

prepare_gazepoint_pupil_window_model_data(
  data,
  outcome_col = "mean_pupil",
  subject_col = "subject",
  condition_col = "condition",
  window_col = "window_label",
  window_start_col = "window_start_ms",
  window_end_col = "window_end_ms",
  trial_col = NULL,
  media_col = "media_id",
  valid_samples_col = "n_valid_pupil",
  total_samples_col = "n_samples",
  min_valid_samples = 5,
  min_valid_prop = 0.7,
  drop_invalid = TRUE,
  missing_condition_label = "all_data",
  outcome_label = "pupil"
)

Arguments

data: Pupil-window summary data.
outcome_col: Column containing the pupil outcome to model. The default is mean_pupil.
subject_col: Subject/participant column.
condition_col: Optional condition column. Common aliases such as condition, Condition, and CONDITION are detected when available.
window_col: Pupil-window label column.
window_start_col: Optional window-start column.
window_end_col: Optional window-end column.
trial_col: Optional trial identifier column.
media_col: Optional media/stimulus identifier column. Common aliases such as media_id and MEDIA_ID are detected when available.
valid_samples_col: Optional column containing the number of valid pupil samples in the window. Common aliases such as n_valid_pupil and n_valid_samples are detected when available.
total_samples_col: Optional column containing the total number of samples in the window. Common aliases such as n_samples and n_window_samples are detected when available.
min_valid_samples: Minimum acceptable number of valid pupil samples.
min_valid_prop: Minimum acceptable valid-sample proportion.
drop_invalid: Logical. If TRUE, rows with invalid or low-quality model inputs are removed.
missing_condition_label: Label used when condition is missing.
outcome_label: Label stored in the output to identify the modelled pupil outcome.

Value

A tibble of pupil-window rows prepared for confirmatory modelling.