Prepare Gazepoint biometric summaries for mixed-model analysis — prepare_gazepoint_biometrics_lme

Prepares biometric window-level or event-level summaries for downstream mixed-model analysis. This helper does not fit a model. It checks variables, optionally baseline-corrects the selected outcome, optionally scales numeric predictors, converts grouping/factor variables, flags complete cases, and returns a conservative model formula.

Usage

prepare_gazepoint_biometrics_lme_data(
  data,
  outcome_col,
  fixed_effect_cols = NULL,
  condition_cols = NULL,
  covariate_cols = NULL,
  random_effect_cols = NULL,
  participant_col = NULL,
  stimulus_col = NULL,
  trial_col = NULL,
  window_col = NULL,
  baseline_col = NULL,
  baseline_correct = FALSE,
  factor_cols = NULL,
  continuous_cols = NULL,
  scale_continuous = FALSE,
  include_window = TRUE,
  drop_missing = TRUE,
  min_rows = 10
)

Arguments

data: A data frame containing biometric summary rows.
outcome_col: Name of the outcome column to analyse.
fixed_effect_cols: Optional fixed-effect predictor columns.
condition_cols: Optional condition/design columns to include as fixed effects.
covariate_cols: Optional covariate columns to include as fixed effects.
random_effect_cols: Optional grouping columns for random intercepts.
participant_col, stimulus_col, trial_col: Optional common grouping columns.
window_col: Optional analysis-window column. Included as a fixed effect when include_window = TRUE.
baseline_col: Optional baseline column.
baseline_correct: Logical. If TRUE, creates an outcome column equal to outcome_col - baseline_col.
factor_cols: Optional columns to convert to factors.
continuous_cols: Optional numeric predictor columns to scale when scale_continuous = TRUE.
scale_continuous: Logical. If TRUE, creates z-scored versions of numeric continuous predictors and uses those in the formula.
include_window: Logical. Should window_col be included as a fixed effect?
drop_missing: Logical. Should incomplete model rows be removed from model_data?
min_rows: Minimum number of complete rows required for a "ready" status.

Value

A list with overview, data, model_data, model_formula, variable_summary, and settings.