Fit confirmatory pupil-window linear mixed models — fit_gazepoint_pupil_window

Fit the main confirmatory trial/window-level pupil model from data prepared with prepare_gazepoint_pupil_window_model_data(). The default model is a linear mixed model with pupil outcome as the continuous dependent variable, condition and/or window fixed effects when available, and a subject random intercept when feasible.

Usage

fit_gazepoint_pupil_window_lmm(
  data,
  formula = NULL,
  outcome_col = "pupil_model_outcome",
  subject_col = "pupil_model_subject",
  condition_col = "pupil_model_condition",
  window_col = "pupil_model_window",
  weights_col = "pupil_model_weight",
  use_weights = FALSE,
  include_condition = TRUE,
  include_window = TRUE,
  include_interaction = TRUE,
  random_intercept = TRUE,
  random_window_slopes = FALSE,
  fallback_on_singular = TRUE,
  REML = FALSE,
  optimizer = "bobyqa",
  maxfun = 2e+05,
  drop_missing = TRUE,
  ...
)

Arguments

data: Pupil-window model data, usually produced by prepare_gazepoint_pupil_window_model_data().
formula: Optional model formula. If NULL, a formula is constructed automatically.
outcome_col: Outcome column.
subject_col: Subject column.
condition_col: Condition column.
window_col: Window column.
weights_col: Optional weights column.
use_weights: Logical. If TRUE, use weights_col as model weights.
include_condition: Logical. Include condition fixed effects when more than one condition level is available.
include_window: Logical. Include window fixed effects when more than one window level is available.
include_interaction: Logical. Include the condition-by-window interaction when both condition and window are used.
random_intercept: Logical. Include a subject random intercept when feasible.
random_window_slopes: Logical. Attempt subject-level random window slopes when feasible.
fallback_on_singular: Logical. If TRUE, fall back from a random-slope model to a random-intercept model when the attempted model is singular or fails.
REML: Logical. Passed to lme4::lmer().
optimizer: Optimizer passed to lme4::lmerControl().
maxfun: Maximum optimizer iterations passed to lme4::lmerControl().
drop_missing: Logical. If TRUE, rows with missing or non-finite model inputs are removed before fitting.
...: Additional arguments passed to lme4::lmer() or stats::lm().

Value

A list containing the fitted model, formula, attempted model, fallback information, fixed effects, comparison table, settings, and model diagnostics.