Fit a Gazepoint Growth Curve Analysis mixed model — fit_gazepoint

Fit a Growth Curve Analysis (GCA) mixed model to prepared pupil time-course data. The function first attempts a random-intercept plus random-time-slopes model and, if the model fails or is singular, falls back to a random-intercept model.

Usage

fit_gazepoint_gca(
  data,
  outcome_col = "gca_pupil",
  subject_col = "subject",
  condition_col = "condition",
  time_terms = NULL,
  degree = NULL,
  weights_col = "gca_weight",
  use_weights = TRUE,
  random_slopes = TRUE,
  fallback_on_singular = TRUE,
  REML = FALSE,
  optimizer = "bobyqa",
  maxfun = 2e+05,
  drop_missing = TRUE
)

Arguments

data: A data frame created by prepare_gazepoint_gca_data().
outcome_col: Name of the GCA outcome column.
subject_col: Name of the subject column.
condition_col: Name of the condition column.
time_terms: Optional character vector of polynomial time-term columns. If NULL, terms named time_poly_1, time_poly_2, ... are detected.
degree: Optional number of polynomial terms to use. If supplied and time_terms = NULL, the function uses time_poly_1 through time_poly_degree.
weights_col: Optional weights column. Use NULL for unweighted models.
use_weights: Logical. If TRUE, uses weights_col when available.
random_slopes: Logical. If TRUE, first attempts random slopes for all polynomial time terms.
fallback_on_singular: Logical. If TRUE, falls back to a random-intercept model when the random-slope model is singular.
REML: Logical passed to lme4::lmer().
optimizer: Optimizer passed to lme4::lmerControl().
maxfun: Maximum optimizer function evaluations.
drop_missing: Logical. If TRUE, rows with missing model variables are removed before fitting.

Value

A list of class gp3_gca_model containing the fitted model, attempted and final formulas, model comparison information, settings, and status.