fit_gau: Fit Gaussian antedependence model by maximum likelihood

In antedep: Antedependence Models for Longitudinal Data

Fit Gaussian antedependence model by maximum likelihood

Description

Fits an AD(0), AD(1), or AD(2) model for Gaussian longitudinal data by maximum likelihood. Missing values can be handled by complete-case deletion or by EM (see em_gau for an explicit EM wrapper).

Usage

fit_gau(
  y,
  order = 1,
  blocks = NULL,
  na_action = c("fail", "complete", "em"),
  estimate_mu = TRUE,
  em_max_iter = 100,
  em_tol = 1e-06,
  em_verbose = FALSE,
  ...
)

Arguments

y	Numeric matrix (n_subjects x n_time). May contain NA.
order	Integer 0, 1, or 2.
blocks	Optional vector of block membership (length n_subjects).
na_action	One of "fail", "complete", or "em".
estimate_mu	Logical, whether to estimate mu (default TRUE).
em_max_iter	Maximum EM iterations (only used when na_action = "em").
em_tol	EM convergence tolerance (only used when na_action = "em").
em_verbose	Logical, print EM progress (only used when na_action = "em").
...	Passed through to the EM fitter.

Details

For missing data with na_action = "em", AD orders 0 and 1 are the primary production path. AD order 2 is available, but the current EM implementation uses simplified second-order updates and should be treated as provisional for high-stakes inference.

For observed-data likelihood evaluation under MAR without fitting, use logL_gau with na_action = "marginalize". In contrast, fit_gau handles missingness via complete-case fitting (na_action = "complete") or EM (na_action = "em").

Value

A list with components including mu, phi, sigma, tau, log_l, n_obs, n_missing.

See Also

em_gau, fit_cat, fit_inad

Examples

set.seed(1)
y <- simulate_gau(n_subjects = 30, n_time = 5, order = 1, phi = 0.3)
fit <- fit_gau(y, order = 1)
fit$log_l

y_miss <- y
y_miss[1, 2] <- NA
fit_em <- fit_gau(y_miss, order = 1, na_action = "em", em_max_iter = 20)
fit_em$settings$na_action

antedep documentation built on April 25, 2026, 1:06 a.m.