Fit Gaussian antedependence model by maximum likelihood

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.

Value

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

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").

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
#> [1] -190.3954

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
#> NULL