logL_gau: Log-likelihood for Gaussian AD models (with missing data...

In antedep: Antedependence Models for Longitudinal Data

Log-likelihood for Gaussian AD models (with missing data support)

Description

Computes the log-likelihood for Gaussian antedependence models of order 0, 1, or 2. Supports missing data under MAR assumption via na_action parameter.

Usage

logL_gau(
  y,
  order,
  mu = NULL,
  phi = NULL,
  sigma = NULL,
  blocks = NULL,
  tau = 0,
  na_action = c("fail", "complete", "marginalize")
)

Arguments

y	Numeric matrix with n_subjects rows and n_time columns. May contain NA.
order	Antedependence order, one of 0, 1, or 2.
mu	Mean vector (length n_time).
phi	Dependence coefficient(s). For order 1: vector of length n_time-1. For order 2: matrix with 2 columns or vector of length 2*(n_time-2).
sigma	Innovation standard deviations (length n_time).
blocks	Integer vector of block membership (length n_subjects), or NULL.
tau	Block effects, first element constrained to zero
na_action	How to handle missing values: fail: Error if any NA is present (default) complete: Use only complete cases marginalize: Compute observed-data likelihood

Details

For complete data (no NA), all three na_action options give the same result.

For missing data:

• marginalize: Uses MVN marginalization to compute P(Y_obs). This is the correct observed-data likelihood for MAR missing data.

• complete: Removes subjects with any missing values. May lose information.

• fail: Stops with error. Useful to ensure no missing data present.

Value

Scalar log-likelihood value.

Examples

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

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