fit_inad: Fit INAD antedependence model by maximum likelihood

In antedep: Antedependence Models for Longitudinal Data

Fit INAD antedependence model by maximum likelihood

Description

Fits INAD models by maximum likelihood.

Usage

fit_inad(
  y,
  order = 1,
  thinning = c("binom", "pois", "nbinom"),
  innovation = c("pois", "bell", "nbinom"),
  blocks = NULL,
  max_iter = 50,
  tol = 1e-06,
  verbose = FALSE,
  init_alpha = NULL,
  init_theta = NULL,
  init_tau = 0.4,
  init_nb_inno_size = 1,
  nb_inno_size_ub = 50,
  na_action = c("fail", "complete", "marginalize")
)

Arguments

y	Integer matrix n_sub by n_time.
order	Integer in {0, 1, 2}.
thinning	One of "binom", "pois", "nbinom".
innovation	One of "pois", "bell", "nbinom".
blocks	Optional integer vector length n_sub. Default NULL.
max_iter	Max iterations for FE coordinate descent.
tol	Tolerance for FE log likelihood stopping.
verbose	Logical.
init_alpha	Optional initial alpha. For order 1 numeric length 1 or n_time. For order 2 matrix n_time by 2 or list(alpha1, alpha2).
init_theta	Optional initial theta numeric length 1 or n_time.
init_tau	Optional initial tau. Scalar expands to c(0, x, ..., x). Vector forces first to 0.
init_nb_inno_size	Optional initial size for innovation nbinom, length 1 or n_time.
nb_inno_size_ub	Upper bound for innovation negative binomial size parameter when innovation = "nbinom". Default is 50.
na_action	How to handle missing values: "fail": stop if any NA is present. "complete": fit using complete-case subjects only. "marginalize": maximize observed-data likelihood under MAR.

Details

No fixed effect: time separable optimization using logL_inad_i with theta eliminated by moment equations for order 1 and 2.

Fixed effect: block coordinate descent using nloptr BOBYQA, updating tau, alpha, theta, and nb_inno_size if needed.

Value

A list of class "inad_fit" containing:

alpha

Estimated antedependence parameter(s)

theta

Estimated innovation parameter(s)

tau

Estimated block effects (if applicable)

nb_inno_size

Estimated innovation NB size parameter(s), when innovation = "nbinom"

log_l

Maximized log-likelihood

aic

Akaike information criterion

bic

Bayesian information criterion

n_params

Number of free parameters

convergence

Convergence code

settings

Model and fitting settings

Examples

set.seed(1)
y <- simulate_inad(
  n_subjects = 60,
  n_time = 5,
  order = 1,
  thinning = "binom",
  innovation = "pois",
  alpha = 0.3,
  theta = 2
)
fit <- fit_inad(y, order = 1, thinning = "binom", innovation = "pois", max_iter = 20)
fit$log_l

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