em_inad: EM algorithm for INAD model estimation

In antedep: Antedependence Models for Longitudinal Data

EM algorithm for INAD model estimation

Description

Fits INAD models using the Expectation-Maximization algorithm. This is an alternative to direct likelihood optimization.

Usage

em_inad(
  y,
  order = 1,
  thinning = "binom",
  innovation = "pois",
  blocks = NULL,
  max_iter = 200,
  tol = 1e-07,
  alpha_init = NULL,
  theta_init = NULL,
  tau_init = NULL,
  nb_inno_size = NULL,
  safeguard = TRUE,
  verbose = FALSE
)

Arguments

y	Integer matrix with n_subjects rows and n_time columns.
order	Model order (1 or 2). Order 0 does not require EM.
thinning	Thinning operator: "binom", "pois", or "nbinom".
innovation	Innovation distribution: "pois", "bell", or "nbinom".
blocks	Optional integer vector of length n_subjects for block effects.
max_iter	Maximum number of EM iterations.
tol	Convergence tolerance for log-likelihood change.
alpha_init	Optional initial values for alpha parameters.
theta_init	Optional initial values for theta parameters.
tau_init	Optional initial values for tau parameters.
nb_inno_size	Size parameter for negative binomial innovation (if used).
safeguard	Logical; if TRUE, use step-halving when likelihood decreases.
verbose	Logical; if TRUE, print iteration progress.

Details

For Gaussian and CAT EM entry points, see em_gau and em_cat. For CAT specifically, fit_cat() supports na_action = "em" for orders 0/1 and na_action = "marginalize" for order 2 missing-data fits.

Value

A list with class "inad_fit" containing estimated parameters.

See Also

em_gau, em_cat, fit_inad, fit_cat

Examples

set.seed(1)
y <- simulate_inad(
  n_subjects = 50,
  n_time = 5,
  order = 1,
  thinning = "binom",
  innovation = "pois",
  alpha = 0.25,
  theta = 2
)
fit <- em_inad(y, order = 1, thinning = "binom", innovation = "pois", max_iter = 20, tol = 1e-6)
fit$log_l

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