bic_order_cat: BIC-based order selection for categorical AD models

In antedep: Antedependence Models for Longitudinal Data

BIC-based order selection for categorical AD models

Description

Fits AD models of increasing orders and selects the best by BIC.

Usage

bic_order_cat(
  y,
  max_order = 2,
  blocks = NULL,
  homogeneous = TRUE,
  n_categories = NULL,
  criterion = "bic"
)

Arguments

y	Integer matrix of categorical data (n_subjects x n_time).
max_order	Maximum order to consider. Default is 2.
blocks	Optional block membership vector.
homogeneous	Whether to use homogeneous parameters across blocks.
n_categories	Number of categories (inferred if NULL).
criterion	Which criterion to use: "bic" (default) or "aic".

Value

A list containing:

table	Data frame with order, log_l, n_params, aic, bic
bic	Named numeric vector of BIC values by order
best_order	Order with lowest criterion value
criterion	Criterion used for order selection ("bic" or "aic")
fits	List of fitted models

Examples

y <- simulate_cat(100, 5, order = 1, n_categories = 2)
result <- bic_order_cat(y, max_order = 2)
print(result$table)
print(result$best_order)

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