Bayesian information criterion for fitted Gaussian AD models

Computes BIC using the fitted log likelihood and a parameter count that respects identifiability constraints for the Gaussian antedependence parameters.

Usage

bic_gau(fit, n_subjects = NULL)

Arguments

fit

A fitted model object returned by fit_gau.

n_subjects

Number of subjects, typically nrow(y). If NULL, inferred from fit$settings$n_subjects.

Value

A numeric scalar BIC value.

Details

The BIC is computed as: $$BIC = -2 \times \ell + k \times \log(N)$$ where \(\ell\) is the log-likelihood, \(k\) is the number of free parameters, and \(N\) is the number of subjects.

This function applies to Gaussian AD fits from fit_gau. For categorical and INAD models, use bic_cat and bic_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)
bic_gau(fit, n_subjects = nrow(y))
#> [1] 428.4076