Skip to contents

Bayesian information criterion for fitted categorical AD models

Source: R/bic_cat.R
bic_cat.Rd

Computes BIC using the fitted log likelihood and a parameter count for categorical antedependence parameters.

Usage

bic_cat(fit, n_subjects = NULL)

Arguments

fit

A fitted model object of class "cat_fit" returned by fit_cat.

n_subjects

Number of subjects. If NULL, extracted from fit.

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.

Examples

set.seed(1)
y <- simulate_cat(40, 5, order = 1, n_categories = 2)

# Fit models of different orders
fit0 <- fit_cat(y, order = 0)
fit1 <- fit_cat(y, order = 1)
fit2 <- fit_cat(y, order = 2)

# Compare BIC
c(BIC_0 = bic_cat(fit0), BIC_1 = bic_cat(fit1), BIC_2 = bic_cat(fit2))
#>    BIC_0    BIC_1    BIC_2 
#> 290.2363 303.7537 308.9566