Calculate Bayesian Information Criterion (BIC) for a 'colsa' Object

This function computes the Bayesian Information Criterion (BIC) for a fitted 'colsa' model object. The BIC is a model selection criterion that balances model fit and complexity.

Usage

# S3 method for class 'colsa'
BIC(object, ...)

Arguments

object

An object of class 'colsa'. This is the fitted model for which the BIC is to be calculated.

...

Additional arguments (currently not used).

Value

A numeric value representing the BIC of the model.

Details

The BIC is calculated as: $$-2 \times \log(\text{Likelihood}) + k \times \log(n)$$ where \(k\) is the number of parameters in the model, and \(n\) is the number of samples in the dataset.

See also

logLik.colsa, AIC.colsa