AIC.fcm: Akaike Information Criterion (AIC) for fcm objects

In eFCM: Exponential Factor Copula Model

Akaike Information Criterion (AIC) for fcm objects

Description

Compute the AIC value for a fitted fcm model using the formula:

\mathrm{AIC} = -2 \cdot \log L + k \cdot p

where L is the likelihood, p is the number of parameters, and k is a penalty parameter.

Usage

## S3 method for class 'fcm'
AIC(object, ..., k = 2)

Arguments

object	An object of class fcm, created by fcm().
...	Currently unused.
k	Penalty per parameter (default is k = 2).

Value

A numeric scalar giving the AIC value for the fitted model.

See Also

logLik.fcm(), BIC.fcm(), AICc.fcm()

eFCM documentation built on Sept. 9, 2025, 5:52 p.m.