AIC.oglmx: Calculate Akaike Information Criterion

In oglmx: Estimation of Ordered Generalized Linear Models

Description

Calculates the Akaike Information Criterion for objects of class oglmx. Calculate using the formula -2*loglikelihood + k*npar where npar represents the number of parameters in the model and k is the cost of additional parameters, equal to 2 for the AIC, it is k=\log(n) with n the number of observations for the BIC.

Usage

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

Arguments

object	object of class oglmx
...	additional arguments. Currently ignored.
k	the penalty per parameter to be used.

Details

When comparing models by maximium likelihood estimation the smaller the value of the AIC the better.

Value

A numeric value with the AIC.

Author(s)

Nathan Carroll, [e-mail]

See Also

AIC.

oglmx documentation built on May 2, 2019, 5:14 a.m.