extractAIC.glc: extractAIC method for class 'glc', 'gqc', 'gcjc', and 'grg'

In grt: General Recognition Theory

Description

Extract Akaike's An Information Criteria from a General Linear, Quadratic, or Conjunctive Classifier, or a General Random Guessing model

Usage

## S3 method for class 'glc'
extractAIC(fit, scale, k = 2, ...)

## S3 method for class 'gqc'
extractAIC(fit, scale, k = 2, ...)

## S3 method for class 'gcjc'
extractAIC(fit, scale, k = 2, ...)

## S3 method for class 'grg'
extractAIC(fit, scale, k = 2, ...)

Arguments

fit	object of class glc, gqc, gcjc, or grg
scale	unused argument
k	numeric specifying the penalty per parameter to be used in calculating AIC. Default to 2.
...	further arguments (currently not used).

Details

As with the default method, the criterion used is

AIC = - 2*log L + k * df,

where L is the likelihood and df is the degrees of freedom (i.e., the number of free parameters) of fit.

Value

A numeric vector of length 2 including:

df	the degrees of freedom for the fitted model fit.
AIC	the Akaike's Information Criterion for fit.

Examples

data(subjdemo_2d)
#fit a 2d suboptimal model
fit.2dl <- glc(response ~ x + y, data=subjdemo_2d, 
    category=subjdemo_2d$category, zlimit=7)
extractAIC(fit.2dl)

grt documentation built on May 2, 2019, 7:10 a.m.