# extractAIC.glc: extractAIC method for class 'glc', 'gqc', 'gcjc', and 'grg' In matsukik/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

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```## 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

 ```1 2 3 4 5``` ```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) ```

matsukik/grt documentation built on May 21, 2019, 12:57 p.m.