grt (version 0.2.1)

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

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 glc
extractAIC(fit, scale, k = 2, …)
# S3 method for gqc
extractAIC(fit, scale, k = 2, …)
# S3 method for gcjc
extractAIC(fit, scale, k = 2, …)
# S3 method for 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).

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.

Details

As with the default method, the criterion used is $$AIC = - 2\log L + k \times \mbox{df},$$ where \(L\) is the likelihood and \(df\) is the degrees of freedom (i.e., the number of free parameters) of fit.

Examples

# NOT RUN {
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)
# }