# AIC: Information Criteria for averaging models In rAverage: Parameter Estimation for the Averaging Model of Information Integration Theory

## Description

Functions to extract or recalculate the Akaike Information Criterion and the Bayesian Information Criterion of an averaging model fitted by the rav function.

## Usage

 1 2 AIC(object, ..., k = 2) BIC(object, ...) 

## Arguments

 object An object of class rav containing an estimated averaging model. ... Optionally more fitted model objects (see details). k Numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC.

## Details

The functions AIC and BIC are used, respectively, to extract the Akaike Information Criterion and the Bayesian Information Criterion of a model fitted by the function rav.

AIC is calculated as:

AIC = n \ln ≤ft( \frac{RSS}{n} \right) + k p

where n is the number of data available, k is the penalty per parameter ()usually equal to 2), p is the number of parameters and RSS is the residual sum of squares.

BIC is calculated as:

BIC = n \ln ≤ft( \frac{RSS}{n} \right) + \ln(n) p

As default, when n / p < 40, AIC and BIC are corrected in AICc and BICc:

AICc = AIC + \frac{2 (p+1) p}{n-p-1}

BICc = BIC + \frac{\ln(n) (p+1) p}{n-p-1}

to avoid the correction, set correct = FALSE. On the contrary, if you want the correction, set correct = TRUE. When the argument correct is not specified, the rule n / p < 40 is applied.

As default, the functions extract the indices of the (first) best model. The optional argument whichModel can be specified to extract the indices of another model. Options are:

1. "null": null model

2. "ESM": equal scale values model

3. "SAM": simple averaging model

4. "EAM": equal-weights averaging model

5. "DAM": differential-weight averaging model

6. "IC": information criteria model

## Value

A numeric value representing the information criterion of the selected model.

rav, rAverage-package AIC, BIC
 1 2 3 4 5 6 7 ## Not run: data(fmdata1) fm1 <- rav(fmdata1, lev=c(3,3)) AIC(fm1) BIC(fm1) ## End(Not run)