# AICf: Calculate AIC, Akaike's Information Criterion In ZeBook: Working with Dynamic Models for Agriculture and Environment

## Description

This function calculate AIC criterion given a vector of observation, a vector of prediction and number of parameter. Note that number of parameters should include variance. AICcomplete is the same calculation of the AIC function of R (AICcomplete = n*log(RSS/n)+n+n*log(2*pi)+2*p, with p including variance). AICshort is the calculation described in chapter 6 Working with crop model (AICshort =n*log(RSS/n)+2*p, with p including variance). difference between AICcomplete and AICshort is AICcomplete-AICshort=n+n*log(2*pi) As you use AIC to compare models (with different number of parameters) on a same data (with same n, number of observation), you can use AICshort or AICcomplete.

## Usage

 `1` ```AICf(Yobs, Ypred, npar) ```

## Arguments

 `Yobs` : observed values `Ypred` : prediction values from the model `npar` : number of parameters (should include variance that count for one supplementary parameter)

## Value

a vector with AICcomplete and AICshort

## Examples

 ```1 2 3 4 5``` ```x=c(1,2,3,4,5) y=c(1.2,1.8,3.5,4.3,5.5) fit = lm(y~x) AIC(fit) AICf(y,predict(fit),3) # 3 parameters : intercept, slope and variance ```

ZeBook documentation built on March 18, 2018, 2:30 p.m.