# calculateAIC: Calculates the Akaike Information Criteria (AIC) In pulsedSilac: Analysis of pulsed-SILAC quantitative proteomics data

## Description

Calculates the AIC for each of the computed models. Requires that modelTurnover is run with reuturnModel = TRUE.

## Usage

 1 calculateAIC(modelList, smallSampleSize = TRUE) 

## Arguments

 modelList a list with the model metrics, the output from modelTurnover. smallSampleSize a logical indicating if the AIC small sample size correction formula should be used.

## Details

The following formulas are used to compute the AIC and AICc (small sample size correction):

$\inline&space;AIC&space;=&space;2k&space;-&space;2ln(logLik)$ $\inline&space;AICc&space;=&space;AIC&space;+&space;\frac{2k(k&space;+&space;1)}{n&space;-&space;k&space;-&space;1}$

## Value

a list with the model metrics (the given input) plus a matrix named "AIC" with the AIC for each value

compareAIC, modelTurnover
  1 2 3 4 5 6 7 8 9 10 11 12 data('wormsPE') wormsPE <- calculateIsotopeFraction(wormsPE, ratioAssay = 'ratio') modelList <- modelTurnover(x = wormsPE[1:10], assayName = 'fraction', formula = 'fraction ~ 1 - exp(-k*t)', start = list(k = 0.02), mode = 'protein', robust = FALSE, returnModel = TRUE) modelList <- calculateAIC(modelList, smallSampleSize = TRUE)