calculateAIC {pulsedSilac} | R Documentation

## Calculates the Akaike Information Criteria (AIC)

### Description

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

### Usage

    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):

### Value

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

### See Also

`compareAIC`, `modelTurnover`

### Examples

    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)

[Package pulsedSilac version 1.9.1 Index]