compareAIC {pulsedSilac} | R Documentation

## Calculates the probability of each model from a set of models.

### Description

For a given set of AIC from models, the probability of each model relative to the rest of the models of the set is calculated using the following formula:

### Usage

    compareAIC(...)

### Arguments

`...` | a `list` with the model metrics, the output from `modelTurnover` and `calculateAIC`.

### Value

a `list` with a matrix for each experiment condition. The matrix contains the probabilities of each model (columns) for each protein/peptide (rows).

### See Also

`calculateAIC`, `modelTurnover`.

### Examples

    data('wormsPE')
    wormsPE <- calculateIsotopeFraction(wormsPE, ratioAssay = 'ratio')

    modelList1 <- modelTurnover(x = wormsPE[1:10],
                               assayName = 'fraction',
                               formula = 'fraction ~ 1 - exp(-k*t)',
                               start = list(k = 0.02),
                               mode = 'protein',
                               robust = FALSE,
                               returnModel = TRUE)

    modelList1 <- calculateAIC(modelList1, smallSampleSize = TRUE)

    modelList2 <- modelTurnover(x = wormsPE[1:10],
                               assayName = 'fraction',
                               formula = 'fraction ~ 1 - exp(-k*t) + b',
                               start = list(k = 0.02, b = 0),
                               mode = 'protein',
                               robust = FALSE,
                               returnModel = TRUE)

    modelList2 <- calculateAIC(modelList2, smallSampleSize = TRUE)

    modelProbabilities <- compareAIC(modelList1, modelList2)

[Package pulsedSilac version 1.4.0 Index]