compareAIC: Calculates the probability of each model from a set of...
In marcpaga/pulsedSilac: Analysis of pulsed-SILAC quantitative proteomics data

Description Usage Arguments Value See Also Examples

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: $\prod AIC_{i} = \frac{exp(\frac{AIC_{min}-AIC_{i}}{2})}{\sum_{j}exp(\frac{AIC_{min}-AIC_{j}}{2})}$

1	compareAIC(...)

...

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

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

calculateAIC, modelTurnover.

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)