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

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

Usage

 1 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).

calculateAIC, modelTurnover.
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 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)