plotDistributionModel: Distribution of modelling output
In marcpaga/pulsedSilac: Analysis of pulsed-SILAC quantitative proteomics data

Description Usage Arguments Value Examples

Plot the distribution of the different model parameters and metrics for each condition.

plotDistributionModel(
  modelList,
  value = "param_values",
  plotType = "density",
  returnDataFrame = FALSE
)

`modelList`	A list containing all the model objects, this should be the output of `modelTurnover`.
`value`	A `character` indicating which metric to plot. Check `names(modelList)` for available options. (Default = 'param_values')
`plotType`	A `character` indicating which geometry to plot: 'boxplot' or 'density'. (default = 'density')
`returnDataFrame`	A `logical` indicating if the `data.frame` used for the plot should be returned instead.

A ggplot density or boxplot object, or the data.frame used to make the plot.

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)

plotDistributionModel(modelList = modelList,
                      value = 'param_values',
                      plotType = 'density')