forestPlot-ModelComparison-method: Render a forest plot from the 'validationStats' slot of a...
In bhklab/PDATK: Pancreatic Ductal Adenocarcinoma Tool-Kit

Description Usage Arguments Value Examples

Render a forest plot from the validationStats slot of a PCOSP model object.

## S4 method for signature 'ModelComparison'
forestPlot(
  object,
  stat,
  groupBy = "cohort",
  colourBy = "model",
  vline,
  ...,
  xlab,
  ylab,
  transform,
  colours,
  title
)

`object`	A `ModelComparison` object to forest plot.
`stat`	A `character` vector specifying a statistic to plot.
`groupBy`	A `character` vector with one or more columns in `validationStats` to group by. These will be the facets in your forestplot.
`colourBy`	A `character` vector specifying the columns in `validationStats` to colour by.
`vline`	An `integer` value on the x-axis to place a dotted vertical line.
`...`	Force subsequent parameters to be named, not used.
`xlab`	A `character` vector specifying the desired x label. Automatically guesses based on the `stat` argument.
`ylab`	A `character` vector specifying the desired y label. Defaults to 'Cohort (P-value)'.
`transform`	The name of a numeric function to transform the statistic before making the forest plot.
`colours`	A `character` vector of colours to pass into `ggplot2::scale_fill_manual`, which modify the colourBy argument.
`title`	A `character` vector with a title to add to the plot.

A ggplot2 object.

data(sampleValPCOSPmodel)
data(sampleClinicalModel)
data(sampleCohortList)

# Set parallelization settings
BiocParallel::register(BiocParallel::SerialParam())

# Train the models
trainedClinicalModel <- trainModel(sampleClinicalModel)

# Predict risk/risk-class
ClinicalPredCohortL <- predictClasses(sampleCohortList[c('PCSI', 'TCGA')],
  model=trainedClinicalModel)

# Validate the models
validatedClinicalModel <- validateModel(trainedClinicalModel,
  valData=ClinicalPredCohortL)

# Compare the models
modelComp <- compareModels(sampleValPCOSPmodel, validatedClinicalModel)

# Make the forest plot
modelComp <- modelComp[modelComp$isSummary == TRUE, ]
modelCindexCompForestPlot <- forestPlot(modelComp, stat='concordance_index')