caret (version 6.0-90)

plotClassProbs: Plot Predicted Probabilities in Classification Models

Description

This function takes an object (preferably from the function extractProb) and creates a lattice plot.

Usage

plotClassProbs(object, plotType = "histogram", useObjects = FALSE, ...)

Arguments

object

an object (preferably from the function extractProb. There should be columns for each level of the class factor and columns named obs, pred, model (e.g. "rpart", "nnet" etc), dataType (e.g. "Training", "Test" etc) and optionally objects (for giving names to objects with the same model type).

plotType

either "histogram" or "densityplot"

useObjects

a logical; should the object name (if any) be used as a conditioning variable?

…

parameters to pass to histogram or densityplot

Value

A lattice object. Note that the plot has to be printed to be displayed (especially in a loop).

Details

If the call to extractProb included test data, these data are shown, but if unknowns were also included, these are not plotted

Examples

# NOT RUN {
# }
# NOT RUN {
data(mdrr)
set.seed(90)
inTrain <- createDataPartition(mdrrClass, p = .5)[[1]]

trainData <- mdrrDescr[inTrain,1:20]
testData <- mdrrDescr[-inTrain,1:20]

trainY <- mdrrClass[inTrain]
testY <- mdrrClass[-inTrain]

ctrl <- trainControl(method = "cv")

nbFit1 <- train(trainData, trainY, "nb",
                trControl = ctrl,
                tuneGrid = data.frame(usekernel = TRUE, fL = 0))

nbFit2 <- train(trainData, trainY, "nb",
                trControl = ctrl,
                tuneGrid = data.frame(usekernel = FALSE, fL = 0))


models <- list(para = nbFit2, nonpara = nbFit1)

predProbs <- extractProb(models, testX = testData,  testY = testY)

plotClassProbs(predProbs, useObjects = TRUE)
plotClassProbs(predProbs,
               subset = object == "para" & dataType == "Test")
plotClassProbs(predProbs,
               useObjects = TRUE,
               plotType = "densityplot",
               auto.key = list(columns = 2))
# }
# NOT RUN {


# }