plotClassProbs function

Plot Predicted Probabilities in Classification Models

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

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

Returns

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:

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))
## End(Not run)

Author(s)

Max Kuhn