plotObsVsPred: Plot Observed versus Predicted Results in Regression and...

In caret: Classification and Regression Training

Plot Observed versus Predicted Results in Regression and Classification Models

Description

This function takes an object (preferably from the function extractPrediction) and creates a lattice plot. For numeric outcomes, the observed and predicted data are plotted with a 45 degree reference line and a smoothed fit. For factor outcomes, a dotplot plot is produced with the accuracies for the different models.

Usage

plotObsVsPred(object, equalRanges = TRUE, ...)

Arguments

object	an object (preferably from the function extractPrediction. There should be columns named obs, pred, model (e.g. "rpart", "nnet" etc.) and dataType (e.g. "Training", "Test" etc)
equalRanges	a logical; should the x- and y-axis ranges be the same?
...	parameters to pass to xyplot or dotplot, such as auto.key

Details

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

Value

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

Author(s)

Max Kuhn

Examples

## Not run: 
# regression example
data(BostonHousing)
rpartFit <- train(BostonHousing[1:100, -c(4, 14)], 
                  BostonHousing$medv[1:100], 
                  "rpart", tuneLength = 9)
plsFit <- train(BostonHousing[1:100, -c(4, 14)], 
                BostonHousing$medv[1:100], 
                "pls")

predVals <- extractPrediction(list(rpartFit, plsFit), 
                              testX = BostonHousing[101:200, -c(4, 14)], 
                              testY = BostonHousing$medv[101:200], 
                              unkX = BostonHousing[201:300, -c(4, 14)])

plotObsVsPred(predVals)


#classification example
data(Satellite)
numSamples <- dim(Satellite)[1]
set.seed(716)

varIndex <- 1:numSamples

trainSamples <- sample(varIndex, 150)

varIndex <- (1:numSamples)[-trainSamples]
testSamples <- sample(varIndex, 100)

varIndex <- (1:numSamples)[-c(testSamples, trainSamples)]
unkSamples <- sample(varIndex, 50)

trainX <- Satellite[trainSamples, -37]
trainY <- Satellite[trainSamples, 37]

testX <- Satellite[testSamples, -37]
testY <- Satellite[testSamples, 37]

unkX <- Satellite[unkSamples, -37]

knnFit  <- train(trainX, trainY, "knn")
rpartFit <- train(trainX, trainY, "rpart")

predTargets <- extractPrediction(list(knnFit, rpartFit), 
                                 testX = testX, 
                                 testY = testY, 
                                 unkX = unkX)

plotObsVsPred(predTargets)

## End(Not run)

caret documentation built on March 31, 2023, 9:49 p.m.