plotObsVsPred {caret} | R Documentation |
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.
If the call to extractPrediction
included test data, these data are shown, but
if unknowns were also included, they are not plotted
plotObsVsPred(object, equalRanges = TRUE, ...)
object |
an object (preferably from the function |
equalRanges |
a logical; should the x- and y-axis ranges be the same? |
... |
A lattice object. Note that the plot has to be printed to be displayed (especially in a loop).
Max Kuhn
## 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)