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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plotObsVsPred.R
\name{plotObsVsPred}
\alias{plotObsVsPred}
\title{Plot Observed versus Predicted Results in Regression and Classification
Models}
\usage{
plotObsVsPred(object, equalRanges = TRUE, ...)
}
\arguments{
\item{object}{an object (preferably from the function
\code{\link{extractPrediction}}. There should be columns named \code{obs},
\code{pred}, \code{model} (e.g. "rpart", "nnet" etc.) and \code{dataType}
(e.g. "Training", "Test" etc)}
\item{equalRanges}{a logical; should the x- and y-axis ranges be the same?}
\item{\dots}{parameters to pass to \code{\link[lattice]{xyplot}} or
\code{\link[lattice:xyplot]{dotplot}}, such as \code{auto.key}}
}
\value{
A lattice object. Note that the plot has to be printed to be
displayed (especially in a loop).
}
\description{
This function takes an object (preferably from the function
\code{\link{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.
}
\details{
If the call to \code{\link{extractPrediction}} included test data, these
data are shown, but if unknowns were also included, they are not plotted
}
\examples{
\dontrun{
# 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)
}
}
\author{
Max Kuhn
}
\keyword{hplot}
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