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\name{cvMclustDA}
\alias{cvMclustDA}
\title{MclustDA cross-validation}
\description{
V-fold cross-validation for classification models based on Gaussian
finite mixture modelling.
}
\usage{
cvMclustDA(object, nfold = 10,
prop = object$prop,
verbose = interactive(),
\dots)
}
\arguments{
\item{object}{
An object of class \code{'MclustDA'} resulting from a call to
\code{\link{MclustDA}}.
}
\item{nfold}{
An integer specifying the number of folds (by defaul 10-fold CV is
used).
}
\item{prop}{
A vector of class prior probabilities, which if not provided default
to the class proportions in the training data.
}
\item{verbose}{
A logical controlling if a text progress bar is displayed during
the cross-validation procedure. By default is \code{TRUE} if the
session is interactive, and \code{FALSE} otherwise.
}
\item{\dots }{Further arguments passed to or from other methods.}
}
\details{
The function implements V-fold cross-validation for classification
models fitted by \code{\link{MclustDA}}.
Classification error and Brier score are the metrics returned, but other
metrics can be computed using the output returned by this function
(see Examples section below).
}
\value{
The function returns a list with the following components:
\item{classification}{a factor of cross-validated class labels.}
\item{z}{a matrix containing the cross-validated probabilites for class assignment.}
\item{ce}{the cross-validation classification error.}
\item{se.ce}{the standard error of the cross-validated classification error.}
\item{brier}{the cross-validation Brier score.}
\item{se.brier}{the standard error of the cross-validated Brier score.}
}
\author{Luca Scrucca}
\seealso{
\code{\link{MclustDA}},
\code{\link{predict.MclustDA}},
\code{\link{classError}},
\code{\link{BrierScore}}
}
\examples{
\donttest{
# Iris data
Class <- iris$Species
X <- iris[,1:4]
## EDDA model with common covariance (essentially equivalent to linear discriminant analysis)
irisEDDA <- MclustDA(X, Class, modelType = "EDDA", modelNames = "EEE")
cv <- cvMclustDA(irisEDDA) # 10-fold CV (default)
str(cv)
cv <- cvMclustDA(irisEDDA, nfold = length(Class)) # LOO-CV
str(cv)
## MclustDA model selected by BIC
irisMclustDA <- MclustDA(X, Class)
cv <- cvMclustDA(irisMclustDA) # 10-fold CV (default)
str(cv)
# Banknote data
data("banknote")
Class <- banknote$Status
X <- banknote[,2:7]
## EDDA model selected by BIC
banknoteEDDA <- MclustDA(X, Class, modelType = "EDDA")
cv <- cvMclustDA(banknoteEDDA) # 10-fold CV (default)
str(cv)
(ConfusionMatrix <- table(Pred = cv$classification, Class))
TP <- ConfusionMatrix[1,1]
FP <- ConfusionMatrix[1,2]
FN <- ConfusionMatrix[2,1]
TN <- ConfusionMatrix[2,2]
(Sensitivity <- TP/(TP+FN))
(Specificity <- TN/(FP+TN))
}
}
\keyword{multivariate}
|
