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\name{plot.MclustDA}
\alias{plot.MclustDA}
\title{Plotting method for MclustDA discriminant analysis}
\description{
Plots for model-based mixture discriminant analysis results, such as scatterplot of training and test data, classification of train and test data, and errors.
}
\usage{
\method{plot}{MclustDA}(x, what = c("scatterplot", "classification", "train&test", "error"),
newdata, newclass, dimens = NULL,
symbols, colors, main = NULL, \dots)
}
\arguments{
\item{x}{
An object of class \code{'MclustDA'} resulting from a call to \code{\link{MclustDA}}.
}
\item{what}{
A string specifying the type of graph requested. Available choices are:
\describe{
\item{\code{"scatterplot"} =}{a plot of training data with points marked based on the known classification. Ellipses corresponding to covariances of mixture components are also drawn.}
\item{\code{"classification"} =}{a plot of data with points marked on based the predicted classification; if \code{newdata} is provided then the test set is shown otherwise the training set.}
\item{\code{"train&test"} =}{a plot of training and test data with points marked according to the type of set.}
\item{\code{"error"} =}{a plot of training set (or test set if \code{newdata} and \code{newclass} are provided) with misclassified points marked.}
}
If not specified, in interactive sessions a menu of choices is proposed.
}
\item{newdata}{
A data frame or matrix for test data.
}
\item{newclass}{
A vector giving the class labels for the observations in
the test data (if known).
}
\item{dimens}{
A vector of integers giving the dimensions of the desired coordinate
projections for multivariate data. The default is to take all the
the available dimensions for plotting.
}
\item{symbols}{
Either an integer or character vector assigning a plotting symbol to each
unique class. Elements in \code{colors} correspond to classes in order of
appearance in the sequence of observations (the order used by the
function \code{factor}).
The default is given by \code{mclust.options("classPlotSymbols")}.
}
\item{colors}{
Either an integer or character vector assigning a color to each
unique class in \code{classification}. Elements in \code{colors}
correspond to classes in order of appearance in the sequence of
observations (the order used by the function \code{factor}).
The default is given by \code{mclust.options("classPlotColors")}.
}
\item{main}{
A logical, a character string, or \code{NULL} (default) for the main title.
If \code{NULL} or \code{FALSE} no title is added to a plot.
If \code{TRUE} a default title is added identifying the type of plot drawn.
If a character string is provided, this is used for the title.
}
\item{\dots}{further arguments passed to or from other methods.}
}
%\value{}
\details{
For more flexibility in plotting, use \code{mclust1Dplot},
\code{mclust2Dplot}, \code{surfacePlot}, \code{coordProj}, or
\code{randProj}.
}
\author{Luca Scrucca}
\seealso{
\code{\link{MclustDA}},
\code{\link{surfacePlot}},
\code{\link{coordProj}},
\code{\link{randProj}}
}
\examples{
\donttest{
odd <- seq(from = 1, to = nrow(iris), by = 2)
even <- odd + 1
X.train <- iris[odd,-5]
Class.train <- iris[odd,5]
X.test <- iris[even,-5]
Class.test <- iris[even,5]
# common EEE covariance structure (which is essentially equivalent to linear discriminant analysis)
irisMclustDA <- MclustDA(X.train, Class.train, modelType = "EDDA", modelNames = "EEE")
summary(irisMclustDA, parameters = TRUE)
summary(irisMclustDA, newdata = X.test, newclass = Class.test)
# common covariance structure selected by BIC
irisMclustDA <- MclustDA(X.train, Class.train, modelType = "EDDA")
summary(irisMclustDA, parameters = TRUE)
summary(irisMclustDA, newdata = X.test, newclass = Class.test)
# general covariance structure selected by BIC
irisMclustDA <- MclustDA(X.train, Class.train)
summary(irisMclustDA, parameters = TRUE)
summary(irisMclustDA, newdata = X.test, newclass = Class.test)
plot(irisMclustDA)
plot(irisMclustDA, dimens = 3:4)
plot(irisMclustDA, dimens = 4)
plot(irisMclustDA, what = "classification")
plot(irisMclustDA, what = "classification", newdata = X.test)
plot(irisMclustDA, what = "classification", dimens = 3:4)
plot(irisMclustDA, what = "classification", newdata = X.test, dimens = 3:4)
plot(irisMclustDA, what = "classification", dimens = 4)
plot(irisMclustDA, what = "classification", dimens = 4, newdata = X.test)
plot(irisMclustDA, what = "train&test", newdata = X.test)
plot(irisMclustDA, what = "train&test", newdata = X.test, dimens = 3:4)
plot(irisMclustDA, what = "train&test", newdata = X.test, dimens = 4)
plot(irisMclustDA, what = "error")
plot(irisMclustDA, what = "error", dimens = 3:4)
plot(irisMclustDA, what = "error", dimens = 4)
plot(irisMclustDA, what = "error", newdata = X.test, newclass = Class.test)
plot(irisMclustDA, what = "error", newdata = X.test, newclass = Class.test, dimens = 3:4)
plot(irisMclustDA, what = "error", newdata = X.test, newclass = Class.test, dimens = 4)
# simulated 1D data
n <- 250
set.seed(1)
triModal <- c(rnorm(n,-5), rnorm(n,0), rnorm(n,5))
triClass <- c(rep(1,n), rep(2,n), rep(3,n))
odd <- seq(from = 1, to = length(triModal), by = 2)
even <- odd + 1
triMclustDA <- MclustDA(triModal[odd], triClass[odd])
summary(triMclustDA, parameters = TRUE)
summary(triMclustDA, newdata = triModal[even], newclass = triClass[even])
plot(triMclustDA)
plot(triMclustDA, what = "classification")
plot(triMclustDA, what = "classification", newdata = triModal[even])
plot(triMclustDA, what = "train&test", newdata = triModal[even])
plot(triMclustDA, what = "error")
plot(triMclustDA, what = "error", newdata = triModal[even], newclass = triClass[even])
# simulated 2D cross data
data(cross)
odd <- seq(from = 1, to = nrow(cross), by = 2)
even <- odd + 1
crossMclustDA <- MclustDA(cross[odd,-1], cross[odd,1])
summary(crossMclustDA, parameters = TRUE)
summary(crossMclustDA, newdata = cross[even,-1], newclass = cross[even,1])
plot(crossMclustDA)
plot(crossMclustDA, what = "classification")
plot(crossMclustDA, what = "classification", newdata = cross[even,-1])
plot(crossMclustDA, what = "train&test", newdata = cross[even,-1])
plot(crossMclustDA, what = "error")
plot(crossMclustDA, what = "error", newdata =cross[even,-1], newclass = cross[even,1])
}
}
\keyword{multivariate}
|
