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\name{randProj}
\alias{randProj}
\title{Random projections of multidimensional data modeled by an MVN mixture}
\description{
Plots random projections given multidimensional data
and parameters of an MVN mixture model for the data.
}
\usage{
randProj(data, seeds = NULL, parameters = NULL, z = NULL,
classification = NULL, truth = NULL, uncertainty = NULL,
what = c("classification", "error", "uncertainty"),
quantiles = c(0.75, 0.95),
addEllipses = TRUE, fillEllipses = mclust.options("fillEllipses"),
symbols = NULL, colors = NULL, scale = FALSE,
xlim = NULL, ylim = NULL, xlab = NULL, ylab = NULL,
cex = 1, PCH = ".", main = FALSE, \dots)
}
\arguments{
\item{data}{
A numeric matrix or data frame of observations.
Categorical variables are not allowed.
If a matrix or data frame, rows correspond to observations and
columns correspond to variables.
}
\item{seeds}{
An integer value or a vector of integer values to be used as seed for
random number generation. If multiple values are provided, then each seed
should produce a different projection.
By default, a single seed is drawn randomnly, so each call of
\code{randProj()} produces different projections.
}
\item{parameters}{
A named list giving the parameters of an \emph{MCLUST} model,
used to produce superimposing ellipses on the plot.
The relevant components are as follows:
\describe{
\item{\code{mean}}{
The mean for each component. If there is more than one component,
this is a matrix whose kth column is the mean of the \emph{k}th
component of the mixture model.
}
\item{\code{variance}}{
A list of variance parameters for the model.
The components of this list depend on the model
specification. See the help file for \code{\link{mclustVariance}}
for details.
}
}
}
\item{z}{
A matrix in which the \code{[i,k]}th entry gives the
probability of observation \emph{i} belonging to the \emph{k}th class.
Used to compute \code{classification} and
\code{uncertainty} if those arguments aren't available.
}
\item{classification}{
A numeric or character vector representing a classification of
observations (rows) of \code{data}. If present argument \code{z}
will be ignored.
}
\item{truth}{
A numeric or character vector giving a known
classification of each data point.
If \code{classification}
or \code{z} is also present,
this is used for displaying classification errors.
}
\item{uncertainty}{
A numeric vector of values in \emph{(0,1)} giving the
uncertainty of each data point. If present argument \code{z}
will be ignored.
}
\item{what}{
Choose from one of the following three options: \code{"classification"}
(default), \code{"error"}, \code{"uncertainty"}.
}
\item{quantiles}{
A vector of length 2 giving quantiles used in plotting
uncertainty. The smallest symbols correspond to the smallest
quantile (lowest uncertainty), medium-sized (open) symbols to points
falling between the given quantiles, and large (filled) symbols to
those in the largest quantile (highest uncertainty). The default is
\emph{(0.75,0.95)}.
}
\item{addEllipses}{
A logical indicating whether or not to add ellipses with axes
corresponding to the within-cluster covariances in case of
\code{"classification"} or \code{"uncertainty"} plots.
}
\item{fillEllipses}{
A logical specifying whether or not to fill ellipses with transparent
colors when \code{addEllipses = TRUE}.
}
\item{symbols}{
Either an integer or character vector assigning a plotting symbol 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{unique}).
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{unique}).
The default is given by \code{mclust.options("classPlotColors")}.
}
\item{scale}{
A logical variable indicating whether or not the two chosen
dimensions should be plotted on the same scale, and
thus preserve the shape of the distribution.
Default: \code{scale=FALSE}
}
\item{xlim, ylim}{
Optional arguments specifying bounds for the ordinate, abscissa of the plot.
This may be useful for when comparing plots.
}
\item{xlab, ylab}{
Optional arguments specifying the labels for, respectively, the horizontal
and vertical axis.
}
\item{cex}{
A numerical value specifying the size of the plotting symbols.
The default value is 1.
}
\item{PCH}{
An argument specifying the symbol to be used when a classificatiion
has not been specified for the data. The default value is a small dot ".".
}
\item{main}{
A logical variable or \code{NULL} indicating whether or not to add a title
to the plot identifying the dimensions used.
}
\item{\dots}{
Other graphics parameters.
}
}
\value{
A plot showing a random two-dimensional projection of the data, together with the location of the mixture components, classification, uncertainty, and/or classification errors.
The function also returns an invisible list with components \code{basis}, the randomnly generated basis of the projection subspace, \code{data}, a matrix of projected data, and \code{mu} and \code{sigma} the component parameters transformed to the projection subspace.
}
\seealso{
\code{\link{clPairs}},
\code{\link{coordProj}},
\code{\link{mclust2Dplot}},
\code{\link{mclust.options}}
}
\examples{
\donttest{
est <- meVVV(iris[,-5], unmap(iris[,5]))
par(pty = "s", mfrow = c(1,1))
randProj(iris[,-5], seeds=1:3, parameters = est$parameters, z = est$z,
what = "classification", main = TRUE)
randProj(iris[,-5], seeds=1:3, parameters = est$parameters, z = est$z,
truth = iris[,5], what = "error", main = TRUE)
randProj(iris[,-5], seeds=1:3, parameters = est$parameters, z = est$z,
what = "uncertainty", main = TRUE)
}
}
\keyword{cluster}
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