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\name{mclust1Dplot}
\alias{mclust1Dplot}
\title{
Plot one-dimensional data modeled by an MVN mixture.
}
\description{
Plot one-dimensional data given parameters of an MVN mixture model
for the data.
}
\usage{
mclust1Dplot(data, parameters = NULL, z = NULL,
classification = NULL, truth = NULL, uncertainty = NULL,
what = c("classification", "density", "error", "uncertainty"),
symbols = NULL, colors = NULL, ngrid = length(data),
xlab = NULL, ylab = NULL,
xlim = NULL, ylim = NULL,
cex = 1, main = FALSE, \dots)
}
\arguments{
\item{data}{
A numeric vector of observations.
Categorical variables are not allowed.
}
\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{pro}}{
Mixing proportions for the components of the mixture.
There should one more mixing proportion than the number of
Gaussian components if the mixture model includes
a Poisson noise term.
}
\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 options: \code{"classification"}
(default), \code{"density"}, \code{"error"}, \code{"uncertainty"}.
}
\item{symbols}{
Either an integer or character vector assigning a plotting symbol to
each unique class \code{classification}. Elements in \code{symbols}
correspond to classes in \code{classification} in order of
appearance in the observations (the order used by the
function \code{unique}). The default is to use a single plotting
symbol \emph{|}. Classes are delineated by showing them in separate
lines above the whole of the data.
}
\item{colors}{
Either an integer or character vector assigning a color to each
unique class \code{classification}. Elements in \code{colors}
correspond to classes in order of appearance in the observations
(the order used by the function \code{unique}).
The default is given is \code{mclust.options("classPlotColors")}.
}
\item{ngrid}{
Number of grid points to use for density computation over the interval
spanned by the data. The default is the length of the data set.
}
\item{xlab, ylab}{
An argument specifying a label for the axes.
}
\item{xlim, ylim}{
An argument specifying bounds of the plot.
This may be useful for when comparing plots.
}
\item{cex}{
An argument specifying the size of the plotting symbols.
The default value is 1.
}
\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 location of the mixture components, classification, uncertainty, density and/or classification errors. Points in the different classes are shown in separated levels above the whole of the data.
}
\seealso{
\code{\link{mclust2Dplot}},
\code{\link{clPairs}},
\code{\link{coordProj}}
}
\examples{
\donttest{
n <- 250 ## create artificial data
set.seed(1)
y <- c(rnorm(n,-5), rnorm(n,0), rnorm(n,5))
yclass <- c(rep(1,n), rep(2,n), rep(3,n))
yModel <- Mclust(y)
mclust1Dplot(y, parameters = yModel$parameters, z = yModel$z,
what = "classification")
mclust1Dplot(y, parameters = yModel$parameters, z = yModel$z,
what = "error", truth = yclass)
mclust1Dplot(y, parameters = yModel$parameters, z = yModel$z,
what = "density")
mclust1Dplot(y, z = yModel$z, parameters = yModel$parameters,
what = "uncertainty")
}
}
\keyword{cluster}
|
