\HeaderA{plot}{Probability plot}{plot}
\aliasA{plot,cloutput,missing-method}{plot}{plot,cloutput,missing.Rdash.method}
\aliasA{plot,cloutput-method}{plot}{plot,cloutput.Rdash.method}
\methaliasA{plot,cloutput-method}{plot}{plot,cloutput.Rdash.method}
\keyword{multivariate}{plot}
\begin{Description}\relax
A popular way of visualizing the output of classifier
is to plot, separately for each class, the predicted
probability of each predicted observations for the respective class.
For this purpose, the plot area is divided into \code{K}
parts, where \code{K} is the number of classes.
Predicted observations are assigned, according to their
true class, to one of those parts. Then, for each part
and each predicted observation, the predicted probabilities
are plotted, displayed by coloured dots, where each
colour corresponds to one class.
\end{Description}
\begin{Arguments}
\begin{ldescription}
\item[\code{x}] An object of class \code{\LinkA{cloutput}{cloutput}} whose slot
\code{probmatrix} does not contain any missing value,
i.e. probability estimations are provided by the classifier.
\item[\code{main}] A title for the plot (character).
\end{ldescription}
\end{Arguments}
\begin{Value}
No return.
\end{Value}
\begin{Note}\relax
The plot usually only makes sense if a sufficiently large numbers
of observations has been classified. This is usually achieved
by running the classifier on several \code{\LinkA{learningsets}{learningsets}}
with the method \code{\LinkA{classification}{classification}}. The output can
then be processed via \code{\LinkA{join}{join}} to obtain an object
of class \code{\LinkA{cloutput}{cloutput}} to which this method can be applied.
\end{Note}
\begin{Author}\relax
Martin Slawski \email{martin.slawski@campus.lmu.de}

Anne-Laure Boulesteix \url{http://www.slcmsr.net/boulesteix}
\end{Author}
\begin{SeeAlso}\relax
\code{\LinkA{cloutput}{cloutput}}
\end{SeeAlso}

