\HeaderA{clusteringOutput-class}{container for clustering outputs in uniform structure}{clusteringOutput.Rdash.class}
\aliasA{getConverter}{clusteringOutput-class}{getConverter}
\aliasA{getConverter,clusteringSchema-method}{clusteringOutput-class}{getConverter,clusteringSchema.Rdash.method}
\aliasA{getDist}{clusteringOutput-class}{getDist}
\aliasA{getDist,clusteringSchema-method}{clusteringOutput-class}{getDist,clusteringSchema.Rdash.method}
\aliasA{plot,clusteringOutput,ANY-method}{clusteringOutput-class}{plot,clusteringOutput,ANY.Rdash.method}
\aliasA{RObject,clusteringOutput-method}{clusteringOutput-class}{RObject,clusteringOutput.Rdash.method}
\aliasA{show,clusteringOutput-method}{clusteringOutput-class}{show,clusteringOutput.Rdash.method}
\aliasA{show,clusteringSchema-method}{clusteringOutput-class}{show,clusteringSchema.Rdash.method}
\keyword{classes}{clusteringOutput-class}
\begin{Description}\relax
container for clustering outputs in uniform structure
\end{Description}
\begin{Section}{Objects from the Class}
Objects can be created by calls of the form \code{new("clusteringOutput", ...)}.
\end{Section}
\begin{Section}{Slots}
\describe{
\item[\code{partition}:] Object of class \code{"integer"}, labels for
observations as clustered 
\item[\code{silhouette}:] Object of class \code{"silhouette"},
structure from Rousseeuw cluster package measuring cluster
membership strength per observation
\item[\code{distEnv}:] Object of class \code{"environment"} not in use 
\item[\code{prcomp}:] Object of class \code{"prcompObj"} a wrapped
instance of stats package prcomp output 
\item[\code{metric}:] Object of class \code{"character"} string
identifying the distance function used to orient objects in feature space 
\item[\code{call}:] Object of class \code{"call"} for auditing 
\item[\code{learnerSchema}:] Object of class \code{"learnerSchema"}, a
formal object indicating the package, function, and other attributes
of the clustering algorithm employed to generate this object  
\item[\code{RObject}:] Object of class \code{"ANY"}, the
unaltered output of the function called according to learnerSchema 
}
\end{Section}
\begin{Section}{Methods}
\describe{
\item[RObject] \code{signature(x = "clusteringOutput")}: extract
the unaltered output of the R function or method called according
to learnerSchema 
\item[plot] \code{signature(x = "clusteringOutput", y = "ANY")}: 
a 4-panel plot showing features of the clustering, including the
scree plot for a principal components transformation and a display
of the partition in PC1xPC2 plane.  For a clustering method that
does not have a native plot procedure, such as kmeans, the parameter
y should be bound to a data frame or matrix with feature data for all records;
an image plot of robust feature z-scores (z=(x-median(x))/mad(x)) and
the cluster indices is produced in the northwest panel.  
\item[show] \code{signature(object = "clusteringOutput")}: concise
report 
}
\end{Section}
\begin{Author}\relax
VJ Carey <stvjc@channing.harvard.edu>
\end{Author}
\begin{Examples}
\begin{ExampleCode}
showClass("clusteringOutput")
\end{ExampleCode}
\end{Examples}

