\HeaderA{obsinfo}{Classifiability of observations}{obsinfo}
\keyword{multivariate}{obsinfo}
\begin{Description}\relax
Some observations are harder to classify than others.
It is frequently of interest to know which observations
are consistenly misclassified; these are candiates for
outliers or wrong class labels.
\end{Description}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] An object of class \code{\LinkA{evaluation}{evaluation}}, generated
with \code{scheme = "observationwise"}
\item[\code{threshold}] threshold value of (observation-wise) performance measure,
s. \code{\LinkA{evaluation}{evaluation}} that has to be exceeded
in order to speak of consistent misclassification.
If \code{measure = "average probability"}, then
values \emph{below} \code{threshold} are regarded
as consistent misclassification. Note that the
default values 1 is not sensible in that case
\item[\code{show}] Should the information be printed ? Default is \code{TRUE}.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
As not all observation must have been classified at least once,
observations not classified at all are also shown.
\end{Details}
\begin{Value}
A list with two components
\begin{ldescription}
\item[\code{misclassification}] A \code{data.frame} containing the indices
of consistenly misclassfied observations and
the corresponding performance measure.
\item[\code{notclassified}] The indices of those observations not classfied at all,
s. details.
\end{ldescription}
\end{Value}
\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{evaluation}{evaluation}}
\end{SeeAlso}

