\HeaderA{multiedit}{Multiedit for k-NN Classifier}{multiedit}
\keyword{classif}{multiedit}
\begin{Description}\relax
Multiedit for k-NN classifier
\end{Description}
\begin{Usage}
\begin{verbatim}
multiedit(x, class, k = 1, V = 3, I = 5, trace = TRUE)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{x}] matrix of training set.

\item[\code{class}] vector of classification of training set.

\item[\code{k}] number of neighbours used in k-NN.

\item[\code{V}] divide training set into V parts.

\item[\code{I}] number of null passes before quitting.

\item[\code{trace}] logical for statistics at each pass.

\end{ldescription}
\end{Arguments}
\begin{Value}
index vector of cases to be retained.
\end{Value}
\begin{References}\relax
P. A. Devijver and J. Kittler (1982)
\emph{Pattern Recognition. A Statistical Approach.}
Prentice-Hall, p. 115.

Ripley, B. D. (1996)
\emph{Pattern Recognition and Neural Networks.} Cambridge.

Venables, W. N. and Ripley, B. D. (2002)
\emph{Modern Applied Statistics with S.} Fourth edition.  Springer.
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{condense}{condense}}, \code{\LinkA{reduce.nn}{reduce.nn}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
tr <- sample(1:50, 25)
train <- rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3])
test <- rbind(iris3[-tr,,1], iris3[-tr,,2], iris3[-tr,,3])
cl <- factor(c(rep(1,25),rep(2,25), rep(3,25)), labels=c("s", "c", "v"))
table(cl, knn(train, test, cl, 3))
ind1 <- multiedit(train, cl, 3)
length(ind1)
table(cl, knn(train[ind1, , drop=FALSE], test, cl[ind1], 1))
ntrain <- train[ind1,]; ncl <- cl[ind1]
ind2 <- condense(ntrain, ncl)
length(ind2)
table(cl, knn(ntrain[ind2, , drop=FALSE], test, ncl[ind2], 1))
\end{ExampleCode}
\end{Examples}

