

   MMuullttiieeddiitt ffoorr kk--NNNN CCllaassssiiffiieerr

        multiedit(x, class, k=1, V=3, I=5, trace=T)

   AArrgguummeennttss::

          x: matrix of training set.

      class: vector of classification of training set.

          k: number of neighbours used in k-NN.

          V: divide training set into V parts.

          I: number of null passes before quitting.

      trace: logical for statistics at each pass.

   VVaalluuee::

        index vector of cases to be retained.

   RReeffeerreenncceess::

        P. A. Devijver and J. Kittler (1982) Pattern Recogni-
        tion. A Statistical Approach.  Prentice-Hall, p. 115.

   SSeeee AAllssoo::

        `condense', `reduce.nn'

   EExxaammpplleess::

        set.seed(99)
        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,], test, cl[ind1], 1))
        ntrain <- train[ind1,]; ncl <- cl[ind1]
        ind2 <- condense(ntrain, ncl)
        length(ind2)
        table(cl, knn(ntrain[ind2,], test, ncl[ind2], 1))

