

   kk--NNeeaarreesstt NNeeiigghhbboouurr CCllaassssiiffiiccaattiioonn

        knn(train, test, class, k=1, l=1, prob=F, use.all=T)

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

      train: matrix or data frame of training set cases.

       test: matrix or data frame of test set cases. A vector
             will be interpreted as a row vector for a single
             case.

      class: factor of true classifications of training set

          k: number of neighbours considered.

          l: minimum vote for definite decision, otherwise
             `doubt'. (More precisely, less than `k-l' dissent-
             ing votes are allowed, even if `k' is increased by
             ties.)

       prob: If this is true, the proportion of the votes for
             the winning class are returned as attribute
             `prob'.

    use.all: controls handling of ties. If true, all distances
             equal to the `k'th largest are included. If false,
             a random selection of distances equal to the `k'th
             is chosen to use exactly `k' neighbours.

   DDeessccrriippttiioonn::

        k-nearest neighbour classification for test set from
        training set. For each row of the test set, the k near-
        est (in Euclidean distance) training set vectors are
        found, and the classification is decided by majority
        vote, with ties broken at random. If there are ties for
        the kth nearest vector, all candidates are included in
        the vote.

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

        factor of classifications of test set. `doubt' will be
        returned as `NA'.

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

        `knn1', `knn.cv'

