Description Usage Arguments Details Value Author(s) See Also Examples

Compute the k-nearest neighbor classification given a matrix of cross-distances and a factor of class values. For each row the majority class is found, where ties are broken at random (default). If there are ties for the kth nearest neighbor, all candidates are included in the vote (default).

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`x` |
a cross-distances matrix. |

`y` |
a factor of class values of the columns of |

`k` |
number of nearest neighbors to consider. |

`l` |
minimum number of votes for a definite decision. |

`break.ties` |
option to break ties. |

`use.all` |
option to consider all neighbors that are tied with the kth neighbor. |

`prob` |
optionally return proportions of winning votes. |

The rows of the cross-distances matrix are interpreted as referencing the test samples and the columns as referencing the training samples.

The options are fashioned after `knn`

in package class but are
extended for tie breaking of votes, e.g. if only definite (majority) votes
are of interest.

Missing class values are not allowed because that would collide with a missing classification result.

Missing distance values are ignored but with the possible consequence of missing classification results. Note that this depends on the options settings, e.g.

Returns a factor of class values (of the rows of `x`

) which may be
`NA`

in the case of doubt (no definite decision), ties, or missing
neighborhood information.

The proportions of winning votes are returned as attribute `prob`

(if option `prob`

was used).

Christian Buchta

`dist`

for efficient computation of cross-distances.

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