Description Usage Arguments Details Value Author(s) Examples

$k$-nearest neighbour classification that can return class votes for all classes.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
knn3(x, ...)
## S3 method for class 'formula'
knn3(formula, data, subset, na.action, k = 5, ...)
## S3 method for class 'data.frame'
knn3(x, y, k = 5, ...)
## S3 method for class 'matrix'
knn3(x, y, k = 5, ...)
## S3 method for class 'knn3'
print(x, ...)
knn3Train(train, test, cl, k = 1, l = 0, prob = TRUE, use.all = TRUE)
``` |

`x` |
a matrix of training set predictors |

`...` |
additional parameters to pass to |

`formula` |
a formula of the form |

`data` |
optional data frame containing the variables in the model formula. |

`subset` |
optional vector specifying a subset of observations to be used. |

`na.action` |
function which indicates what should happen when the data
contain |

`k` |
number of neighbours considered. |

`y` |
a factor vector of training set classes |

`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. |

`cl` |
factor of true classifications of training set |

`l` |
minimum vote for definite decision, otherwise |

`prob` |
If this is true, the proportion of the votes for each class are
returned as attribute |

`use.all` |
controls handling of ties. If true, all distances equal to
the |

`knn3`

is essentially the same code as `ipredknn`

and `knn3Train`

is a copy of `knn`

. The underlying C
code from the `class`

package has been modified to return the vote
percentages for each class (previously the percentage for the winning class
was returned).

An object of class `knn3`

. See `predict.knn3`

.

`knn`

by W. N. Venables and B. D. Ripley and
`ipredknn`

by Torsten.Hothorn
<Torsten.Hothorn@rzmail.uni-erlangen.de>, modifications by Max Kuhn and
Andre Williams

1 2 3 4 5 6 7 8 9 | ```
irisFit1 <- knn3(Species ~ ., iris)
irisFit2 <- knn3(as.matrix(iris[, -5]), iris[,5])
data(iris3)
train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
knn3Train(train, test, cl, k = 5, prob = TRUE)
``` |

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