knn.cv {class} | R Documentation |

## k-Nearest Neighbour Cross-Validatory Classification

### Description

k-nearest neighbour cross-validatory classification from training set.

### Usage

```
knn.cv(train, cl, k = 1, l = 0, prob = FALSE, use.all = TRUE)
```

### Arguments

`train` |
matrix or data frame of training set cases. |

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

`k` |
number of neighbours considered. |

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

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

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

### Details

This uses leave-one-out cross validation.
For each row of the training set `train`

, the `k`

nearest
(in Euclidean distance) other
training set vectors are found, and the classification is decided by
majority vote, with ties broken at random. If there are ties for the
`k`

th nearest vector, all candidates are included in the vote.

### Value

Factor of classifications of training set. `doubt`

will be returned as `NA`

.

### References

Ripley, B. D. (1996)
*Pattern Recognition and Neural Networks.* Cambridge.

Venables, W. N. and Ripley, B. D. (2002)
*Modern Applied Statistics with S.* Fourth edition. Springer.

### See Also

### Examples

```
train <- rbind(iris3[,,1], iris3[,,2], iris3[,,3])
cl <- factor(c(rep("s",50), rep("c",50), rep("v",50)))
knn.cv(train, cl, k = 3, prob = TRUE)
attributes(.Last.value)
```

*class*version 7.3-22 Index]