outlier {randomForest} | R Documentation |

## Compute outlying measures

### Description

Compute outlying measures based on a proximity matrix.

### Usage

## Default S3 method:
outlier(x, cls=NULL, ...)
## S3 method for class 'randomForest':
outlier(x, ...)

### Arguments

`x` |
a proximity matrix (a square matrix with 1 on the diagonal
and values between 0 and 1 in the off-diagonal positions); or an object of
class `randomForest` , whose `type` is not
`regression` . |

`cls` |
the classes the rows in the proximity matrix belong to. If
not given, all data are assumed to come from the same class. |

`...` |
arguments for other methods. |

### Value

A numeric vector containing the outlying measures. The outlying
measure of a case is computed as n / sum(squared proximity), normalized by
subtracting the median and divided by the MAD, within each class.

### See Also

`randomForest`

### Examples

set.seed(1)
iris.rf <- randomForest(iris[,-5], iris[,5], proximity=TRUE)
plot(outlier(iris.rf), type="h",
col=c("red", "green", "blue")[as.numeric(iris$Species)])

[Package

*randomForest* version 4.5-1

Index]