R: Compute outlying measures

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 4.6-14 Index]