# outlier

From randomForest v4.6-14
by Andy Liaw

##### Compute outlying measures

Compute outlying measures based on a proximity matrix.

- Keywords
- classif

##### Usage

```
# S3 method for default
outlier(x, cls=NULL, ...)
# S3 method for 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

##### Examples

```
# NOT RUN {
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)])
# }
```

*Documentation reproduced from package randomForest, version 4.6-14, License: GPL (>= 2)*

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