outlier

outlier.randomForest

outlier.default

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 <code><a rd-options="" href="/link/randomForest?package=randomForest&version=4.7-1.1" data-mini-rdoc="randomForest::randomForest">randomForest</a></code>, whose <code>type</code> is not
 <code>regression</code>.

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

Compute outlying measures based on a proximity matrix.

classif

Classification and regression based on a forest of trees using random inputs, based on Breiman (2001) <DOI:10.1023/A:1010933404324>.

Andy Liaw

randomForest

Breiman and Cutler's Random Forests for Classification and
Regression

outlier function

<dl> <dt>x</dt>
<dd>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 <code>randomForest</code>, whose <code>type</code> is not
 <code>regression</code>.</dd> <dt>cls</dt>
<dd>the classes the rows in the proximity matrix belong to. If
 not given, all data are assumed to come from the same class.</dd> <dt>...</dt>
<dd>arguments for other methods.</dd></dl>

Arguments

Compute outlying measures — outlier

<dl>

 <dt>x</dt>
<dd>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 <code>randomForest</code>, whose <code>type</code> is not
 <code>regression</code>.</dd>

 <dt>cls</dt>
<dd>the classes the rows in the proximity matrix belong to. If
 not given, all data are assumed to come from the same class.</dd>

 <dt>...</dt>
<dd>arguments for other methods.</dd>

</dl>

outlier: Compute outlying measures

Description

Usage

Value

Arguments

See Also

Examples