Outliers

Computes outlierness scores and detects outliers.

classif

Additions to 'party' and 'partykit' packages : tools for the interpretation of forests (surrogate trees, prototypes, etc.), feature selection (see Gregorutti et al (2017) <arXiv:1310.5726>, Hapfelmeier and Ulm (2013) <doi:10.1016/j.csda.2012.09.020>, Altmann et al (2010) <doi:10.1093/bioinformatics/btq134>) and parallelized versions of conditional forest and variable importance functions. Also modules and a shiny app for conditional inference trees.

Nicolas Robette

moreparty

A Toolbox for Conditional Inference Trees and Random Forests

Outliers function

<dl> <dt>prox</dt>
<dd>a proximity matrix (a square matrix with 1 on the diagonal and values between 0 and 1 in the off-diagonal positions).</dd> <dt>cls</dt>
<dd>Factor. The classes the rows in the proximity matrix belong to. If NULL (default), all data are assumed to come from the same class.</dd> <dt>data</dt>
<dd>A data frame of variables to describe the outliers (optional).</dd> <dt>threshold</dt>
<dd>Numeric. The value of outlierness above which an observation is considered an outlier. Default is 10.</dd></dl>

Arguments

Computes outliers — Outliers

<dl>

 <dt>prox</dt>
<dd>a proximity matrix (a square matrix with 1 on the diagonal and values between 0 and 1 in the off-diagonal positions).</dd>

 <dt>cls</dt>
<dd>Factor. The classes the rows in the proximity matrix belong to. If NULL (default), all data are assumed to come from the same class.</dd>

 <dt>data</dt>
<dd>A data frame of variables to describe the outliers (optional).</dd>

 <dt>threshold</dt>
<dd>Numeric. The value of outlierness above which an observation is considered an outlier. Default is 10.</dd>

</dl>

Outliers: Computes outliers

Description

Usage

Value

Arguments

Details

Examples