generateOutliers

Takes a vector, matrix or data frame and replaces some numeric values by outliers.

Provides a random forest based implementation of the method
described in Chapter 7.1.2 (Regression model based anomaly detection)
of Chandola et al. (2009) <doi:10.1145/1541880.1541882>. It works as
follows: Each numeric variable is regressed onto all other variables
by a random forest. If the scaled absolute difference between observed
value and out-of-bag prediction of the corresponding random forest is
suspiciously large, then a value is considered an outlier. The package
offers different options to replace such outliers, e.g. by realistic
values found via predictive mean matching. Once the method is trained
on a reference data, it can be applied to new data.

Michael Mayer

outForest

Multivariate Outlier Detection and Replacement

generateOutliers function

<dl><dt>x</dt>
<dd>A vector, matrix or <code>data.frame</code>.</dd>
<dt>p</dt>
<dd>Proportion of outliers to add to <code>x</code>. In case <code>x</code> is a <code>data.frame</code>, <code>p</code> can
also be a vector of probabilities per column or a named vector (see examples).</dd>
<dt>sd_factor</dt>
<dd>Each outlier is generated by shifting the original value by a
realization of a normal random variable with <code>sd_factor</code> times
the original sample standard deviation.</dd>
<dt>seed</dt>
<dd>An integer seed.</dd></dl>

Arguments

Adds Outliers — generateOutliers

<dl>

<dt>x</dt>
<dd>A vector, matrix or <code>data.frame</code>.</dd>


<dt>p</dt>
<dd>Proportion of outliers to add to <code>x</code>. In case <code>x</code> is a <code>data.frame</code>, <code>p</code> can
also be a vector of probabilities per column or a named vector (see examples).</dd>


<dt>sd_factor</dt>
<dd>Each outlier is generated by shifting the original value by a
realization of a normal random variable with <code>sd_factor</code> times
the original sample standard deviation.</dd>


<dt>seed</dt>
<dd>An integer seed.</dd>

</dl>

generateOutliers: Adds Outliers

Description

Usage

Value

Arguments

See Also

Examples