.flag_outliers

A clustering algorithm similar to K-Means is implemented, it has two main advantages,
namely (a) The estimator is resistant to outliers, that means that results of estimator are still correct when
there are atypical values in the sample and (b) The estimator is efficient, roughly speaking,
if there are no outliers in the sample, results will be similar to those obtained by a classic algorithm (K-Means).
Clustering procedure is carried out by minimizing the overall robust scale so-called tau scale.
(see Gonzalez, Yohai and Zamar (2019) <arxiv:1906.08198>).

Juan Domingo Gonzalez

ktaucenters

Robust Clustering Procedures

Victor J. Yohai

Ruben H. Zamar

Douglas Alberto Carmona Guanipa

.flag_outliers function

<dl><dt>cutoff</dt>
<dd>quantile of chi-square to be used as a threshold for outliers
detection.</dd>
<dt>b</dt>
<dd>break down point.</dd>
<dt>ktau</dt>
<dd>ktaucenters results.</dd></dl>

Arguments

Flag outliers — .flag_outliers

<dl>

<dt>cutoff</dt>
<dd>quantile of chi-square to be used as a threshold for outliers
detection.</dd>


<dt>b</dt>
<dd>break down point.</dd>


<dt>ktau</dt>
<dd>ktaucenters results.</dd>

</dl>

.flag_outliers: Flag outliers

Description

Usage

Value

Arguments