new_shapley

This function creates an object of class <code>shapley</code> that is returned by the <code>shapley</code> function.

Based on Shapley values to explain multivariate outlyingness and to detect and impute cellwise outliers.
Includes implementations of methods described in Mayrhofer and Filzmoser (2023) <doi:10.1016/j.ecosta.2023.04.003>.

Marcus Mayrhofer

ShapleyOutlier

Multivariate Outlier Explanations using Shapley Values and
Mahalanobis Distances

Peter Filzmoser

new_shapley function

<dl><dt>phi</dt>
<dd>A \(p\)-dimensional vector (or a \(n \times p\) matrix) containing the
Shapley values (outlyingness-scores) of a \(p\)-dimensional data vector (or a \(n \times p\) data matrix).</dd>
<dt>mu_tilde</dt>
<dd>Optional. A \(p\)-dimensional vector (or a \(n \times p\) matrix) containing the
alternative reference points based on the regular cells of the original observations.</dd>
<dt>non_centrality</dt>
<dd>Optional. The non-centrality parameters for the Chi-Squared distribution,
which are given by <code>mahlanobis(mu_tilde, mu, Sigma)</code>.</dd></dl>

Arguments

Class constructor for class shapley. — new_shapley

<dl>

<dt>phi</dt>
<dd>A \(p\)-dimensional vector (or a \(n \times p\) matrix) containing the
Shapley values (outlyingness-scores) of a \(p\)-dimensional data vector (or a \(n \times p\) data matrix).</dd>


<dt>mu_tilde</dt>
<dd>Optional. A \(p\)-dimensional vector (or a \(n \times p\) matrix) containing the
alternative reference points based on the regular cells of the original observations.</dd>


<dt>non_centrality</dt>
<dd>Optional. The non-centrality parameters for the Chi-Squared distribution,
which are given by <code>mahlanobis(mu_tilde, mu, Sigma)</code>.</dd>

</dl>

new_shapley: Class constructor for class `shapley`.

Description

Usage

Value

Arguments