pca_outliers

Calculates Hotelling T^2 (score distance) and Q-residual (orthogonal distance)
for each observation, given a chosen number of components.

Provides a set of basic and extensible data structures and functions for multivariate analysis, including dimensionality reduction techniques, projection methods, and preprocessing functions. The aim of this package is to offer a flexible and user-friendly framework for multivariate analysis that can be easily extended for custom requirements and specific data analysis tasks.

Bradley Buchsbaum

multivarious

Extensible Data Structures for Multivariate Analysis

pca_outliers function

<dl><dt>x</dt>
<dd>A <code>pca</code> object.</dd>
<dt>X</dt>
<dd>The original data matrix used for PCA.</dd>
<dt>ncomp</dt>
<dd>Number of components to consider.</dd>
<dt>cutoff</dt>
<dd>Logical or numeric specifying threshold for labeling outliers. If <code>TRUE</code>,
uses some typical statistical threshold (F-dist) for T^2, or sets an arbitrary Q limit.
If numeric, treat it as a cutoff. Default is <code>FALSE</code> (no labeling).</dd></dl>

Arguments

PCA Outlier Diagnostics — pca_outliers

<dl>

<dt>x</dt>
<dd>A <code>pca</code> object.</dd>


<dt>X</dt>
<dd>The original data matrix used for PCA.</dd>


<dt>ncomp</dt>
<dd>Number of components to consider.</dd>


<dt>cutoff</dt>
<dd>Logical or numeric specifying threshold for labeling outliers. If <code>TRUE</code>,
uses some typical statistical threshold (F-dist) for T^2, or sets an arbitrary Q limit.
If numeric, treat it as a cutoff. Default is <code>FALSE</code> (no labeling).</dd>

</dl>

pca_outliers: PCA Outlier Diagnostics

Description

Usage

Value

Arguments