svd2

Singular Value Decomposition of a Matrix

internal

Simple Principal Components Analysis (PCA) and (Multiple)
Correspondence Analysis (CA) based on the Singular Value Decomposition
(SVD). This package provides S4 classes and methods to compute,
extract, summarize and visualize results of multivariate data
analysis. It also includes methods for partial bootstrap validation
described in Greenacre (1984, ISBN: 978-0-12-299050-2) and Lebart et
al. (2006, ISBN: 978-2-10-049616-7).

Nicolas Frerebeau

dimensio

Multivariate Data Analysis

Jean-Baptiste Fourvel

Brice Lebrun

Université Bordeaux Montaigne 

CNRS 

svd2 function

<dl><dt>x</dt>
<dd>A \(m \times p\) numeric <code><a href="/link/matrix?package=dimensio&version=0.10.1" data-mini-rdoc="dimensio::matrix">matrix</a></code>.</dd>
<dt>rank</dt>
<dd>An <code><a href="/link/integer?package=dimensio&version=0.10.1" data-mini-rdoc="dimensio::integer">integer</a></code> value specifying the maximal number of components
to be kept in the results.</dd></dl>

Arguments

Singular Value Decomposition of a Matrix — svd2

<dl>

<dt>x</dt>
<dd>A \(m \times p\) numeric <code><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></code>.</dd>


<dt>rank</dt>
<dd>An <code><a href='https://rdrr.io/r/base/integer.html'>integer</a></code> value specifying the maximal number of components
to be kept in the results.</dd>

</dl>

svd2: Singular Value Decomposition of a Matrix

Description

Usage

Value

Arguments