pca.supplementary.vertex.lambda.fun.j

Calculate the variance
pca.supplementary.vertex.lambda.fun.j

internal

Symbolic Data Analysis (SDA) was proposed by professor Edwin Diday in 1987, the main purpose of SDA is to substitute the set of rows (cases) in the data table for  a concept (second order statistical unit). This package implements, to the symbolic case, certain techniques of automatic classification, as well as some linear models.

Oldemar Rodriguez

RSDA

R to Symbolic Data Analysis

Jose Emmanuel Chacon

Carlos Aguero

Jorge Arce

pca.supplementary.vertex.lambda.fun.j function

<dl><dt>x</dt>
<dd>A Matrix</dd>
<dt>M</dt>
<dd>Number of variables</dd>
<dt>N</dt>
<dd>Number of concepts</dd>
<dt>sym.var.names</dt>
<dd>Names of concepts</dd>
<dt>sym.data.vertex.matrix</dt>
<dd>Vertex Matrix</dd>
<dt>tot.individuals</dt>
<dd>Number of individuals</dd>
<dt>num.dimen.aux</dt>
<dd>Number of dimensions</dd></dl>

Arguments

Calculate the variance — pca.supplementary.vertex.lambda.fun.j

<dl>

<dt>x</dt>
<dd>A Matrix</dd>


<dt>M</dt>
<dd>Number of variables</dd>


<dt>N</dt>
<dd>Number of concepts</dd>


<dt>sym.var.names</dt>
<dd>Names of concepts</dd>


<dt>sym.data.vertex.matrix</dt>
<dd>Vertex Matrix</dd>


<dt>tot.individuals</dt>
<dd>Number of individuals</dd>


<dt>num.dimen.aux</dt>
<dd>Number of dimensions</dd>

</dl>

pca.supplementary.vertex.lambda.fun.j: Calculate the variance

Description

Usage

Value

Arguments