get.limits.PCA.indivduals

Calculate the interval projection onto the principal components

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

get.limits.PCA.indivduals function

<dl><dt>sym.data</dt>
<dd>An interval matrix</dd>
<dt>matrix.stan</dt>
<dd>A standardized matrix</dd>
<dt>min.stan</dt>
<dd>A matrix of minimum values standardized for each interval</dd>
<dt>max.stan</dt>
<dd>A matrix of maximum values standardized for each interval</dd>
<dt>svd</dt>
<dd>An eigen vectors matrix</dd>
<dt>nn</dt>
<dd>Number of concepts</dd>
<dt>mm</dt>
<dd>Number of variables</dd></dl>

Arguments

Projections onto PCA — get.limits.PCA.indivduals

<dl>

<dt>sym.data</dt>
<dd>An interval matrix</dd>


<dt>matrix.stan</dt>
<dd>A standardized matrix</dd>


<dt>min.stan</dt>
<dd>A matrix of minimum values standardized for each interval</dd>


<dt>max.stan</dt>
<dd>A matrix of maximum values standardized for each interval</dd>


<dt>svd</dt>
<dd>An eigen vectors matrix</dd>


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


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

</dl>

get.limits.PCA.indivduals: Projections onto PCA

Description

Usage

Value

Arguments