e_afccol

Performs an exploratory data analysis through a 'shiny' interface. It includes basic methods such as the mean, median, mode, normality test, among others. It also includes clustering techniques such as Principal Components Analysis, Hierarchical Clustering and the K-Means Method.

Oldemar Rodriguez

discoveR

Exploratory Data Analysis System

Diego Jiménez

e_afccol function

<dl><dt>modelo</dt>
<dd>an object of class CA [FactoMineR].</dd>
<dt>axes</dt>
<dd>a numeric vector of length 2 specifying the dimensions to be plotted.</dd>
<dt>colorCol</dt>
<dd>a color for the variables well represented.</dd>
<dt>cos2</dt>
<dd>a numeric value from 0 to 1 specifying the quality of the variables.</dd>
<dt>colorCos</dt>
<dd>a color for the variables badly represented.</dd>
<dt>titulos</dt>
<dd>a character vector of length 2 specifying the titles to use on legend.</dd></dl>

Arguments

Diego Jimenez &lt;diego.jimenez@promidat.com&gt;

Author

AFC plot of variables — e_afccol

<dl>

<dt>modelo</dt>
<dd>an object of class CA [FactoMineR].</dd>


<dt>axes</dt>
<dd>a numeric vector of length 2 specifying the dimensions to be plotted.</dd>


<dt>colorCol</dt>
<dd>a color for the variables well represented.</dd>


<dt>cos2</dt>
<dd>a numeric value from 0 to 1 specifying the quality of the variables.</dd>


<dt>colorCos</dt>
<dd>a color for the variables badly represented.</dd>


<dt>titulos</dt>
<dd>a character vector of length 2 specifying the titles to use on legend.</dd>

</dl>

e_afccol: AFC plot of variables

Description

Usage

Value

Arguments

Author

Examples