pca_interpret

Colored Table to Help Interpretation of Principal Component Analysis

Readable, complete and pretty graphs for correspondence analysis made
with 'FactoMineR'. They can be rendered as interactive 'HTML' plots, showing useful
informations at mouse hover. The interest is not mainly visual but statistical:
it helps the reader to keep in mind the data contained in the cross-table or Burt
table while reading the correspondence analysis, thus preventing over-interpretation.
Most graphs are made with 'ggplot2', which means that you can use the + syntax to
manually add as many graphical pieces you want, or change theme elements. 3D
graphs are made with 'plotly'.

Brice Nocenti

ggfacto

Graphs for Correspondence Analysis

pca_interpret function

<dl><dt>res.pca</dt>
<dd>The result of <code>FactoMineR::PCA</code>.</dd>
<dt>axes</dt>
<dd>The axes to print, as a numeric vector.</dd></dl>

Arguments

Colored Table to Help Interpretation of Principal Component Analysis — pca_interpret

<dl>

<dt>res.pca</dt>
<dd>The result of <code>FactoMineR::PCA</code>.</dd>


<dt>axes</dt>
<dd>The axes to print, as a numeric vector.</dd>

</dl>

pca_interpret: Colored Table to Help Interpretation of Principal Component Analysis

Description

Usage

Value

Arguments

Examples