Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).

Multivariate Exploratory Data Analysis and Data Mining

package-ubuntu-20.04-r-4.0.5#cran#FactoMineR-2.4

<table summary="R argblock"><tr valign="top">
<td><code>tab</code></td>
<td>
<p>a data frame with factors</p>
</td>
</tr></table>

package-ubuntu-20.04-r-4.0.5#cran#FactoMineR-2.4#docs#tab.disjonctif

tab.disjonctif

Description

Usage

Arguments

Value

FactoMineR

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