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

<p>Dataset to illustrate the selection of variables in regression
</p>

<p>Dataset with 85 rows and 6 columns :
85 milks described by the 5 variables: density, fat content, protein, casein, dry, yield</p>

data(milk)
res = RegBest(y=milk[,6],x=milk[,-6])
res$best

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

milk

Description

Usage

Format

Examples

FactoMineR

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