DMFA

a data frame with <em>n</em> rows (individuals) and <em>p</em> columns (numeric variables)

the number of the categorical variable which allows to make the group of individuals

num.fact

a boolean, if TRUE (value set by default) then data are scaled to unit variance

scale.unit

number of dimensions kept in the results (by default 5)

a vector indicating the indexes of the quantitative supplementary variables

quanti.sup

a vector indicating the indexes of the categorical supplementary variables

quali.sup

graph

a length 2 vector specifying the components to plot

axes


Performs Dual Multiple Factor Analysis (DMFA) with supplementary individuals, supplementary quantitative 
variables and supplementary categorical variables.


multivariate

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.

DMFA: Dual Multiple Factor Analysis (DMFA)

Description

Usage

Arguments

Value

See Also

Examples