DMFA: Dual Multiple Factor Analysis (DMFA)

In FactoMineR: Multivariate Exploratory Data Analysis and Data Mining

Dual Multiple Factor Analysis (DMFA)

Description

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

Usage

DMFA(don, num.fact = ncol(don), scale.unit = TRUE, ncp = 5, 
    quanti.sup = NULL, quali.sup = NULL, graph = TRUE, axes=c(1,2))

Arguments

don	a data frame with n rows (individuals) and p columns (numeric variables)
num.fact	the number of the categorical variable which allows to make the group of individuals
scale.unit	a boolean, if TRUE (value set by default) then data are scaled to unit variance
ncp	number of dimensions kept in the results (by default 5)
quanti.sup	a vector indicating the indexes of the quantitative supplementary variables
quali.sup	a vector indicating the indexes of the categorical supplementary variables
graph	boolean, if TRUE a graph is displayed
axes	a length 2 vector specifying the components to plot

Value

Returns a list including:

eig	a matrix containing all the eigenvalues, the percentage of variance and the cumulative percentage of variance
var	a list of matrices containing all the results for the active variables (coordinates, correlation between variables and axes, square cosine, contributions)
ind	a list of matrices containing all the results for the active individuals (coordinates, square cosine, contributions)
ind.sup	a list of matrices containing all the results for the supplementary individuals (coordinates, square cosine)
quanti.sup	a list of matrices containing all the results for the supplementary quantitative variables (coordinates, correlation between variables and axes)
quali.sup	a list of matrices containing all the results for the supplementary categorical variables (coordinates of each categories of each variables, and v.test which is a criterion with a Normal distribution)
svd	the result of the singular value decomposition
var.partiel	a list with the partial coordinate of the variables for each group
cor.dim.gr
Xc	a list with the data centered by group
group	a list with the results for the groups (cordinate, normalized coordinates, cos2)
Cov	a list with the covariance matrices for each group

Returns the individuals factor map and the variables factor map.

Author(s)

Francois Husson [e-mail]

See Also

plot.DMFA, dimdesc

Examples

## Example with the famous Fisher's iris data
res.dmfa = DMFA ( iris, num.fact = 5)

FactoMineR documentation built on May 29, 2024, 3:36 a.m.