DMFA {FactoMineR}  R Documentation 
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 francois.husson@institutagro.fr
See Also
Examples
## Example with the famous Fisher's iris data
res.dmfa = DMFA ( iris, num.fact = 5)