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@institut-agro.fr

plot.DMFA, dimdesc
## Example with the famous Fisher's iris data