DMFA: Dual Multiple Factor Analysis (DMFA) In FactoMineR: Multivariate Exploratory Data Analysis and Data Mining

 DMFA 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