MCA {FactoMineR}R Documentation

Multiple Correspondence Analysis (MCA)

Description

Performs Multiple Correspondence Analysis (MCA) with supplementary individuals, supplementary quantitative variables and supplementary qualitative variables.

Usage

MCA(X, ncp = 5, ind.sup = NULL, quanti.sup = NULL, 
    quali.sup = NULL, graph = TRUE)

Arguments

X a data frame with n rows (individuals) and p columns (categorical variables)
ncp number of dimensions kept in the results (by default 5)
ind.sup a vector indicating the indexes of the supplementary individuals
quanti.sup a vector indicating the indexes of the quantitative supplementary variables
quali.sup a vector indicating the indexes of the qualitative supplementary variables
graph boolean, if TRUE a graph is displayed

Value

Returns a list including:

eig a numeric vector containing all the eigenvalues
var a list of matrices containing all the results for the active variables (coordinates, square cosine, contributions, v.test)
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 matrix containing the coordinates of the supplementary quantitative variables (the correlation between a variable and an axis is equal to the variable coordinate on the axis)
quali.sup a list of matrices with all the results for the supplementary qualitative variables (coordinates of each categories of each variables, square cosine and v.test which is a criterion with a Normal distribution)
call a list with some statistics


Returns the individuals factor map and the variables factor map.

Author(s)

Jérémy Mazet, François Husson François.Husson@agrocampus-rennes.fr

See Also

print.MCA, plot.MCA

Examples

data (poison)
MCA (poison, quali.sup = 3:4, quanti.sup = 1:2)

[Package FactoMineR version 1.01 Index]