MCA {FactoMineR}R Documentation

Multiple Correspondence Analysis (MCA)

Description

Performs Multiple Correspondence Analysis (MCA) with supplementary individuals, supplementary quantitative variables and supplementary qualitative variables.
Missing values are treated as an additional level,

Usage

MCA(X, ncp = 5, ind.sup = NULL, quanti.sup = NULL, 
    quali.sup = NULL, graph = TRUE, axes = c(1,2),
    row.w = NULL)

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
axes a length 2 vector specifying the components to plot
row.w an optional row weights (by default, uniform row weights)

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, 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)

Jeremy Mazet, Francois Husson Francois.Husson@agrocampus-ouest.fr

See Also

print.MCA, plot.MCA, dimdesc

Examples

## Not run: 
data (poison)
res.mca = MCA (poison, quali.sup = 3:4, quanti.sup = 1:2)
## End(Not run)

[Package FactoMineR version 1.10 Index]