AFDM {FactoMineR}R Documentation

Multiple Factor Analysis for Mixed Data

Description

Performs Multiple Factor Analysis with both quantitative and qualitative data.

Usage

AFDM (base, type, ncp = 5, graph = TRUE, sup.var = NULL, 
    ind.sup = NULL, axes = c(1,2))

Arguments

base a data frame with n rows (individuals) and p columns
type a vector indicating the type of each variable (the length of type must be equal to the number of columns of X
ncp number of dimensions kept in the results (by default 5)
graph boolean, if TRUE a graph is displayed
ind.sup a vector indicating the indexes of the supplementary individuals
sup.var a vector indicating the indexes of the supplementary variables
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
link.group
group
ind a list of matrices with all the results for the individuals (coordinates, square cosine, contributions)
quali.var a list of matrices with all the results for the qualitative variables (coordinates, square cosine, contributions, v.test)
quanti.var a list of matrices with all the results for the quantitative variables (coordinates, correlation, square cosine, contributions)
call a list with some statistics


Returns the individuals factor map.

Author(s)

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

References

Pages J. (2004). Analyse factorielle de donnees mixtes. Revue Statistique Appliquee. LII (4). pp. 93-111.

See Also

print.AFDM, plot.AFDM

Examples

## Not run: 
data(wine)
res.afdm = AFDM(wine[,c(1,2,30,31)],type=c("n","n","s","s"))
## End(Not run)

[Package FactoMineR version 1.10 Index]