MFA: Multiple Factor Analysis (MFA)

Description

Performs Multiple Factor Analysis (MFA) with supplementary individuals and supplementary groups of variables. Groups of variables can be quantitative or qualitative.

Usage

MFA (base, group, type = rep("s",length(group)), ind.sup = NULL, 
    ncp = 5, name.group = NULL, num.group.sup = NULL, 
    graph = TRUE, weight.col.mfa = NULL)

Arguments

base

a data frame with n rows (individuals) and p columns (variables)

group

a list indicating the number of variables in each group

type

the type of variables in each group; three possibilities: "c" or "s" for quantitative variables (the difference is that for "s" variables are scaled to unit variance), "n" for qualitative variables; by default, all variables are quantitative and scale

ind.sup

a vector indicating the indexes of the supplementary individuals

ncp

number of dimensions kept in the results (by default 5)

name.group

a vector containing the name of the groups (by default, NULL and the group are named group.1, group.2 and so on)

num.group.sup

the indexes of the illustrative groups (by default, NULL and no group are illustrative)

graph

boolean, if TRUE a graph is displayed

weight.col.mfa

vector of weights, useful for HMFA method (by default, NULL and an MFA is performed)

Value

summary.qualia summary of the results for the qualitative variables
summary.quantia summary of the results for the quantitative variables
separate.analysesthe results for the separate analyses
eiga numeric vector containing all the eigenvalues
groupa list of matrices containing all the results for the groups (Lg and RV coefficients, coordinates, square cosine, contributions, distance to the origin, the correlations between each group and each factor)
rapport.inertieinertia ratio
inda list of matrices containing all the results for the active individuals (coordinates, square cosine, contributions)
ind.supa list of matrices containing all the results for the supplementary individuals (coordinates, square cosine)
quanti.vara list of matrices containing all the results for the quantitative variables (coordinates, correlation between variables and axes)
quali.vara list of matrices containing all the results for the supplementary qualitative variables (coordinates of each categories of each variables, and v.test which is a criterion with a Normal distribution)
partial.axesa list of matrices containing all the results for the partial axes (coordinates, correlation between variables and axes, correlation between partial axes)
Returns the individuals factor map, the variables factor map and the groups factor map.

References

Escofier, B. and Pag�s, J. (1994) Multiple Factor Analysis (AFMULT package), Computational Statistics and Data Analysis, 18, 121-140.

Examples

Run this code

data(wine)
aa = MFA(wine, group=c(2,5,3,10,9,2), type=c("n",rep("s",5)),
    ncp=5, name.group=c("orig","olf","vis","olfag","gust","ens"),
    num.group.sup=c(1,6))

#### Interactive graph
liste = plot.MFApartial(aa)
plot(aa,choix="ind",habillage = "Terroir")

###Example 2
data (poison)
MFA(poison, group=c(2,2,5,6), type=c("s","n","n","n"),
    name.group=c("desc","desc2","symptom","eat"),
    num.group.sup=1:2)