MFA {FactoMineR}R Documentation

Multiple Factor Analysis (MFA)

Description

Performs Multiple Factor Analysis in the sense of Escofier-Pages with supplementary individuals and supplementary groups of variables. Groups of variables can be quantitative or qualitative.
Missing values in numeric variables are replaced by the column mean. Missing values in categorical variables are treated as an additional level,

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, axes = c(1,2))

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 scaled to unit variance
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)
axes a length 2 vector specifying the components to plot

Value

summary.quali a summary of the results for the qualitative variables
summary.quanti a summary of the results for the quantitative variables
separate.analyses the results for the separate analyses
eig a matrix containing all the eigenvalues, the percentage of variance and the cumulative percentage of variance
group a 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.inertie inertia ratio
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.var a list of matrices containing all the results for the quantitative variables (coordinates, correlation between variables and axes)
quali.var a 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.axes a 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.

Author(s)

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

References

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

See Also

print.MFA, plot.MFA, dimdesc

Examples

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))
barplot(aa$eig[,1],main="Eigenvalues",names.arg=1:nrow(aa$eig))

## Not run: 
#### 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)
## End(Not run)

[Package FactoMineR version 1.10 Index]