PCAmixdata (version 2.1)

MFAmix: Multiple Factor Analysis for a mixture of qualitative and quantitative variables inside the groups

Description

Performs Multiple Factor Analysis in the sense of Escofier-Pages. Groups of variables can be quantitative, categorical or contain both quantitatives and categoricals variables.

Usage

MFAmix(data, group, name.group, ndim, graph = TRUE,
    axes = c(1, 2))

Arguments

data

a data frame with n rows and p colums containing all the variables. This data frame will be split into G groups according to the vector group

group

a vector of size p whose values indicate at which group each variable belongs

name.group

a vector of size G which contains names for each group we want to create. Each name must be written as a charactor chain without any spaces and any special characters. Only letters, numbers and "_" allowed.

ndim

number of dimensions kept in the results

graph

boolean, if TRUE (default) a graph is displayed

axes

a length 2 vector specifying the axes to plot

Value

• eig
  a matrix containing all the eigenvalues, the percentage of variance and the cumulative percentage of variance
• separate.analyses
  the results for the separate analyses for each group
• group
  a list of matrices containing all the results for the groups (Lg and RV coefficients, coordinates, square cosine, contributions, distance to the origin)
• partial.axes
  a list of matrices containing all the results for the partial axes (coordinates, correlation between variables and axes)
• ind
  a list of matrices containing all the results for the individuals (coordinates, square cosine, contributions)
• ind.partiel
  a matrice containing the coordinates of the partial individuals
• quanti.var
  a list of matrices containing all the results for the quantitative variables (coordinates, contribution, cos2)
• quali.var
  a list of matrices containing all the results for the categorical variables (coordinates, contribution, cos2)
• global.pca
  The results of the MFAmix considered as a unique weighted PCAmix

References

{Escofier B et Pages J (1983), Methode pour l'analyse de plusieurs groupes de variables. Application a la caracterisation des vins rouges du Val de Loire, Revue de statistique appliquee, 31(2) : 43-59. Escofier B et Pages J (1998), Analyses factorielles simples et multiples, Dunod, 3e ed.}

Examples

#MFAmix:
data(INSEE)

class.var<-c(1,1,1,1,1,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,4,4,4,4,
             5,5,1,1,1,1,6,7,7,7,7,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,8,8,1)
nom.groupes<-c("Log_Services","Population","Emplois","Condit_Famil",
      "Educ","Revenu","Environnement","Secu_et_Soins")
res<-MFAmix(data=INSEE,group=class.var,name.group=nom.groupes,ndim=5,graph=FALSE)
summary(res)
res$eig
res$group$Lg

#Eigenvalues of the separate analyses
res$recap.eig.separate

#Individuals map on dim 1-2 colored by MedGen
plot(res,choice="ind",invisible="quali",habillage="MedGen_R",
     cex=0.6,title="Individuals colored by  the Qualitative variable MedGen_R") 
#Categories maps on dim 2-3
plot(res,choice="ind",invisible="ind",cex=0.6,axes=c(2,3)) 
#Partial axes on dim 1-2
plot(res,choice="axes",invisible="ind",cex=0.6,habillage="group",
     col.hab=c("green","blue","red","skyblue1","orange","grey","purple","brown"))
#Groups representation on dim 1-2
plot(res,choice="group",cex=0.6,habillage="group")
#Correlation circle on dim 1-2
plot(res,choice="var",cex=0.6,habillage="group")
#Squared loadings on dim 1-2
plot(res,choice="loadings",cex=0.6,habillage="group")
#Some partial individuals
plot(res,choice="ind",invisible="quali",habillage="group",partial=c("33119","17410"),cex=0.6) 
#All partial individuals
plot(res,choice="ind",invisible="quali",habillage="group",partial="all",cex=0.6)