Multiple Correspondence Analysis

dudi.acm performs the multiple correspondence analysis of a factor table.
acm.burt an utility giving the crossed Burt table of two factors table.
acm.disjonctif an utility giving the complete disjunctive table of a factor table.
boxplot.acm a graphic utility to interpret axes.

Usage

dudi.acm (df, row.w = rep(1, nrow(df)), scannf = TRUE, nf = 2)
acm.burt (df1, df2, counts = rep(1, nrow(df1))) 
acm.disjonctif (df) 
# S3 method for class 'acm'
boxplot(x, xax = 1, ...)

Arguments

df, df1, df2: data frames containing only factors
row.w, counts: vector of row weights, by default, uniform weighting
scannf: a logical value indicating whether the eigenvalues bar plot should be displayed
nf: if scannf FALSE, an integer indicating the number of kept axes
x: an object of class acm
xax: the number of factor to display
...: further arguments passed to or from other methods

Value

dudi.acm returns a list of class acm and dudi (see dudi) containing

cr: a data frame which rows are the variables, columns are the kept scores and the values are the correlation ratios

References

Tenenhaus, M. & Young, F.W. (1985) An analysis and synthesis of multiple correspondence analysis, optimal scaling, dual scaling, homogeneity analysis ans other methods for quantifying categorical multivariate data. Psychometrika, 50, 1, 91-119.

Lebart, L., A. Morineau, and M. Piron. 1995. Statistique exploratoire multidimensionnelle. Dunod, Paris.

Author

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

Examples

data(ours)
summary(ours)
#>  altit  deniv  cloiso domain boise  hetra  favor  inexp  citat  depart 
#>  1: 8   1:13   1:12   1: 9   1:10   1:19   1:15   1:20   1:22   AHP:5  
#>  2:17   2:14   2: 4   2:13   2:15   2: 5   2:12   2:10   2: 7   AM :4  
#>  3:13   3:11   3:22   3:16   3:13   3:14   3:11   3: 8   3: 4   D  :5  
#>                                                          4: 5   HP :8  
#>                                                                 HS :4  
#>                                                                 I  :5  
#>                                                                 S  :7  

if(adegraphicsLoaded()) {
  g1 <- s1d.boxplot(dudi.acm(ours, scan = FALSE)$li[, 1], ours)
} else {
  boxplot(dudi.acm(ours, scan = FALSE))
}

if (FALSE) { # \dontrun{
data(banque)
banque.acm <- dudi.acm(banque, scann = FALSE, nf = 3)

if(adegraphicsLoaded()) {
  g2 <- adegraphics:::scatter.dudi(banque.acm)
} else {
  scatter(banque.acm)
}  

apply(banque.acm$cr, 2, mean)
banque.acm$eig[1:banque.acm$nf] # the same thing

if(adegraphicsLoaded()) {
  g3 <- s1d.boxplot(banque.acm$li[, 1], banque)
  g4 <- scatter(banque.acm)
} else {
  boxplot(banque.acm)
  scatter(banque.acm)
}


s.value(banque.acm$li, banque.acm$li[,3])

bb <- acm.burt(banque, banque)
bbcoa <- dudi.coa(bb, scann = FALSE)
plot(banque.acm$c1[,1], bbcoa$c1[,1])
# mca and coa of Burt table. Lebart & coll. section 1.4

bd <- acm.disjonctif(banque)
bdcoa <- dudi.coa(bd, scann = FALSE)
plot(banque.acm$li[,1], bdcoa$li[,1]) 
# mca and coa of disjonctive table. Lebart & coll. section 1.4
plot(banque.acm$co[,1], dudi.coa(bd, scann = FALSE)$co[,1]) 
} # }