| dudi.acm {ade4} | R Documentation |
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.
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, ...)
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 |
xax |
the number of factor to display |
... |
further arguments passed to or from other methods |
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 |
Daniel Chessel
Anne B Dufour anne-beatrice.dufour@univ-lyon1.fr
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.
data(ours)
summary(ours)
if(adegraphicsLoaded()) {
g1 <- s1d.boxplot(dudi.acm(ours, scan = FALSE)$li[, 1], ours)
} else {
boxplot(dudi.acm(ours, scan = FALSE))
}
## Not run:
data(banque)
banque.acm <- dudi.acm(banque, scann = FALSE, nf = 3)
if(adegraphicsLoaded()) {
g2 <- adegraphics:::scatter.dudi(banque.acm)
} else {
scatter(banque.acm)
}
apply(banque.acm$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])
## End(Not run)