R: Multiple Correspondence Analysis

dudi.acm {ade4} | R Documentation

## Multiple Correspondence Analysis

### Description

`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

### Author(s)

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

### 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.

### See Also

`s.chull`, `s.class`

### Examples

    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)

[Package ade4 version 1.7-16 Index]