# dudi.acm: Multiple Correspondence Analysis In ade4: Analysis of Ecological Data : Exploratory and Euclidean Methods in Environmental Sciences

## 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

 ```1 2 3 4 5``` ```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 B Dufour [email protected]

## 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.

`s.chull`, `s.class`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44``` ```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) ```