Correspondence Analysis

performs a correspondence analysis.

Usage

dudi.coa(df, scannf = TRUE, nf = 2)

Arguments

df

a data frame containing positive or null values

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

Value

returns a list of class coa and dudi (see dudi) containing

N

the sum of all the values of the initial table

References

Benzécri, J.P. and Coll. (1973) L'analyse des données. II L'analyse des correspondances, Bordas, Paris. 1--620.

Greenacre, M. J. (1984) Theory and applications of correspondence analysis, Academic Press, London.

Author

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

Examples

data(rpjdl)
chisq.test(rpjdl$fau)$statistic
#> Warning: Chi-squared approximation may be incorrect
#> X-squared 
#>  7323.597 
rpjdl.coa <- dudi.coa(rpjdl$fau, scannf = FALSE, nf = 4)
sum(rpjdl.coa$eig)*rpjdl.coa$N # the same
#> [1] 7323.597

if(adegraphicsLoaded()) {
  g1 <- s.label(rpjdl.coa$co, plab.cex = 0.6, lab = rpjdl$frlab, plot = FALSE)
  g2 <- s.label(rpjdl.coa$li, plab.cex = 0.6, plot = FALSE)
  cbindADEg(g1, g2, plot = TRUE)
} else {
  par(mfrow = c(1,2))
  s.label(rpjdl.coa$co, clab = 0.6, lab = rpjdl$frlab)
  s.label(rpjdl.coa$li, clab = 0.6)
  par(mfrow = c(1,1))
}


data(bordeaux)
db <- dudi.coa(bordeaux, scan = FALSE)
db
#> Duality diagramm
#> class: coa dudi
#> $call: dudi.coa(df = bordeaux, scannf = FALSE)
#> 
#> $nf: 2 axis-components saved
#> $rank: 3
#> eigen values: 0.5906 0.1102 0.03109
#>   vector length mode    content       
#> 1 $cw    4      numeric column weights
#> 2 $lw    5      numeric row weights   
#> 3 $eig   3      numeric eigen values  
#> 
#>   data.frame nrow ncol content             
#> 1 $tab       5    4    modified array      
#> 2 $li        5    2    row coordinates     
#> 3 $l1        5    2    row normed scores   
#> 4 $co        4    2    column coordinates  
#> 5 $c1        4    2    column normed scores
#> other elements: N 
score(db)