witwit.coa {ade4}R Documentation

Internal Correspondence Analysis

Description

witwit.coa performs an Internal Correspondence Analysis. witwitsepan gives the computation and the barplot of the eigenvalues for each separated analysis in an Internal Correspondence Analysis.

Usage

witwit.coa(dudi, row.blocks, col.blocks, scannf = TRUE, nf = 2)
## S3 method for class 'witwit':
summary(object, ...)
witwitsepan(ww, mfrow = NULL, csub = 2, plot = TRUE)

Arguments



dudi an object of class coa
row.blocks a numeric vector indicating the row numbers for each block of rows
col.blocks a numeric vector indicating the column numbers for each block of columns
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
object an object of class witwit
... further arguments passed to or from other methods
ww an object of class witwit
mfrow a vector of the form "c(nr,nc)", otherwise computed by a special own function 'n2mfrow'
csub a character size for the sub-titles, used with par("cex")*csub
plot if FALSE, numeric results are returned

Value

returns a list of class witwit, coa and dudi (see as.dudi) containing
rbvar a data frame with the within variances of the rows of the factorial coordinates
lbw a data frame with the marginal weighting of the row classes
cvar a data frame with the within variances of the columns of the factorial coordinates
cbw a data frame with the marginal weighting of the column classes

Author(s)

Daniel Chessel Anne B Dufour dufour@biomserv.univ-lyon1.fr Correction by Campo Elías PARDO cepardot@cable.net.co

References

Cazes, P., Chessel, D. and Dolédec, S. (1988) L'analyse des correspondances internes d'un tableau partitionné : son usage en hydrobiologie. Revue de Statistique Appliquée, 36, 39–54.

Examples

data(ardeche)
coa1 <- dudi.coa(ardeche$tab, scann = FALSE, nf = 4)
ww <- witwit.coa(coa1, ardeche$row.blocks, ardeche$col.blocks, scann = FALSE)
ww
s.class(ww$co, ardeche$sta.fac, clab = 1.5, cell = 0, axesell = FALSE)
s.label(ww$co, add.p = TRUE, clab = 0.75)
summary(ww)

witwitsepan(ww, c(4,6))

Worked out examples


> library(ade4)
> ### Name: witwit.coa
> ### Title: Internal Correspondence Analysis
> ### Aliases: witwit.coa summary.witwit witwitsepan
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(ardeche)
> coa1 <- dudi.coa(ardeche$tab, scann = FALSE, nf = 4)
> ww <- witwit.coa(coa1, ardeche$row.blocks, ardeche$col.blocks, scann = FALSE)
> ww
Duality diagramm
class: witwit coa dudi
$call: witwit.coa(dudi = coa1, row.blocks = ardeche$row.blocks, col.blocks = ardeche$col.blocks, 
    scannf = FALSE)

$nf: 2 axis-components saved
$rank: 29
eigen values: 0.06858 0.06325 0.04254 0.03566 0.02911 ...
  vector length mode    content       
1 $cw    35     numeric column weights
2 $lw    43     numeric row weights   
3 $eig   29     numeric eigen values  

  data.frame nrow ncol content             
1 $tab       43   35   modified array      
2 $li        43   2    row coordinates     
3 $l1        43   2    row normed scores   
4 $co        35   2    column coordinates  
5 $c1        35   2    column normed scores
other elements: lbvar lbw cbvar cbw 
> s.class(ww$co, ardeche$sta.fac, clab = 1.5, cell = 0, axesell = FALSE)
> s.label(ww$co, add.p = TRUE, clab = 0.75)
> summary(ww)
Internal correspondence analysis
class: witwit coa dudi
$call: witwit.coa(dudi = coa1, row.blocks = ardeche$row.blocks, col.blocks = ardeche$col.blocks, 
    scannf = FALSE)
2 axis-components saved
eigen values: 0.06858 0.06325 0.04254 0.03566 0.02911 ...

Eigen value decomposition among row blocks
     Axis1  Axis2  weights
Eph  0.0511 0.0563 0.2879 
Ple  0.1154 0.0263 0.0653 
Col  0.0204 0.0709 0.3703 
Tri  0.1403 0.069  0.2766 
mean 0.0686 0.0633        

    Axis1 Axis2
Eph 214   256  
Ple 110   27   
Col 110   415  
Tri 566   302  
sum 1000  1000 

Eigen value decomposition among column blocks
      Comp1  Comp2  weights
jul82 0.0109 0.0706 0.1859 
aug82 0.0413 0.1064 0.1797 
nov82 0.017  7e-04  0.1054 
feb83 0.1916 0.032  0.1364 
apr83 0.1385 0.0613 0.1895 
jul83 0.0243 0.0736 0.2031 
mean  0.0686 0.0633        

      Comp1 Comp2
jul82 29    207  
aug82 108   302  
nov82 26    1    
feb83 381   69   
apr83 383   184  
jul83 72    236  
sum   1000  1000 

> 
> witwitsepan(ww, c(4,6))
> 
> 
> 
> 

[Package ade4 Index]