| witwit.coa {ade4} | R Documentation |
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.
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)
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 |
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 |
Daniel Chessel Anne B Dufour dufour@biomserv.univ-lyon1.fr Correction by Campo Elías PARDO cepardot@cable.net.co
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.
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))
> 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))
> > > >