ade4 (version 1.7-22)

statis: STATIS, a method for analysing K-tables

Description

performs a STATIS analysis of a ktab object.

Usage

statis(X, scannf = TRUE, nf = 3, tol = 1e-07)
# S3 method for statis
plot(x, xax = 1, yax = 2, option = 1:4, ...) 
# S3 method for statis
print(x, ...)

Value

statis returns a list of class 'statis' containing :

RV

a matrix with the all RV coefficients

RV.eig

a numeric vector with all the eigenvalues

RV.coo

a data frame with the array scores

tab.names

a vector of characters with the names of the arrays

RV.tabw

a numeric vector with the array weigths

C.nf

an integer indicating the number of kept axes

C.rank

an integer indicating the rank of the analysis

C.li

a data frame with the row coordinates

C.Co

a data frame with the column coordinates

C.T4

a data frame with the principal vectors (for each table)

TL

a data frame with the factors (not used)

TC

a data frame with the factors for Co

T4

a data frame with the factors for T4

Arguments

X

an object of class 'ktab'

scannf

a logical value indicating whether the number of kept axes for the compromise should be asked

nf

if scannf FALSE, an integer indicating the number of kept axes for the compromise

tol

a tolerance threshold to test whether the distance matrix is Euclidean : an eigenvalue is considered positive if it is larger than -tol*lambda1 where lambda1 is the largest eigenvalue

x

an object of class 'statis'

xax, yax

the numbers of the x-axis and the y-axis

option

an integer between 1 and 4, otherwise the 4 components of the plot are dispayed

...

further arguments passed to or from other methods

Author

Daniel Chessel

References

Lavit, C. (1988) Analyse conjointe de tableaux quantitatifs, Masson, Paris.

Lavit, C., Escoufier, Y., Sabatier, R. and Traissac, P. (1994) The ACT (Statis method). Computational Statistics and Data Analysis, 18, 97--119.

Examples

data(jv73)
kta1 <- ktab.within(withinpca(jv73$morpho, jv73$fac.riv, scann = FALSE))
statis1 <- statis(kta1, scann = FALSE)
plot(statis1)

dudi1 <- dudi.pca(jv73$poi, scann = FALSE, scal = FALSE)
wit1 <- wca(dudi1, jv73$fac.riv, scann = FALSE)
kta3 <- ktab.within(wit1)
data(jv73)
statis3 <- statis(kta3, scann = FALSE)
plot(statis3)

if(adegraphicsLoaded()) {
  s.arrow(statis3$C.li, pgrid.text.cex = 0)
  kplot(statis3, traj = TRUE, arrow = FALSE, plab.cex = 0, psub.cex = 3, ppoi.cex = 3)
} else {
  s.arrow(statis3$C.li, cgrid = 0)
  kplot(statis3, traj = TRUE, arrow = FALSE, unique = TRUE, 
    clab = 0, csub = 3, cpoi = 3)
}

statis3