## 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 class 'statis'
plot(x, xax = 1, yax = 2, option = 1:4, ...)
## S3 method for class 'statis'
print(x, ...)
```

### 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

### 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

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)