statis: STATIS, a method for analysing K-tables

In ade4: Analysis of Ecological Data : Exploratory and Euclidean Methods in Environmental Sciences

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

Author(s)

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

Example output

Warning messages:
1: In scalewt(w) : Variables with null variance not standardized.
2: In scalewt(w) : Variables with null variance not standardized.
3: In scalewt(w) : Variables with null variance not standardized.
STATIS Analysis
class:statis 
table number: 12 
row number: 19   total column number: 92 

     **** Interstructure ****

eigen values: 5.337 1.525 1.294 1.037 0.6419 ...
 $RV       matrix       12      12     RV coefficients
 $RV.eig   vector       12       eigenvalues
 $RV.coo   data.frame   12      4    array scores
 $tab.names    vector       12        array names
 $RV.tabw  vector       12      array weigths

RV coefficient
            Allaine    Audeux    Clauge  Cuisance  Cusancin  Dessoubre
Allaine   1.0000000                                                   
Audeux    0.3923156 1.0000000                                         
Clauge    0.4142577 0.2568859 1.0000000                               
Cuisance  0.4881191 0.3045202 0.4934249 1.0000000                     
Cusancin  0.6750590 0.4465916 0.2351574 0.5962413 1.0000000           
Dessoubre 0.4264883 0.7460391 0.2912210 0.4596960 0.4098816 1.00000000
Doubs     0.4162722 0.5275275 0.4599651 0.4196404 0.2648507 0.55605542
Doulonnes 0.2401718 0.3781006 0.3310817 0.5445763 0.2987118 0.40145726
Drugeon   0.3301627 0.0999847 0.5153033 0.2209572 0.1435757 0.09888345
Furieuse  0.3844109 0.3291450 0.3259230 0.7768327 0.6693345 0.40898890
Lison     0.2312130 0.3968212 0.2895775 0.6310371 0.3982919 0.48144475
Loue      0.3872305 0.1128074 0.5192117 0.6487130 0.3069628 0.20311166
              Doubs Doulonnes    Drugeon  Furieuse     Lison Loue
Allaine                                                          
Audeux                                                           
Clauge                                                           
Cuisance                                                         
Cusancin                                                         
Dessoubre                                                        
Doubs     1.0000000                                              
Doulonnes 0.2183520 1.0000000                                    
Drugeon   0.4214976 0.0671516 1.00000000                         
Furieuse  0.2782714 0.4748992 0.18165914 1.0000000               
Lison     0.4181009 0.5346002 0.07442131 0.5209446 1.0000000     
Loue      0.4396741 0.3005171 0.31081167 0.3862660 0.3597613    1

      **** Compromise ****

eigen values: 2.012 0.903 0.5025 0.3003 0.2282 ...

 $nf: 3 axis-components saved
 $rank: 19 
 data.frame nrow ncol content                       
 $C.li      19   3    row coordinates               
 $C.Co      92   3    column coordinates            
 $C.T4      48   3    principal vectors (each table)
 $TL        228  2    factors (not used)            
 $TC        92   2    factors for Co                
 $T4        48   2    factors for T4                

ade4 documentation built on May 2, 2019, 5:50 p.m.