statis {ade4}R Documentation

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
nf an integer indicating the number of kept axes
rank an integer indicating the rank of the studied matrix
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$poi, scann = FALSE, scal = FALSE)
wit1 <- within(dudi1, jv73$fac.riv, scann = FALSE)
kta3 <- ktab.within(wit1)
data(jv73)
statis3 <- statis(kta3, scann = FALSE)
plot(statis3)

s.arrow(statis3$C.li, cgrid = 0)

kplot(statis3, traj = TRUE, arrow = FALSE, unique = TRUE, 
    clab = 0, csub = 3, cpoi = 3)
statis3

Worked out examples


> library(ade4)
> ### Name: statis
> ### Title: STATIS, a method for analysing K-tables
> ### Aliases: statis print.statis plot.statis
> ### Keywords: multivariate
> 
> ### ** 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$poi, scann = FALSE, scal = FALSE)
> wit1 <- within(dudi1, jv73$fac.riv, scann = FALSE)
> kta3 <- ktab.within(wit1)
> data(jv73)
> statis3 <- statis(kta3, scann = FALSE)
> plot(statis3)

> 
> s.arrow(statis3$C.li, cgrid = 0)

> 
> kplot(statis3, traj = TRUE, arrow = FALSE, unique = TRUE, 
+     clab = 0, csub = 3, cpoi = 3)
> statis3
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
              Doubs    Drugeon Dessoubre   Allaine    Audeux  Cusancin
Doubs     1.0000000                                                   
Drugeon   0.4214976 1.00000000                                        
Dessoubre 0.5560554 0.09888345 1.0000000                              
Allaine   0.4162722 0.33016274 0.4264883 1.0000000                    
Audeux    0.5275275 0.09998470 0.7460391 0.3923156 1.0000000          
Cusancin  0.2648507 0.14357569 0.4098816 0.6750590 0.4465916 1.0000000
Loue      0.4396741 0.31081167 0.2031117 0.3872305 0.1128074 0.3069628
Lison     0.4181009 0.07442131 0.4814447 0.2312130 0.3968212 0.3982919
Furieuse  0.2782714 0.18165914 0.4089889 0.3844109 0.3291450 0.6693345
Cuisance  0.4196404 0.22095719 0.4596960 0.4881191 0.3045202 0.5962413
Doulonnes 0.2183520 0.06715160 0.4014573 0.2401718 0.3781006 0.2987118
Clauge    0.4599651 0.51530332 0.2912210 0.4142577 0.2568859 0.2351574
               Loue     Lison  Furieuse  Cuisance Doulonnes Clauge
Doubs                                                             
Drugeon                                                           
Dessoubre                                                         
Allaine                                                           
Audeux                                                            
Cusancin                                                          
Loue      1.0000000                                               
Lison     0.3597613 1.0000000                                     
Furieuse  0.3862660 0.5209446 1.0000000                           
Cuisance  0.6487130 0.6310371 0.7768327 1.0000000                 
Doulonnes 0.3005171 0.5346002 0.4748992 0.5445763 1.0000000       
Clauge    0.5192117 0.2895775 0.3259230 0.4934249 0.3310817      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                

> 
> 
> 
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[Package ade4 Index]