dudi: Duality Diagram

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

Description

as.dudi is called by many functions (dudi.pca, dudi.coa, dudi.acm, ...) and not directly by the user. It creates duality diagrams.

t.dudi returns an object of class 'dudi' where the rows are the columns and the columns are the rows of the initial dudi.

is.dudi returns TRUE if the object is of class dudi

redo.dudi computes again an analysis, eventually changing the number of kept axes. Used by other functions.

Usage

as.dudi(df, col.w, row.w, scannf, nf, call, type, tol = 1e-07, 
    full = FALSE) 
## S3 method for class 'dudi'
print(x, ...) 
is.dudi(x) 
redo.dudi(dudi, newnf = 2) 
## S3 method for class 'dudi'
t(x)
## S3 method for class 'dudi'
summary(object, ...) 
## S3 method for class 'dudi'
x[i,j]

Arguments

df	a data frame with n rows and p columns
col.w	a numeric vector containing the row weights
row.w	a numeric vector containing the column weights
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
call	generally match.call()
type	a string of characters : the returned list will be of class c(type, "dudi")
tol	a tolerance threshold for null eigenvalues (a value less than tol times the first one is considered as null)
full	a logical value indicating whether all non null eigenvalues should be kept
x, dudi, object	objects of class dudi
...	further arguments passed to or from other methods
newnf	an integer indicating the number of kept axes
i,j	elements to extract (integer or empty): index of rows (i) and columns (j)

Value

as.dudi and all the functions that use it return a list with the following components :

tab	a data frame with n rows and p columns
cw	column weights, a vector with n components
lw	row (lines) weights, a vector with p components
eig	eigenvalues, a vector with min(n,p) components
nf	integer, number of kept axes
c1	principal axes, data frame with p rows and nf columns
l1	principal components, data frame with n rows and nf columns
co	column coordinates, data frame with p rows and nf columns
li	row coordinates, data frame with n rows and nf columns
call	original call

Author(s)

Daniel Chessel
Anne B Dufour [e-mail]
Stephane Dray [e-mail]

References

Escoufier, Y. (1987) The duality diagram : a means of better practical applications In Development in numerical ecology, Legendre, P. & Legendre, L. (Eds.) NATO advanced Institute, Serie G. Springer Verlag, Berlin, 139–156.

Examples

data(deug)
dd1 <- dudi.pca(deug$tab, scannf = FALSE)
dd1
t(dd1)
is.dudi(dd1)
redo.dudi(dd1,3)
summary(dd1)

Example output

Duality diagramm
class: pca dudi
$call: dudi.pca(df = deug$tab, scannf = FALSE)

$nf: 2 axis-components saved
$rank: 9
eigen values: 3.101 1.363 1.032 0.9341 0.7398 ...
  vector length mode    content       
1 $cw    9      numeric column weights
2 $lw    104    numeric row weights   
3 $eig   9      numeric eigen values  

  data.frame nrow ncol content             
1 $tab       104  9    modified array      
2 $li        104  2    row coordinates     
3 $l1        104  2    row normed scores   
4 $co        9    2    column coordinates  
5 $c1        9    2    column normed scores
other elements: cent norm 
Duality diagramm
class: transpo dudi
$call: t.dudi(x = dd1)

$nf: 2 axis-components saved
$rank: 9
eigen values: 3.101 1.363 1.032 0.9341 0.7398 ...
  vector length mode    content       
1 $cw    104    numeric column weights
2 $lw    9      numeric row weights   
3 $eig   9      numeric eigen values  

  data.frame nrow ncol content             
1 $tab       9    104  modified array      
2 $li        9    2    row coordinates     
3 $l1        9    2    row normed scores   
4 $co        104  2    column coordinates  
5 $c1        104  2    column normed scores
other elements: NULL
[1] TRUE
Duality diagramm
class: pca dudi
$call: dudi.pca(df = deug$tab, scannf = FALSE, nf = 3)

$nf: 3 axis-components saved
$rank: 9
eigen values: 3.101 1.363 1.032 0.9341 0.7398 ...
  vector length mode    content       
1 $cw    9      numeric column weights
2 $lw    104    numeric row weights   
3 $eig   9      numeric eigen values  

  data.frame nrow ncol content             
1 $tab       104  9    modified array      
2 $li        104  3    row coordinates     
3 $l1        104  3    row normed scores   
4 $co        9    3    column coordinates  
5 $c1        9    3    column normed scores
other elements: cent norm 
Class: pca dudi
Call: dudi.pca(df = deug$tab, scannf = FALSE)

Total inertia: 9

Eigenvalues:
    Ax1     Ax2     Ax3     Ax4     Ax5 
 3.1014  1.3630  1.0323  0.9341  0.7398 

Projected inertia (%):
    Ax1     Ax2     Ax3     Ax4     Ax5 
 34.460  15.144  11.470  10.378   8.219 

Cumulative projected inertia (%):
    Ax1   Ax1:2   Ax1:3   Ax1:4   Ax1:5 
  34.46   49.60   61.07   71.45   79.67 

(Only 5 dimensions (out of 9) are shown)

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