Duality Diagram
dudi.Rdas.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.
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
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.
Author
Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr
Stéphane Dray stephane.dray@univ-lyon1.fr
Examples
data(deug)
dd1 <- dudi.pca(deug$tab, scannf = FALSE)
dd1
#> 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
t(dd1)
#> 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
is.dudi(dd1)
#> [1] TRUE
redo.dudi(dd1,3)
#> 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 $c1 9 3 column normed scores
#> other elements: cent norm
summary(dd1)
#> Class: pca dudi
#> Call: dudi.pca(df = deug$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)
#>