wca.rlq: Within-Class RLQ analysis

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

Description

Performs a particular RLQ analysis where a partition of sites (rows of R) is taken into account. The within-class RLQ analysis search for linear combinations of traits and environmental variables of maximal covariance.

Usage

## S3 method for class 'rlq'
wca(x, fac, scannf = TRUE, nf = 2, ...)
## S3 method for class 'witrlq'
plot(x, xax = 1, yax = 2, ...)
## S3 method for class 'witrlq'
print(x, ...)

Arguments

x	an object of class rlq (created by the rlq function) for the wca.rlq function. An object of class witrlq for the print and plot functions
fac	a factor partitioning the rows of R
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
xax	the column number for the x-axis
yax	the column number for the y-axis
...	further arguments passed to or from other methods

Value

The wca.rlq function returns an object of class 'betrlq' (sub-class of 'dudi'). See the outputs of the print function for more details.

Author(s)

Stephane Dray [e-mail]

References

Wesuls, D., Oldeland, J. and Dray, S. (2012) Disentangling plant trait responses to livestock grazing from spatio-temporal variation: the partial RLQ approach. Journal of Vegetation Science, 23, 98–113.

See Also

rlq, wca, wca.rlq

Examples

data(piosphere)
afcL <- dudi.coa(log(piosphere$veg + 1), scannf = FALSE)
acpR <- dudi.pca(piosphere$env, scannf = FALSE, row.w = afcL$lw)
acpQ <- dudi.hillsmith(piosphere$traits, scannf = FALSE, row.w = afcL$cw)
rlq1 <- rlq(acpR, afcL, acpQ, scannf = FALSE)

wrlq1 <- wca(rlq1, fac = piosphere$habitat, scannf = FALSE)
wrlq1
plot(wrlq1)

Example output

Within RLQ analysis
call: wca.rlq(x = rlq1, fac = piosphere$habitat, scannf = FALSE)
class: witrlq dudi 

$rank (rank): 25
$nf (axis saved): 2

eigen values: 1.293 0.2569 0.087 0.0458 0.02769 ...

  vector length mode    content                    
1 $eig   25     numeric eigen values               
2 $lw    25     numeric row weigths (crossed array)
3 $cw    54     numeric col weigths (crossed array)

   data.frame nrow ncol content                          
1  $tab       25   54   crossed array (CA)               
2  $li        25   2    R col = CA row: coordinates      
3  $l1        25   2    R col = CA row: normed scores    
4  $co        54   2    Q col = CA column: coordinates   
5  $c1        54   2    Q col = CA column: normed scores 
6  $lR        378  2    row coordinates (R)              
7  $lsR       378  2    supplementary row coordinates (R)
8  $mR        378  2    normed row scores (R)            
9  $lQ        87   2    row coordinates (Q)              
10 $mQ        87   2    normed row scores (Q)            
11 $aR        2    2    axes onto within-RLQ axes (R)    
12 $aQ        2    2    axes onto within-RLQ axes (Q)    
13 $acR       2    2    RLQ axes onto within-RLQ axes (R)
14 $acQ       2    2    RLQ axes onto within-RLQ axes (Q)

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