Within-Class RLQ analysis

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

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.

Author

Stéphane Dray stephane.dray@univ-lyon1.fr

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$habitat, scannf = FALSE)
wrlq1
#> Within RLQ analysis
#> call: wca.rlq(x = rlq1, fac = piosphere$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 $acQ       2    2    RLQ axes onto within-RLQ axes (Q)
#> 
plot(wrlq1)
#> Error in s.class(dfxy = wrlq1$lsR, fac = piosphere$habitat, xax = 1, yax = 2,     plot = FALSE, storeData = TRUE, pos = -3, psub = list(text = "R row scores and classes"),     plabels = list(cex = 1.25)): non convenient selection for dfxy (can not be converted to dataframe)