| bca.rlq {ade4} | R Documentation |
Performs a particular RLQ analysis where a partition of sites (rows of R) is taken into account. The between-class RLQ analysis search for linear combinations of traits and environmental variables maximizing the covariances between the traits and the average environmental conditions of classes.
## S3 method for class 'rlq' bca(x, fac, scannf = TRUE, nf = 2, ...) ## S3 method for class 'betrlq' plot(x, xax = 1, yax = 2, ...) ## S3 method for class 'betrlq' print(x, ...)
x |
an object of class rlq (created by the |
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 |
The bca.rlq function returns an object of class 'betrlq'
(sub-class of 'dudi'). See the outputs of the print function
for more details.
Stephane Dray stephane.dray@univ-lyon1.fr
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.
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$cw) rlq1 <- rlq(acpR, afcL, acpQ, scannf = FALSE) brlq1 <- bca(rlq1, fac = piosphere$habitat, scannf = FALSE) brlq1 plot(brlq1)