data(aravo)
dim(aravo$spe)
dim(aravo$traits)
dim(aravo$env)
# Preliminary analyses: CA, Hill-Smith and PCA
afcL.aravo <- dudi.coa(aravo$spe, scannf = FALSE)
acpR.aravo <- dudi.hillsmith(aravo$env,
row.w = afcL.aravo$lw,
scannf = FALSE)
acpQ.aravo <- dudi.pca(aravo$traits,
row.w = afcL.aravo$cw,
scannf = FALSE)
# RLQ analyses
rlq.aravo <- rlq(
dudiR = acpR.aravo,
dudiL = afcL.aravo,
dudiQ = acpQ.aravo,
scannf = FALSE)
plot(rlq.aravo)
# Traits by environment crossed table
rlq.aravo$tab
# Since the plots are crowded, one can plot them one by one
# in large graphical windows.
# Site (L) scores:
s.label(rlq.aravo$lR,
plabels.boxes.draw = FALSE,
ppoints.alpha = 0
)
# Species (Q) scores
s.label(rlq.aravo$lQ,
plabels.boxes.draw = FALSE,
ppoints.alpha = 0
)
# Environmental variables:
s.arrow(rlq.aravo$l1)
# Species traits:
s.arrow(rlq.aravo$c1)
# Global test:
randtest(rlq.aravo, nrepet = 999, modeltype = 6)
Hint Note that among the preliminary analyses, the CA uses unequal site weights. In
the two other dudi.xxx() calls, the CA site weights (afcL.aravo$lw) are
attributed to the other (Hill-Smith and PCA) analyses.
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6 Canonical Ordination