| cca {ade4} | R Documentation |
Performs a Canonical Correspondence Analysis.
cca(sitspe, sitenv, scannf = TRUE, nf = 2) ## S3 method for class 'cca' summary(object, ...)
sitspe |
a data frame for correspondence analysis, typically a sites x species table |
sitenv |
a data frame containing variables, typically a sites x environmental variables table |
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 |
object |
an object of class |
... |
further arguments passed to or from other methods |
returns an object of class pcaiv. See pcaiv
Daniel Chessel
Anne B Dufour anne-beatrice.dufour@univ-lyon1.fr
Ter Braak, C. J. F. (1986) Canonical correspondence analysis : a new eigenvector technique for multivariate direct gradient analysis. Ecology, 67, 1167–1179.
Ter Braak, C. J. F. (1987) The analysis of vegetation-environment relationships by canonical correspondence analysis. Vegetatio, 69, 69–77.
Chessel, D., Lebreton J. D. and Yoccoz N. (1987) Propriétés de l'analyse canonique des correspondances. Une utilisation en hydrobiologie. Revue de Statistique Appliquée, 35, 55–72.
cca in the package vegan
data(rpjdl)
millog <- log(rpjdl$mil + 1)
iv1 <- cca(rpjdl$fau, millog, scan = FALSE)
if(adegraphicsLoaded()) {
G1 <- plot(iv1)
# analysis with c1 - as - li -ls
# projections of inertia axes on PCAIV axes
G2 <- s.corcircle(iv1$as)
# Species positions
g31 <- s.label(iv1$c1, xax = 2, yax = 1, plab.cex = 0.5, xlim = c(-4, 4), plot = F)
# Sites positions at the weighted mean of present species
g32 <- s.label(iv1$ls, xax = 2, yax = 1, plab.cex = 0, plot = F)
G3 <- superpose(g31, g32, plot = T)
# Prediction of the positions by regression on environmental variables
G4 <- s.match(iv1$ls, iv1$li, xax = 2, yax = 1, plab.cex = 0.5)
# analysis with fa - l1 - co -cor
# canonical weights giving unit variance combinations
G5 <- s.arrow(iv1$fa)
# sites position by environmental variables combinations
# position of species by averaging
g61 <- s.label(iv1$l1, xax = 2, yax = 1, plab.cex = 0, ppoi.cex = 1.5, plot = F)
g62 <- s.label(iv1$co, xax = 2, yax = 1, plot = F)
G6 <- superpose(g61, g62, plot = T)
G7 <- s.distri(iv1$l1, rpjdl$fau, xax = 2, yax = 1, ellipseSize = 0, starSize = 0.33)
# coherence between weights and correlations
g81 <- s.corcircle(iv1$cor, xax = 2, yax = 1, plot = F)
g82 <- s.arrow(iv1$fa, xax = 2, yax = 1, plot = F)
G8 <- cbindADEg(g81, g82, plot = T)
} else {
plot(iv1)
# analysis with c1 - as - li -ls
# projections of inertia axes on PCAIV axes
s.corcircle(iv1$as)
# Species positions
s.label(iv1$c1, 2, 1, clab = 0.5, xlim = c(-4, 4))
# Sites positions at the weighted mean of present species
s.label(iv1$ls, 2, 1, clab = 0, cpoi = 1, add.p = TRUE)
# Prediction of the positions by regression on environmental variables
s.match(iv1$ls, iv1$li, 2, 1, clab = 0.5)
# analysis with fa - l1 - co -cor
# canonical weights giving unit variance combinations
s.arrow(iv1$fa)
# sites position by environmental variables combinations
# position of species by averaging
s.label(iv1$l1, 2, 1, clab = 0, cpoi = 1.5)
s.label(iv1$co, 2, 1, add.plot = TRUE)
s.distri(iv1$l1, rpjdl$fau, 2, 1, cell = 0, csta = 0.33)
s.label(iv1$co, 2, 1, clab = 0.75, add.plot = TRUE)
# coherence between weights and correlations
par(mfrow = c(1, 2))
s.corcircle(iv1$cor, 2, 1)
s.arrow(iv1$fa, 2, 1)
par(mfrow = c(1, 1))
}