cca: Canonical Correspondence Analysis

Description

Performs a Canonical Correspondence Analysis.

Usage

cca(sitspe, sitenv, scannf = TRUE, nf = 2)
# S3 method for cca
summary(object, …)

Arguments

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

if scannf FALSE, an integer indicating the number of kept axes

object

an object of class cca

…

further arguments passed to or from other methods

Value

returns an object of class pcaiv. See pcaiv

References

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) Propri<U+00E9>t<U+00E9>s de l'analyse canonique des correspondances. Une utilisation en hydrobiologie. Revue de Statistique Appliqu<U+00E9>e, 35, 55--72.

Examples

Run this code

# NOT RUN {
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$c1, xax = 2, yax = 1, plab.cex = 0.5, xlim = c(-4, 4), plot = FALSE)
  # Sites positions at the weighted mean of present species
  g32 <- s.label(iv1$ls, xax = 2, yax = 1, plab.cex = 0, plot = FALSE)
  G3 <- superpose(g31, g32, plot = TRUE)
  
  # 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$l1, xax = 2, yax = 1, plab.cex = 0, ppoi.cex = 1.5, plot = FALSE)
  g62 <- s.label(iv1$co, xax = 2, yax = 1, plot = FALSE)
  G6 <- superpose(g61, g62, plot = TRUE)
  
  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$cor, xax = 2, yax = 1, plot = FALSE)
  g82 <- s.arrow(iv1$fa, xax = 2, yax = 1, plot = FALSE)
  G8 <- cbindADEg(g81, g82, plot = TRUE)

} 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))
}
# }