cca: Canonical Correspondence Analysis

In ade4: Analysis of Ecological Data : Exploratory and Euclidean Methods in Environmental Sciences

Description

Performs a Canonical Correspondence Analysis.

Usage

cca(sitspe, sitenv, scannf = TRUE, nf = 2)
## S3 method for class '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
nf	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

Author(s)

Daniel Chessel
Anne B Dufour [e-mail]

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) Propriétés de l'analyse canonique des correspondances. Une utilisation en hydrobiologie. Revue de Statistique Appliquée, 35, 55–72.

See Also

cca in the package vegan

Examples

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))
}

ade4 documentation built on May 2, 2019, 5:50 p.m.