cca {ade4}R Documentation

Canonical Correspondence Analysis

Description

Performs a Canonical Correspondence Analysis.

Usage

cca(sitspe, sitenv, scannf = TRUE, nf = 2)

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

Value

returns an object of class pcaiv. See pcaiv

Author(s)

Daniel Chessel
Anne B Dufour dufour@biomserv.univ-lyon1.fr

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)
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))
 

Worked out examples


> library(ade4)
> ### Name: cca
> ### Title: Canonical Correspondence Analysis
> ### Aliases: cca
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(rpjdl)
> millog <- log(rpjdl$mil + 1)
> iv1 <- cca(rpjdl$fau, millog, scan = FALSE)
> 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))
> 
> 
> 
> 

[Package ade4 Index]