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 chessel@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))

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