vegan (version 2.4-2)

cca.object: Result Object from Constrained Ordination with cca, rda or capscale

Description

Ordination methods cca, rda and capscale return similar result objects. Function capscale inherits from rda and rda inherits from cca. This inheritance structure is due to historic reasons: cca was the first of these implemented in vegan. Hence the nomenclature in cca.object reflects cca. This help page describes the internal structure of the cca object for programmers.

Value

A cca object has the following elements:

NA Action and Subset

If the constraints had missing values or subsets, and na.action was set to na.exclude or na.omit, the result will have some extra items:

capscale and dbrda

Vegan has two functions for distance-based Redundancy analysis: capscale and dbrda. Function capscale uses rda and returns its result object, but it may add some items depending on its arguments:

Function dbrda does not use rda but provides a parallel implementation for dissimilarities. Its result output is very similar to capscale described above with the following differences:

References

Legendre, P. and Legendre, L. (2012) Numerical Ecology. 3rd English ed. Elsevier.

See Also

The description here provides a hacker's interface. User level functions for further analysis and handling of cca objects are described in this section in cca. Also for a hacker interface, it may be better to use following low level functions to access the results: scores.cca (which also scales results), predict.cca (which can also use newdata), fitted.cca, residuals.cca, alias.cca, coef.cca, model.frame.cca, model.matrix.cca, deviance.cca, eigenvals.cca, RsquareAdj.cca, weights.cca, nobs.cca, or rda variants of these functions. You can use as.mlm to cast a cca.object into result of multiple response linear model (lm) in order to more easily find some statistics (which in principle could be directly found from the cca object as well).

This section in cca gives a more complete list of methods to handle the constrained ordination result object.

Examples

# Some species will be missing in the analysis, because only a subset
# of sites is used below.
data(dune)
data(dune.env)
mod <- cca(dune[1:15,] ~ ., dune.env[1:15,])
# Look at the names of missing species
attr(mod$CCA$v, "na.action")
# Look at the names of the aliased variables:
mod$CCA$alias
# Access directly constrained weighted orthonormal species and site
# scores, constrained eigenvalues and margin sums.
spec <- mod$CCA$v
sites <- mod$CCA$u
eig <- mod$CCA$eig
rsum <- mod$rowsum
csum <- mod$colsum