vegan (version 2.0-10)

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:
• call
  the function call.
• colsum, rowsum, rowsum.excluded
  Column and row sums in cca. In rda, item colsum contains standard deviations of species and rowsum is NA. If some data were removed in na.action, the row sums of excluded observations are in item rowsum.excluded in cca (but not in rda). The rowsum.excluded add to the total (one) of rowsum.
• grand.total
  Grand total of community data in cca and NA in rda.
• inertia
  Text used as the name of inertia.
• method
  Text used as the name of the ordination method.
• terms
  The terms component of the formula. This is missing if the ordination was not called with formula.
• terminfo
  Further information on terms with three subitems: terms which is like the terms component above, but lists conditions and constraints similarly; xlev which lists the factor levels, and ordered which is TRUE to ordered factors. This is produced by vegan internal function ordiTerminfo, and it is needed in predict.cca with newdata. This is missing if the ordination was not called with formula.
• tot.chi
  Total inertia or the sum of all eigenvalues.
• na.action
  The result of na.action if missing values in constraints were handled by na.omit or na.exclude (or NULL if there were no missing values). This is a vector of indices of missing value rows in the original data and a class of the action, usually either "omit" or "exclude".
• pCCA, CCA, CA
  Actual ordination results for conditioned (partial), constrained and unconstrained components of the model. If constraints or conditions are not given, the corresponding components CCA and pCCA are NULL. If they are specified but have zero rank and zero eigenvalue (e.g., due to aliasing), they have a standard structure like described below, but the result scores have zero columns, but the correct number of rows. The residual component is never NULL, and if there is no residual variation (like in overdefined model), its scores have zero columns. The standard print command does not show NULL components, but it prints zeros for zeroed components. Items pCCA, CCA and CA contain following items:

  [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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