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vegan (version 1.15-3)

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.

Arguments

Value

  • A cca object has the following elements:
  • callthe function call.
  • colsum, rowsumColumn and row sums in cca. In rda, item colsum contains standard deviations of species and rowsum is NA.
  • grand.totalGrand total of community data in cca and NA in rda.
  • inertiaText used as the name of inertia.
  • methodText used as the name of the ordination method.
  • termsThe terms component of the formula. This is missing if the ordination was not called with formula.
  • terminfoFurther information on terms with three subitems: terms which is like the terms component above, but lists conditions and constrainst 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.chiTotal inertia or the sum of all eigenvalues.
  • pCCA, CCA, CAActual ordination results for conditioned (partial), constrained and unconstrained components of the model. Any of these can be NULL if there is no corresponding component. Items pCCA, CCA and CA have similar structure, and 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]

References

Legendre, P. and Legendre, L. (1998) Numerical Ecology. 2nd English ed. Elsevier.

See Also

The description here provides a hacker's interface. For more user friendly acces to the cca object see alias.cca, coef.cca, deviance.cca, predict.cca, scores.cca, summary.cca, vif.cca, weights.cca, spenvcor 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).

Examples

Run this code
# 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

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