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.cca object has the following elements:cca. In
rda, item colsum contains standard deviations of
species and rowsum is NA.cca and
NA in rda.terms component of the
formula. This is missing if the ordination was not called
with formula.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 ordiTerminfo, and it is needed in
predict.cca with newdata. This is missing if
the ordination was not called with formula.NULL if there is no corresponding
component.
Items pCCA, CCA and CA have similar
structure, and contain following items:
alias.cca does not access this item
directly, but it finds the aliased variables and their defining
equations from the item QR.}
CCA.}
CCA. Missing if the ordination was not
called with formula.}
CCA and CA.}
pCCA and in CCA.}
pCCA.}
qr.
The constrained ordination
algorithm is based on QR decomposition of constraints and
conditions (environmental data). The environmental data
are first centred in rda or weighted and centred in
cca. The QR decomposition is used in many functions that
access cca results, and it can be used to find many items
that are not directly stored in the object. For examples, see
coef.cca, coef.rda,
vif.cca, permutest.cca,
predict.cca, predict.rda,
calibrate.cca. For possible uses of this component,
see qr. In pCCA and CCA.}
cca object, but they
are made when the object is accessed with functions like
scores.cca, summary.cca or
plot.cca, or their rda variants. Only in
CCA and CA. In CCA component these are the
so-called linear combination scores. }
u scaled by eigenvalues. There is no guarantee
that any .eig variants of scores will be kept in the future
releases.}
na.action that lists the
omitted species. Only in CCA and CA.}
v weighted by eigenvalues.}
cca) or
weighted sums (rda) of
v with weights Xbar, but the multiplying effect of
eigenvalues removed. These often are known as WA scores in
cca. Only in CCA.}
CCA this is after possible pCCA or
after partialling out the effects of conditions, and in CA
after both pCCA and CCA. In cca the
standardization is Chi-square, and in rda centring
and optional scaling by species standard deviations using function
scale. }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.# 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$rowsum
csum <- mod$colsumRun the code above in your browser using DataLab