cca.object {vegan} | R Documentation |
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.
A cca
object has the following elements:
call |
the function call. |
colsum, rowsum |
Column and row sums in cca . In
rda , item colsum contains standard deviations of
species and rowsum is NA . |
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 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.chi |
Total inertia or the sum of all eigenvalues. |
pCCA, CCA, CA |
Actual 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:
alias alias.cca does not access this item
directly, but it finds the aliased variables and their defining
equations from the QR item.biplot CCA .CCA . Missing if the ordination was not
called with formula .eig CCA and CA .envcentre pCCA and in CCA .Fit pCCA .QR 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 .rank qrank pCCA and
CCA components. Only in CCA .tot.chi u 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 the CCA component these are
the so-called linear combination scores. u.eig u scaled by eigenvalues. There is no
guarantee that any .eig variants of scores will be kept in
the future releases.v na.action that lists the
omitted species. Only in CCA and CA .v.eig v weighted by eigenvalues.wa 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 .wa.eig Xbar 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 . |
Jari Oksanen
Legendre, P. and Legendre, L. (1998) Numerical Ecology. 2nd English ed. Elsevier.
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).
# 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