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, 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. Any of these can be NULL if there is no corresponding
component.
Items pCCA , CCA and CA have similar
structure, and contain following items:
|
If the constraints had missing values, and na.action
was set to na.exclude
or na.omit
, the
result will have some extra items:
na.action
na.action
which is a named vector of indices of
removed items. The class of the vector is either "omit"
or
"exclude"
as set by na.action
.residuals.zombie
rowsum.excluded
cca
.CCA$wa.excluded
na.action
was na.exclude
and the
scores could be calculated. The scores cannot be found for
capscale
and in partial ordination.CA$u.excluded
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 access 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