capscale return similar result objects. Function
cca. This inheritance structure is due to
cca was the first of these implemented in
vegan. Hence the nomenclature in
cca. This help page describes the internal structure of the
cca object for programmers.
cca object has the following elements:
the function call.
Column and row sums in
Grand total of community data in
Text used as the name of inertia.
Text used as the name of the ordination method.
Further information on terms with three subitems:
Total inertia or the sum of all eigenvalues.
The result of
Actual ordination results for conditioned
(partial), constrained and unconstrained components of the
model. If constraints or conditions are not given, the
If the constraints had missing values or subsets, and
was set to
result will have some extra items:
subset evaluated as a logical vector
TRUE for included cases).
The object returned by
na.action which is a named vector of indices of
removed items. The class of the vector is either
"exclude" as set by
is applied after
subset so that the indices refer to the subset
A zombie vector of the length of
number of rows in the residual ordination. R versions before 2.13.0
may use this vector to find the number of valid observations,
and it is provided for their use although this is useless in R 2.13.0
and in vegan. Currently R uses
nobs.cca to find
the number of observations.
Row sums of removed observations. Only
The WA scores for sites (found from
community data) in constrained ordination if
na.exclude and the
scores could be calculated. The scores cannot be found for
capscale and in partial ordination.
Row scores for sites in unconstrained ordination with identical conditions as above.
capscale may add some items depending on its arguments:
The data set name if
metaMDSdist = TRUE.
Additive constant used if
add = TRUE.
Adjustment of dissimilarities: see
capscale, section “Notes”.
Legendre, P. and Legendre, L. (2012) Numerical Ecology. 3rd English ed. Elsevier.
The description here provides a hacker's interface. User
level functions for further analysis and handling of
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
variants of these functions.
You can use
as.mlm to cast a
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
# 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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