summary.predcoca {cocorresp} | R Documentation |
summary
methods for classes "predcoca"
and
"symcoca"
. These provide a summary of the main results of a
Co-Correspondence Analysis model.
## S3 method for class 'predcoca': summary(object, axes = c(1:min(6, object$n.axes)), display = c("species", "site"), ...) ## S3 method for class 'symcoca': summary(object, axes = c(1:min(6, object$n.axes)), display = c("species", "site"), scaling = 1, ...)
object |
an object of class "predcoca" or
"symcoca" . Generally the result of a call to coca . |
axes |
the number of CoCA axes to return in the result set. |
display |
one or both of "species" and/or "site" |
scaling |
for objects of class "symcoca" only, the scaling
to be applied to the results. One of "1" or "2" . See
below for details of scalings used. |
... |
arguments to be passed to other methods. |
A list with the some of the following components:
cocaScores |
The site and or species scores for the axes requested. |
call |
The call used to fit the model. |
lambda |
The eigenvalues for the axes requested. Not for
predcoca.simpls . |
namY, namX |
the names of the response and predictor either supplied by the user or derived from the original call. |
loadings |
a list with two components loadings1 and
loadings2 , which refer to the response and the predictor
matrices respectively. (Only for predictive CoCA models.) |
varianceExp |
a list with components Yblock and
Xblock containing the amount of variance explained on each
CoCA axis in the response and the predictor respectively. (Only for
predictive CoCA models.) |
totalVar |
a list with components Yblock and Xblock
containing the total variance in the response and the predictor data
sets respectively |
inertia |
a list with components total and residual
containing the total and residual inertia (variance) in the response
and the predictor matrices of a symmetric CoCA model. (Only for
symmetric CoCA models.) |
scaling |
the scaling used/requested. (Only for symmetric CoCA models.) |
Gavin L. Simpson
The model fitting function coca
## continue the example from coca(.) ## summary for symmetric CoCA bp.summ <- summary(bp.sym, axes = 1:4) bp.summ ## Different scaling bp.summ <- summary(bp.sym, axes = 1:4, scaling = 2) bp.summ ## summary for predictive CoCA bp.summ <- summary(bp.pred, axes = 1:2) bp.summ