summary-methods: Summarizing Co-CA Model Fits

Description

summary methods for classes "predcoca" and "symcoca". These provide a summary of the main results of a Co-Correspondence Analysis model.

Usage

# S3 method for predcoca
summary(object, axes = NULL, ...)
# S3 method for symcoca
summary(object, ...)

Value

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.)

Arguments

object: an object of class "predcoca" or "symcoca". Generally the result of a call to coca.
axes: numeric; how many axes to summarise? The default is to display 6 axes or all available axes, whichever is the smaller.
...: arguments to be passed to other methods.

Author

Gavin L. Simpson

Examples

Run this code

od <- options(digits = 4)
## symmetric CoCA
data(beetles)
data(plants)

## log transform the bettle data
beetles <- log(beetles + 1)

## fit the model
bp.sym <- coca(beetles ~ ., data = plants, method = "symmetric")
summary(bp.sym)

## Predictive CoCA
bp.pred <- coca(beetles ~ ., data = plants)
summary(bp.pred, axes = 1:2)
options(od)