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

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## S3 method for class 'predcoca'
summary(object, axes = c(1:min(6, object$n.axes)),
         display = c("species", "sites"), ...)

## S3 method for class 'symcoca'
summary(object, axes = c(1:min(6, object$n.axes)),
        display = c("species", "sites"), scaling = 1, ...)

Arguments

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 "sites"

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.

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.)

Author(s)

Gavin L. Simpson

See Also

The model fitting function coca

Examples

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## 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, axes = 1:4)

## Different scaling
summary(bp.sym, axes = 1:4, scaling = 2)

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