cocorresp (version 0.4-6)

scores-methods: Get Species or Site Scores from an Ordination

Description

Function to access either species or site scores for specified axes in co-correspondence analysis ordination methods.

Usage

# S3 method for predcoca
scores(x, choices = c(1, 2),
       display = c("sites","species"), ...)
# S3 method for symcoca
scores(x, choices = c(1, 2),
       display = c("sites","species"), scaling = FALSE, ...)

Value

A list with one or more components containing matrices of the requested scores:

species

A list with two components, Y and X, containing the species scores for the response matrix Y and the predictor matrix X respectively.

sites

A list with two components, Y and X, containing the site scores for the response matrix Y and the predictor matrix X respectively.

loadings

A list with two components, Y and X containing the loadings for the response and predictor matrix. For symcoca only.

xmatrix

The X matrix. For symcoca only.

Arguments

x

an ordination result

display

partial match to access scores for “sites” “species”, “loadings” or “xmatrix”. The latter two are only available for symcoca.

choices

numeric; the ordination axes to return.

scaling

logical; whether scores should be rescaled by the quarter root of the eigenvalues using rescale.symcoca.

...

arguments to be passed to other methods.

Author

Gavin L. Simpson, based on Matlab code by C.J.F. ter Braak and A.P. Schaffers.

Details

Implements a scores method for symmetric co-correspondence analysis ordination results.

References

ter Braak, C.J.F and Schaffers, A.P. (2004) Co-Correspondence Analysis: a new ordination method to relate two community compositions. Ecology 85(3), 834--846

See Also

scores, for further details on the method.

Examples

od <- options(digits = 4)
## load some data
data(beetles)
data(plants)

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

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

## extract the scores
scr <- scores(bp.sym)

## predictive CoCA using SIMPLS and formula interface
bp.pred <- coca(beetles ~ ., data = plants)
scr2 <- scores(bp.pred)

options(od)