scores.predcoca function

Get Species or Site Scores from an Ordination

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

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

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

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.

Details

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

Returns

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.

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

Author(s)

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

See Also

scores, for further details on the method.

Examples

## 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)