crossval function

Cross-validation for predictive Co-Correspondence Analysis models

Performs a leave-one-out cross-validation of a predictive Co-Correspondence Analysis model.

crossval(y, x, n.axes = min(dim(x), dim(y)) - 1,
         centre = TRUE, verbose = TRUE)


## S3 method for class 'crossval'
summary(object, axes = c(1:min(6, object$n.axes)), ...)

Arguments

• y: the response species matrix.

• x: the predictor species matrix.

• n.axes: the number of axes to calculate the leave-one-out cross-validation for. Default is to perform the CV for all extractable axes.

• centre: centre y and x during analysis? Currently ignored as it may not be necessary.

• verbose: if TRUE, the default, print information on the progress of the cross-validation procedure.

• object: an object of class crossval as returned by crossval.

• axes: the number of axes to summarise results for.

• ...: further arguments to print - currently ignored.

Details

Performs a leave-one-out cross-validation of a predictive Co-Correspondence Analysis model. It can be slow depending on the number of columns in the matrices, and of course the number of sites.

Returns

Returns a large list with the following components: - dimx, dimy: the dimensions of the input matrices x and y respectively.

• press0: the $press_0$ statistic.

• n.axes: the number of axes tested.

• CVfit: the cross-validatory fit.

• varianceExp: list with components Yblock and Xblock containing the variances in the response and the predictor respectively, explained by each fitted PLS axis.

• totalVar: list with components Yblock and Xblock

  containing the total variance in the response and the predictor respectively.

• nam.dat: list with components namY and namX

  containing the names of the response and the predictor(s) respectively.

• call: the R call used.

Author(s)

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

Note

This function is not a bit out-of-date compared to some of the other functions. It should have a formula interface like coca or work on the results from coca, although that will have to be altered to store a copy of the data?

See Also

The model fitting function coca

Examples

## load the data sets
data(beetles)
data(plants)

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

## predictive CoCA using SIMPLS and formula interface
bp.pred <- coca(beetles ~ ., data = plants)
## should retain only the useful PLS components for a
## parsimonious model

## Leave-one-out crossvalidation - this takes a while
## Not run:

crossval(beetles, plants)
## End(Not run)

## so 2 axes are sufficient