crossval: Cross-validation for predictive Co-Correspondence Analysis...
In cocorresp: Co-correspondence analysis methods

Description Usage Arguments Details Value Note Author(s) See Also Examples

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)), ...)

`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 neccessary.
`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.

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 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.

This function is not a bit out-of-date compared to some of the other functions.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

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

The model fitting function coca

## 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