crossval: Cross-validation for predictive Co-Correspondence Analysis...

In cocorresp: Co-Correspondence Analysis Methods

Cross-validation for predictive Co-Correspondence Analysis models

Description

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

Usage

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 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.

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.

Value

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.

Note

This function is not a bit out-of-date compared to some of the other functions.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?

Author(s)

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

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

cocorresp documentation built on Oct. 7, 2024, 1:08 a.m.