evaluateCARTparallel {ofw}R Documentation

Error rate assessment of ofwCART

Description

The error rate assessment e.632+ (Efron and Tibshirani, 1997) is performed on ofwCART. This second version of evaluateCART allows to perform the learning step independently (for example with parallel computing).

Usage

evaluateCARTparallel(x, y, matTrain, matProb, maxvar=15, nvar=nlevels(y)+1,
         ntreeTest=100, weight=FALSE)

Arguments

x A data frame with continuous values.
y A response vector given as a factor (classification only).
matTrain A nsample by Bsample matrix indicating the training samples in each bootstrap sample.
matProb A nvariable by Bsample matrix for each probability distribution learnt.
maxvar Size of the evaluated variable selection.
nvar Number of randomly sampled variables in the selection that are used to construct each tree. Should be at least nlevels(y)+1 to ensure generalizable trees.
ntreeTest Number of trees aggregated to evaluate the performance of ofwCART when each variable enters the selection.
weight Should the weighting procedure be applied during the evaluation phase ?

Details

see evaluateCART

Value

An object of class evaluateCARTparallel, which is a list with the following components:

maxvar Size of the evaluated variable selection.
nvar Number of randomly sampled variables in the selection that are used to construct each tree.
weight.eval Was the weighting procedure applied during the evaluation step ?
ntreeTest Number of aggregated trees as each variable enters the selection.
matTrain A nsample by Bsample matrix indicating the training samples in each bootstrap sample.
matProb A nvariable by Bsample matrix for each probability distribution learnt.
error The evaluated e.632+ boostrap error as each variable enters the selection.
sampleWeight if weight = TRUE, a n by Bsample matrix indicating each sample weight in each bootstrap sample.
matPredInbag A nvariable by Bsample matrix indicating the prediction of the inbag samples.
matPredTest A nvariable by Bsample matrix indicating the prediction of the test samples.

Note

The e.632+ code comes from the ipred package from Thorsten.

This type of evaluation should only be used to compare several methods and not to assess the performance of only one method.

Author(s)

Kim-Anh L^e Cao Kim-Anh.Le-Cao@toulouse.inra.fr

Patrick Chabrier Patrick.Chabrier@toulouse.inra.fr.

References

Efron, B. and Tibshirani R.J. (1997), Improvements on cross-validation: the e.632+ bootstrap method, Journal of American Statistical Association 92, 548-560.

L^e Cao, K-A., Gonc calves, O., Besse, P. and Gadat, S. (2007), Selection of biologically relevant genes with a wrapper stochastic algorithm Statistical Applications in Genetics and Molecular Biology: Vol. 6: Iss.1, Article 29.

See Also

evaluate

Examples

## On data set "data"
##first learn ofwCART on Bsample bootstrap samples and store matTrain.data and matProb.data
##data.evalCARTparallel <- evaluateCARTparalell(data[,-1], as.factor(data[,1], matTrain=matTrain.data, matProb=matProb.data, ntreeTest=50, maxvar=10)
##plot(data.evalCARTparallel$error, type="l")

[Package ofw version 1.0-0 Index]