bagboost {boost}R Documentation

bagboost

Description

{An implementation of the BagBoost algorithm for binary classification}

Usage

bagboost(xlearn, ylearn, xtest, presel = 200, mfinal = 100, bag = 50)

Arguments

xlearn A (n x p)-matrix, where rows correspond to training instances and columns contain the predictor variables.
ylearn A vector of length n containing the class labels, which need to be coded by 0 and 1.
xtest A (m x p)-matrix, where rows correspond to test instances and columns contain the predictor variables.
presel An integer, giving the number of features to be pre-selected according to the Wilcoxon test statistic. Default is presel=200 features. If presel=0, no feature preselection is carried out.
mfinal An integer, the number of iterations for which boosting is run. Defaults to mfinal=100 iterations
bag An integer, the number of bagging steps that shall be done to obtain the weak learner. Defaults to bag=50 bagging iterations.

Value

The function outputs an array, whose rows contain out-of-sample probabilities that the class labels are predicted as being of class 1, for every boosting iteration.

Author(s)

Marcel Dettling

References

o
"Boosting for Tumor Classification with Gene Expression Data", Marcel Dettling and Peter B"uhlmann. Bioinformatics (2003), Vol. 19, p. 1061–1069.
o
"BagBoosting for Tumor Classification with Gene Expression Data", Marcel Dettling. To appear in Bioinformatics (2005).
o
Further information is available from the webpage http://stat.ethz.ch/~dettling

Examples

data(leukemia, package = "boost")

## Dividing the leukemia dataset into training and test data
xlearn <- leukemia.x[c(1:20, 34:38),]
ylearn <- leukemia.y[c(1:20, 34:38)]
xtest  <- leukemia.x[21:33,]
ytest  <- leukemia.y[21:33]

## Classification with bagboost
fit <- bagboost(xlearn, ylearn, xtest, presel=50, mfinal=20, bag=5)
summarize(fit, ytest)

[Package boost version 1.0-0 Index]