adabag (version 5.0)

predict.bagging: Predicts from a fitted bagging object

Description

Classifies a dataframe using a fitted bagging object.

Usage

# S3 method for bagging
predict(object, newdata, newmfinal=length(object$trees), ...)

Value

An object of class predict.bagging, which is a list with the following components:

formula

the formula used.

votes

a matrix describing, for each observation, the number of trees that assigned it to each class.

prob

a matrix describing, for each observation, the posterior probability or degree of support of each class. These probabilities are calculated using the proportion of votes in the final ensemble.

class

the class predicted by the ensemble classifier.

confusion

the confusion matrix which compares the real class with the predicted one.

error

returns the average error.

Arguments

object

fitted model object of class bagging. This is assumed to be the result of some function that produces an object with the same named components as that returned by the bagging function.

newdata

data frame containing the values at which predictions are required. The predictors referred to in the right side of formula(object) must be present by name in newdata.

newmfinal

The number of trees of the bagging object to be used in the prediction. This argument allows the user to prune the ensemble. By default all the trees in the bagging object are used

...

further arguments passed to or from other methods.

Author

Esteban Alfaro-Cortes , Matias Gamez-Martinez and Noelia Garcia-Rubio

References

Alfaro, E., Gamez, M. and Garcia, N. (2013): ``adabag: An R Package for Classification with Boosting and Bagging''. Journal of Statistical Software, Vol 54, 2, pp. 1--35.

Alfaro, E., Garcia, N., Gamez, M. and Elizondo, D. (2008): ``Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks''. Decision Support Systems, 45, pp. 110--122.

Breiman, L. (1996): "Bagging predictors". Machine Learning, Vol 24, 2, pp. 123--140.

Breiman, L. (1998). "Arcing classifiers". The Annals of Statistics, Vol 26, 3, pp. 801--849.

See Also

bagging, bagging.cv

Examples

#library(rpart)
#data(iris)
#sub <- c(sample(1:50, 25), sample(51:100, 25), sample(101:150, 25))
#iris.bagging <- bagging(Species ~ ., data=iris[sub,], mfinal=5)
#iris.predbagging<- predict.bagging(iris.bagging, newdata=iris[-sub,])
#iris.predbagging

## rpart and mlbench libraries should be loaded
library(rpart)
library(mlbench)
data(BreastCancer)
l <- length(BreastCancer[,1])
sub <- sample(1:l,2*l/3)
BC.bagging <- bagging(Class ~.,data=BreastCancer[,-1],mfinal=5, 
control=rpart.control(maxdepth=3))
BC.bagging.pred <- predict.bagging(BC.bagging,newdata=BreastCancer[-sub,-1])
BC.bagging.pred$prob
BC.bagging.pred$confusion
BC.bagging.pred$error