```
library("MASS")
library("survival")
# Classification: Breast Cancer data
data("BreastCancer", package = "mlbench")
# Test set error bagging (nbagg = 50): 3.7% (Breiman, 1998, Table 5)
mod <- bagging(Class ~ Cl.thickness + Cell.size
+ Cell.shape + Marg.adhesion
+ Epith.c.size + Bare.nuclei
+ Bl.cromatin + Normal.nucleoli
+ Mitoses, data=BreastCancer, coob=TRUE)
print(mod)
# Test set error bagging (nbagg=50): 7.9% (Breiman, 1996a, Table 2)
data("Ionosphere", package = "mlbench")
Ionosphere$V2 <- NULL # constant within groups
bagging(Class ~ ., data=Ionosphere, coob=TRUE)
# Double-Bagging: combine LDA and classification trees
# predict returns the linear discriminant values, i.e. linear combinations
# of the original predictors
comb.lda <- list(list(model=lda, predict=function(obj, newdata)
predict(obj, newdata)$x))
# Note: out-of-bag estimator is not available in this situation, use
# errorest
mod <- bagging(Class ~ ., data=Ionosphere, comb=comb.lda)
predict(mod, Ionosphere[1:10,])
# Regression:
data("BostonHousing", package = "mlbench")
# Test set error (nbagg=25, trees pruned): 3.41 (Breiman, 1996a, Table 8)
mod <- bagging(medv ~ ., data=BostonHousing, coob=TRUE)
print(mod)
library("mlbench")
learn <- as.data.frame(mlbench.friedman1(200))
# Test set error (nbagg=25, trees pruned): 2.47 (Breiman, 1996a, Table 8)
mod <- bagging(y ~ ., data=learn, coob=TRUE)
print(mod)
# Survival data
# Brier score for censored data estimated by
# 10 times 10-fold cross-validation: 0.2 (Hothorn et al,
# 2002)
data("DLBCL", package = "ipred")
mod <- bagging(Surv(time,cens) ~ MGEc.1 + MGEc.2 + MGEc.3 + MGEc.4 + MGEc.5 +
MGEc.6 + MGEc.7 + MGEc.8 + MGEc.9 +
MGEc.10 + IPI, data=DLBCL, coob=TRUE)
print(mod)
```

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