bagging {ipred} | R Documentation |
Bagging for classification, regression and survival trees.
## S3 method for class 'factor': ipredbagg(y, X=NULL, nbagg=25, control= rpart.control(minsplit=2, cp=0, xval=0), comb=NULL, coob=FALSE, ns=length(y), keepX = TRUE, ...) ## S3 method for class 'numeric': ipredbagg(y, X=NULL, nbagg=25, control=rpart.control(xval=0), comb=NULL, coob=FALSE, ns=length(y), keepX = TRUE, ...) ## S3 method for class 'Surv': ipredbagg(y, X=NULL, nbagg=25, control=rpart.control(xval=0), comb=NULL, coob=FALSE, ns=dim(y)[1], keepX = TRUE, ...) ## S3 method for class 'data.frame': bagging(formula, data, subset, na.action=na.rpart, ...)
y |
the response variable: either a factor vector of class labels
(bagging classification trees), a vector of numerical values
(bagging regression trees) or an object of class
Surv (bagging survival trees). |
X |
a data frame of predictor variables. |
nbagg |
an integer giving the number of bootstrap replications. |
coob |
a logical indicating whether an out-of-bag estimate of the
error rate (misclassification error, root mean squared error
or Brier score) should be computed.
See predict.classbagg for
details. |
control |
options that control details of the rpart
algorithm, see rpart.control . It is
wise to set xval = 0 in order to save computing
time. Note that the
default values depend on the class of y . |
comb |
a list of additional models for model combination, see below
for some examples. Note that argument method for double-bagging is no longer there,
comb is much more flexible. |
ns |
number of sample to draw from the learning sample. By default,
the usual bootstrap n out of n with replacement is performed.
If ns is smaller than length(y) , subagging
(Buehlmann and Yu, 2002), i.e. sampling ns out of
length(y) without replacement, is performed. |
keepX |
a logical indicating whether the data frame of predictors
should be returned. Note that the computation of the
out-of-bag estimator requires keepX=TRUE . |
formula |
a formula of the form lhs ~ rhs where lhs
is the response variable and rhs a set of
predictors. |
data |
optional data frame containing the variables in the model formula. |
subset |
optional vector specifying a subset of observations to be used. |
na.action |
function which indicates what should happen when
the data contain NA s. Defaults to
na.rpart . |
... |
additional parameters passed to ipredbagg or
rpart , respectively. |
Bagging for classification and regression trees were suggested by Breiman (1996a, 1998) in order to stabilise trees.
The trees in this function are computed using the implementation in the
rpart
package. The generic function ipredbagg
implements methods for different responses. If y
is a factor,
classification trees are constructed. For numerical vectors
y
, regression trees are aggregated and if y
is a survival
object, bagging survival trees (Hothorn et al, 2003) is performed.
The function bagging
offers a formula based interface to
ipredbagg
.
nbagg
bootstrap samples are drawn and a tree is constructed
for each of them. There is no general rule when to stop the tree
growing. The size of the
trees can be controlled by control
argument
or prune.classbagg
. By
default, classification trees are as large as possible whereas regression
trees and survival trees are build with the standard options of
rpart.control
. If nbagg=1
, one single tree is
computed for the whole learning sample without bootstrapping.
If coob
is TRUE, the out-of-bag sample (Breiman,
1996b) is used to estimate the prediction error
corresponding to class(y)
. Alternatively, the out-of-bag sample can
be used for model combination, an out-of-bag error rate estimator is not
available in this case. Double-bagging (Hothorn and Lausen,
2003) computes a LDA on the out-of-bag sample and uses the discriminant
variables as additional predictors for the classification trees. comb
is an optional list of lists with two elements model
and predict
.
model
is a function with arguments formula
and data
.
predict
is a function with arguments object, newdata
only. If
the estimation of the covariance matrix in lda
fails due to a
limited out-of-bag sample size, one can use slda
instead.
See the example section for an example of double-bagging. The methodology is
not limited to a combination with LDA: bundling (Hothorn and Lausen, 2002b)
can be used with arbitrary classifiers.
The class of the object returned depends on class(y)
:
classbagg, regbagg
and survbagg
. Each is a list with elements
y |
the vector of responses. |
X |
the data frame of predictors. |
mtrees |
multiple trees: a list of length nbagg containing the
trees (and possibly additional objects) for each bootstrap sample. |
OOB |
logical whether the out-of-bag estimate should be computed. |
err |
if OOB=TRUE , the out-of-bag estimate of
misclassification or root mean squared error or the Brier score for censored
data. |
comb |
logical whether a combination of models was requested. |
For each class methods for the generics prune
,
print
, summary
and predict
are
available for inspection of the results and prediction, for example:
print.classbagg
, summary.classbagg
,
predict.classbagg
and prune.classbagg
for
classification problems.
Torsten.Hothorn <Torsten.Hothorn@rzmail.uni-erlangen.de>
Leo Breiman (1996a), Bagging Predictors. Machine Learning 24(2), 123–140.
Leo Breiman (1996b), Out-Of-Bag Estimation. Technical Report ftp://ftp.stat.berkeley.edu/pub/users/breiman/OOBestimation.ps.Z.
Leo Breiman (1998), Arcing Classifiers. The Annals of Statistics 26(3), 801–824.
Peter Buehlmann and Bin Yu (2002), Analyzing Bagging. The Annals of Statistics 30(4), 927–961.
Torsten Hothorn and Berthold Lausen (2003), Double-Bagging: Combining classifiers by bootstrap aggregation. Pattern Recognition, 36(6), 1303–1309.
Torsten Hothorn and Berthold Lausen (2002b), Bundling Classifiers by Bagging Trees. submitted. Preprint available from http://www.mathpreprints.com/math/Preprint/blausen/20021016/1.
Torsten Hothorn, Berthold Lausen, Axel Benner and Martin Radespiel-Troeger (2004), Bagging Survival Trees. Statistics in Medicine, 23(1), 77–91.
# Classification: Breast Cancer data data(BreastCancer) # 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) 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) # Test set error (nbagg=25, trees pruned): 3.41 (Breiman, 1996a, Table 8) mod <- bagging(medv ~ ., data=BostonHousing, coob=TRUE) print(mod) 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) 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)