bag.default
A General Framework For Bagging
bag
provides a framework for bagging classification or regression models. The user can provide their own functions for model building, prediction and aggregation of predictions (see Details below).
 Keywords
 models
Usage
bag(x, ...)
"bag"(x, y, B = 10, vars = ncol(x), bagControl = NULL, ...)
bagControl(fit = NULL, predict = NULL, aggregate = NULL, downSample = FALSE, oob = TRUE, allowParallel = TRUE)
ldaBag
plsBag
nbBag
ctreeBag
svmBag
nnetBag
"predict"(object, newdata = NULL, ...)
Arguments
 x
 a matrix or data frame of predictors
 y
 a vector of outcomes
 B
 the number of bootstrap samples to train over.
 bagControl
 a list of options.
 ...
 arguments to pass to the model function
 fit

a function that has arguments
x
,y
and...
and produces a model object that can later be used for prediction. Example functions are found inldaBag
,plsBag
,nbBag
,svmBag
andnnetBag
.  predict

a function that generates predictions for each submodel. The function should have arguments
object
andx
. The output of the function can be any type of object (see the example below where posterior probabilities are generated. Example functions are found inldaBag
,plsBag
,nbBag
,svmBag
andnnetBag
.)  aggregate

a function with arguments
x
andtype
. The function that takes the output of thepredict
function and reduces the bagged predictions to a single prediction per sample. thetype
argument can be used to switch between predicting classes or class probabilities for classification models. Example functions are found inldaBag
,plsBag
,nbBag
,svmBag
andnnetBag
.  downSample
 a logical: for classification, should the data set be randomly sampled so that each class has the same number of samples as the smallest class?
 oob
 a logical: should outofbag statistics be computed and the predictions retained?
 allowParallel
 if a parallel backend is loaded and available, should the function use it?
 vars

an integer. If this argument is not
NULL
, a random sample of sizevars
is taken of the predictors in each bagging iteration. IfNULL
, all predictors are used.  object

an object of class
bag
.  newdata
 a matrix or data frame of samples for prediction. Note that this argument must have a nonnull value
Details
The function is basically a framework where users can plug in any model in to assess the effect of bagging. Examples functions can be found in ldaBag
, plsBag
, nbBag
, svmBag
and nnetBag
. Each has elements fit
, pred
and aggregate
.
One note: when vars
is not NULL
, the subsetting occurs prior to the fit
and predict
functions are called. In this way, the user probably does not need to account for the change in predictors in their functions.
When using bag
with train
, classification models should use type = "prob"
inside of the predict
function so that predict.train(object, newdata, type = "prob")
will work.
If a parallel backend is registered, the foreach package is used to train the models in parallel.
Value
 fits
 a list with two subobjects: the
fit
object has the actual model fit for that bagged samples and thevars
object is eitherNULL
or a vector of integers corresponding to which predictors were sampled for that model  control
 a mirror of the arguments passed into
bagControl
 call
 the call
 B
 the number of bagging iterations
 dims
 the dimensions of the training set
bag
produces an object of class bag
with elements
Examples
## A simple example of bagging conditional inference regression trees:
data(BloodBrain)
## treebag < bag(bbbDescr, logBBB, B = 10,
## bagControl = bagControl(fit = ctreeBag$fit,
## predict = ctreeBag$pred,
## aggregate = ctreeBag$aggregate))
## An example of pooling posterior probabilities to generate class predictions
data(mdrr)
## remove some zero variance predictors and linear dependencies
mdrrDescr < mdrrDescr[, nearZeroVar(mdrrDescr)]
mdrrDescr < mdrrDescr[, findCorrelation(cor(mdrrDescr), .95)]
## basicLDA < train(mdrrDescr, mdrrClass, "lda")
## bagLDA2 < train(mdrrDescr, mdrrClass,
## "bag",
## B = 10,
## bagControl = bagControl(fit = ldaBag$fit,
## predict = ldaBag$pred,
## aggregate = ldaBag$aggregate),
## tuneGrid = data.frame(vars = c((1:10)*10 , ncol(mdrrDescr))))