# bag.default

0th

Percentile

##### 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, ...)## S3 method for class 'default':
bag(x, y, B = 10, vars = NULL, bagControl = bagControl(), ...)bagControl(fit = NULL,
predict = NULL,
aggregate = NULL,
downSample = FALSE)
## S3 method for class 'bag':
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
predict
a function that generates predictions for each sub-model. The function should have arguments object and x. The output of the function can be any type of object (see the example below where posterior probabilities are generated)
aggregate
a function with arguments x and type. The function that takes the output of the predict function and reduces the bagged predictions to a single prediction per sample. the type argument can be used to swi
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?
vars
an integer. If this argument is not NULL, a random sample of size vars is taken of the predictors in each bagging iteration. If NULL, 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 non-null value
##### Details

The function is basically a framework where users can plug in any model in to assess the effect of bagging.

One note: when vars is not NULL, the sub-setting 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.

##### Value

• bag produces an object of class bag with elements
• fitsa list with two sub-objects: the fit object has the actual model fit for that bagged samples and the vars object is either NULL or a vector of integers corresponding to which predictors were sampled for that model
• controla mirror of the arguments passed into bagControl
• callthe call
• Bthe number of bagging iterations
• dimsthe dimensions of the training set

• bag.default
• bag
• bagControl
• predict.bag
##### Examples
## A simple example of bagging conditional inference regression trees:
data(BloodBrain)

## Fit a model with the default values
ctreeFit <- function(x, y, ...)
{
library(party)
data <- as.data.frame(x)
data$y <- y ctree(y~., data = data) } ## Generate simple predictions of the outcome ctreePred <- function(object, x) { predict(object, x)[,1] } ## Take the median of the bagged predictions ctreeAg <- function(x, type = NULL) { ## x is a list of vectors, so we convert them to a matrix preds <- do.call("cbind", x) apply(preds, 1, median) } treebag <- bag(bbbDescr, logBBB, B = 10, bagControl = bagControl(fit = ctreeFit, predict = ctreePred, aggregate = ctreeAg)) ## An example of pooling posterior probabilities to generate class predictions data(mdrr) ## remove some zero variance predictors and linear dependencies mdrrDescr <- mdrrDescr[, -nearZeroVar(mdrrDescr)] mdrrDescr <- mdrrDescr[, -findCorrelation(cor(mdrrDescr), .95)] ## The fit and predict functions are stright-forward: ldaFit <- function(x, y, ...) { library(MASS) lda(x, y, ...) } ldaPred <- function(object, x) { predict(object, x)$posterior
}

## For the aggregation function, we take the median of the bagged
## posterior probabilities and pick the largest as the class
ldaAg <- function(x, type = "class")
{
## The class probabilities come in as a list of matrices
## For each class, we can pool them then average over them

pooled <- x[[1]] & NA
classes <- colnames(pooled)
for(i in 1:ncol(pooled))
{
tmp <- lapply(x, function(y, col) y[,col], col = i)
tmp <- do.call("rbind", tmp)
pooled[,i] <- apply(tmp, 2, median)
}
if(type == "class")
{
out <- factor(classes[apply(pooled, 1, which.max)],
levels = classes)
} else out <- pooled
out
}

bagLDA <- bag(mdrrDescr, mdrrClass,
B = 10,
vars = 10,
bagControl = bagControl(fit = ldaFit,
predict = ldaPred,
downSample = TRUE,
aggregate = ldaAg))

basicLDA <- train(mdrrDescr, mdrrClass, "lda")

bagLDA2 <- train(mdrrDescr, mdrrClass,
"bag",
B = 10,
bagControl(fit = ldaFit,
predict = ldaPred,
aggregate = ldaAg),
tuneGrid = data.frame(.vars = c((1:10)*10 , ncol(mdrrDescr))))
Documentation reproduced from package caret, version 4.75, License: GPL-2

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