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).

Usage

bag(x, ...)
## S3 method for class 'default':
bag(x, y, B = 10, vars = NULL, bagControl = bagControl(), ...)
bagControl(fit = NULL, 
           predict = NULL, 
           aggregate = NULL)

## S3 method for class 'bag':
predict(object, newdata = NULL, ...)

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.

Aliases

• 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,
                                      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))))