bag | R Documentation |

`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).

```
bag(x, ...)
bagControl(
fit = NULL,
predict = NULL,
aggregate = NULL,
downSample = FALSE,
oob = TRUE,
allowParallel = TRUE
)
## Default S3 method:
bag(x, y, B = 10, vars = ncol(x), bagControl = NULL, ...)
## S3 method for class 'bag'
predict(object, newdata = NULL, ...)
## S3 method for class 'bag'
print(x, ...)
## S3 method for class 'bag'
summary(object, ...)
## S3 method for class 'summary.bag'
print(x, digits = max(3, getOption("digits") - 3), ...)
ldaBag
plsBag
nbBag
ctreeBag
svmBag
nnetBag
```

`x` |
a matrix or data frame of predictors |

`...` |
arguments to pass to the model function |

`fit` |
a function that has arguments |

`predict` |
a function that generates predictions for each sub-model. The function should have #' arguments |

`aggregate` |
a function with arguments |

`downSample` |
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` |
logical: should out-of-bag statistics be computed and the predictions retained? |

`allowParallel` |
a parallel backend is loaded and available, should the function use it? |

`y` |
a vector of outcomes |

`B` |
the number of bootstrap samples to train over. |

`vars` |
an integer. If this argument is not |

`bagControl` |
a list of options. |

`object` |
an object of class |

`newdata` |
a matrix or data frame of samples for prediction. Note that this argument must have a non-null value |

`digits` |
minimal number of |

An object of class `list`

of length 3.

An object of class `list`

of length 3.

An object of class `list`

of length 3.

An object of class `list`

of length 3.

An object of class `list`

of length 3.

An object of class `list`

of length 3.

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 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.

If a parallel backend is registered, the foreach package is used to train the models in parallel.

`bag`

produces an object of class `bag`

with elements

`fits ` |
a list with two sub-objects: the |

`control ` |
a mirror of the arguments passed into |

`call ` |
the call |

`B ` |
the number of bagging iterations |

`dims ` |
the dimensions of the training set |

Max Kuhn

```
## 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))))
```

caret documentation built on March 31, 2023, 9:49 p.m.

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