# bagging.cv

##### Runs v-fold cross validation with Bagging

The data are divided into `v`

non-overlapping subsets of roughly equal size. Then, `bagging`

is applied on `(v-1)`

of the subsets. Finally, predictions are made for the left out subsets,
and the process is repeated for each of the `v`

subsets.

##### Usage

`bagging.cv(formula, data, v = 10, mfinal = 100, control, par=FALSE)`

##### Arguments

- formula
a formula, as in the

`lm`

function.- data
a data frame in which to interpret the variables named in

`formula`

- v
An integer, specifying the type of v-fold cross validation. Defaults to 10. If

`v`

is set as the number of observations, leave-one-out cross validation is carried out. Besides this, every value between two and the number of observations is valid and means that roughly every v-th observation is left out.- mfinal
an integer, the number of iterations for which boosting is run or the number of trees to use. Defaults to

`mfinal=100`

iterations.- control
options that control details of the rpart algorithm. See rpart.control for more details.

- par
if

`TRUE`

, the cross validation process is runned in parallel. If`FALSE`

(by default), the function runs without parallelization.

##### Value

An object of class `bagging.cv`

, which is a list with the following components:

the class predicted by the ensemble classifier.

the confusion matrix which compares the real class with the predicted one.

returns the average error.

##### References

Alfaro, E., Gamez, M. and Garcia, N. (2013): ``adabag: An R Package for Classification with Boosting and Bagging''. Journal of Statistical Software, Vol 54, 2, pp. 1--35.

Alfaro, E., Garcia, N., Gamez, M. and Elizondo, D. (2008): ``Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks''. Decision Support Systems, 45, pp. 110--122.

Breiman, L. (1996): "Bagging predictors". Machine Learning, Vol 24, 2, pp. 123--140.

Breiman, L. (1998). "Arcing classifiers". The Annals of Statistics, Vol 26, 3, pp. 801--849.

##### See Also

##### Examples

```
# NOT RUN {
## rpart library should be loaded
library(rpart)
data(iris)
iris.baggingcv <- bagging.cv(Species ~ ., v=2, data=iris, mfinal=3,
control=rpart.control(cp=0.01))
iris.baggingcv[-1]
## rpart and mlbench libraries should be loaded
## Data Vehicle (four classes)
#This example has been hidden to keep execution time <5s
#data(Vehicle)
#Vehicle.bagging.cv <- bagging.cv(Class ~.,data=Vehicle,v=5,mfinal=10,
#control=rpart.control(maxdepth=5))
#Vehicle.bagging.cv[-1]
# }
```

*Documentation reproduced from package adabag, version 4.2, License: GPL (>= 2)*