BootRF: Bootstrap with 'randomForest()'

In shipunov: Miscellaneous Functions from Alexey Shipunov

Bootstrap with 'randomForest()'

Description

How to bootstrap with 'randomForest()'

Usage

BootRF(data, classes, sub="none", nsam=4, nboot=1000, misclass=TRUE, ...)

Arguments

data	Data frame to classify
classes	Character vector of class names
sub	Subsample to use (see example)
nsam	Number of training items from each level of grouping factor, default 4
nboot	Number of iterations
misclass	Calculate misclassification table?
...	Further options to randomForest()

Details

Note that as randomForest::randomForest() is based on sampling, BootRF() is the kind of second-level bootstrap.

BootRF() is very simple and does not interact with Random Forest algorithms. It is stratified, i.e. samples equal numbers ('nsam') of training items from the each level of grouping factor.

Also, it allows to use the subset of data which will be in turn used for sub-sampling of training data.

Value

Returns all predictions as character matrix, each boot is a column

Author(s)

Alexey Shipunov

See Also

randomForest::randomForest

Examples

iris.sub <- 1:nrow(iris) %in% seq(1, nrow(iris), 5)

## could be slow
iris.bootrf <- BootRF(iris[, -5], iris[, 5], sub=iris.sub)
iris.bootrf <- BootRF(iris[, -5], iris[, 5]) # naturally, lower
## calculate and plot stability
st <- apply(iris.bootrf, 1, function(.x) var(as.numeric(as.factor(.x))))
plot(prcomp(iris[, -5])$x, col=iris$Species, pch=ifelse(st == 0, 19, 1))

shipunov documentation built on Feb. 16, 2023, 9:05 p.m.