BootRF {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))