# tuneRF: Tune randomForest for the optimal mtry parameter

## Description

Starting with the default value of mtry, search for the optimal value
(with respect to Out-of-Bag error estimate) of mtry for randomForest.

## Usage

tuneRF(x, y, mtryStart, ntreeTry=50, stepFactor=2, improve=0.05,
trace=TRUE, plot=TRUE, doBest=FALSE, ...)

## Arguments

x

matrix or data frame of predictor variables

y

response vector (factor for classification, numeric for
regression)

mtryStart

starting value of mtry; default is the same as in
`randomForest`

ntreeTry

number of trees used at the tuning step

stepFactor

at each iteration, mtry is inflated (or deflated) by
this value

improve

the (relative) improvement in OOB error must be by this
much for the search to continue

trace

whether to print the progress of the search

plot

whether to plot the OOB error as function of mtry

doBest

whether to run a forest using the optimal mtry found

## Value

If `doBest=FALSE`

(default), it returns a matrix whose first
column contains the mtry values searched, and the second column the
corresponding OOB error.

If `doBest=TRUE`

, it returns the `randomForest`

object produced with the optimal `mtry`

.

## Examples

# NOT RUN {
data(fgl, package="MASS")
fgl.res <- tuneRF(fgl[,-10], fgl[,10], stepFactor=1.5)
# }