R: Tune RRF for the optimal mtry parameter

tuneRRF {RRF} | R Documentation

## Tune RRF 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 RRF.

### Usage

    tuneRRF(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 `RRF`
`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
`...` | options to be given to `RRF`

### 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 `RRF` object produced with the optimal `mtry`.

### See Also

`RRF`

### Examples

    data(fgl, package="MASS")
    fgl.res <- tuneRRF(fgl[,-10], fgl[,10], stepFactor=1.5)

[Package RRF version 1.9.4 Index]