RRF: tuneRRF – R documentation

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tuneRRF

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.

Examples

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

RRF

Regularized Random Forest

v1.9.1

GPL (>= 2)

Authors

Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener, Regularized random forest for classification by Houtao Deng. Regularized random forest for regression by Xin Guan.

Initial release

2018-07-16