tuneRF {randomForest} | R Documentation |

## 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 |

`...` |
options to be given to `randomForest` |

### 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`

.

### See Also

`randomForest`

### Examples

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

[Package

*randomForest* version 4.5-1

Index]