# tuneRF

##### Tune randomForest for the optimal mtry parameter

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

##### Examples

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

*Documentation reproduced from package randomForest, version 4.6-14, License: GPL (>= 2)*