buildRandomForest: Random Forest Interface

In bartzbeielstein/SPOT: Sequential Parameter Optimization Toolbox

Description

This is a simple wrapper for the randomForest function from the randomForest package. The purpose of this function is to provide an interface as required by SPOT, to enable modeling and model-based optimization with random forest.

Usage

buildRandomForest(x, y, control = list())

Arguments

x	matrix of input parameters. Rows for each point, columns for each parameter.
y	one column matrix of observations to be modeled.
control	list of control parameters, currently not used.

Value

an object of class "spotRandomForest", with a predict method and a print method.

Examples

## Not run: 
## Test-function:
braninFunction <- function (x) {	
	(x[2]  - 5.1/(4 * pi^2) * (x[1] ^2) + 5/pi * x[1]  - 6)^2 + 
10 * (1 - 1/(8 * pi)) * cos(x[1] ) + 10
}
## Create design points
set.seed(1)
x <- cbind(runif(20)*15-5,runif(20)*15)
## Compute observations at design points (for Branin function)
y <- as.matrix(apply(x,1,braninFunction))
## Create model
fit <- buildRandomForest(x,y)
## Print model parameters
print(fit)
## Predict at new location
predict(fit,cbind(1,2))
## True value at location
braninFunction(c(1,2))

## End(Not run)

bartzbeielstein/SPOT documentation built on June 13, 2020, 5:58 p.m.