Model Based Random Forest Analysis

Functions to implements random forest method for model based recursive partitioning. The mob() function, developed by Zeileis et al. (2008), within 'party' package, is modified to construct model-based decision trees based on random forests methodology. The main input function mobforest.analysis() takes all input parameters to construct trees, compute out-of-bag errors, predictions, and overall accuracy of forest. The algorithm performs parallel computation using cluster functions within 'parallel' package.

mobForest implements random forest method for model based recursive partitioning. The mob() function, developed by Zeileis et al (2008), within party package, is modified to construct model-based decision trees based on random forests methodology. The main input function mobforest.analysis() takes all input parameters to construct trees, compute out-of-bag errors, predictions, and overall accuracy of forest. The algorithm performs parallel computation using clusterApply() function within the parallel package.

Installation

install.packages("mobForest")

Usage

To run the example, you will need the mlbench package. It contains a boston housing dataset for machine learning algorithms to run benchmark tests on.

library(mlbench)
set.seed(1111)
# Random Forest analysis of model based recursive partitioning load data
data("BostonHousing", package = "mlbench")
BostonHousing <- BostonHousing[1:90, c("rad", "tax", "crim", "medv", "lstat")] 

# Recursive partitioning based on linear regression model medv ~ lstat with 3 trees.  1 core/processor used. 
rfout <- mobforest.analysis(as.formula(medv ~ lstat), c("rad", "tax", "crim"),
mobforest_controls = mobforest.control(ntree = 3, mtry = 2, replace = TRUE,
        alpha = 0.05, bonferroni = TRUE, minsplit = 25), data = BostonHousing,
        processors = 1, model = linearModel, seed = 1111)
rfout