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.
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
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