mob.rf.tree: Model based recursive partitioning - randomized subset of...

In RTIInternational/mobForest: Model Based Random Forest Analysis

Description

The mob function in party package is modified so that a random subset of predictor variables are considered during each split. mtry represents the number of predictor variables to be considered during each split.

Usage

mob.rf.tree(main_model, partition_vars, mtry, weights, data = list(),
  na.action = na.omit, model = glinearModel, control = mob_control(),
  ...)

Arguments

main_model	A model in character format
partition_vars	A vector of partition variables
mtry	A Random subset of partition variables to be considered at each node of decision tree
weights	An optional vector of weights, as described in mob
data	A data frame containing the variables in the model.
na.action	A function which indicates what should happen when the data contain NAs, as described in mob
model	A model of class StatModel
control	A list with control parameters as returned by mob_control
...	Additional arguments passed to the fit call for the model.

Value

An object of class mob inheriting from BinaryTree. Every node of the tree is additionally associated with a fitted model.

References

Achim Zeileis, Torsten Hothorn, and Kurt Hornik (2008). Model-Based Recursive Partitioning. Journal of Computational and Graphical Statistics, 17(2), 492-514.

RTIInternational/mobForest documentation built on Aug. 3, 2019, 8:28 a.m.