mob.rf.tree

0th

Percentile

Model based recursive partitioning - randomized subset of partition variables considered during each split.

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.

Aliases
  • mob.rf.tree
Documentation reproduced from package mobForest, version 1.3.1, License: GPL (>= 2)

Community examples

Looks like there are no examples yet.