# mob.rf.tree

##### 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
- 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.

*Documentation reproduced from package mobForest, version 1.3.1, License: GPL (>= 2)*