Parties, Models, Mobsters: A New Implementation of Model-Based Recursive Partitioning in R

MOB is a generic algorithm for model-based recursive partitioning (Zeileis, Hothorn, and Hornik 2008). Rather than fitting one global model to a dataset, it estimates local models on subsets of data that are “learned”by recursively partitioning. It proceeds in the following way: (1) fit a parametric model to a data set, (2) test for parameter instability over a set of partitioning variables, (3) if there is some overall parameter instability, split the model with respect to the variable associated with the highest instability, (4) repeat the procedure in each of the resulting subsamples. It is discussed how these steps of the conceptual algorithm are translated into computational tools in an object-oriented manner, allowing the user to plug in various types of parametric models. For representing the resulting trees, the R package partykit is employed and extended with generic infrastructure for recursive partitions where nodes are associated with statistical models. Compared to the previously available implementation in the party package, the new implementation supports more inference options, is easier to extend to new models, and provides more convenience features.