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partykit: A Modular Toolkit for Recursive Partytioning in R

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  • Torsten Hothorn

    ()

  • Achim Zeileis

    ()

Abstract

The R package partykit provides a flexible toolkit for learning, representing, summarizing, and visualizing a wide range of tree-structured regression and classification models. The functionality encompasses: (a) basic infrastructure for representing trees (inferred by any algorithm) so that unified print/plot/predict methods are available; (b) dedicated methods for trees with constant fits in the leaves (or terminal nodes) along with suitable coercion functions to create such trees (e.g., by rpart, RWeka, PMML); (c) a reimplementation of conditional inference trees (ctree, originally provided in the party package); (d) an extended reimplementation of model-based recursive partitioning (mob, also originally in party) along with dedicated methods for trees with parametric models in the leaves. Here, a brief overview of the package and its design is given while more detailed discussions of items (a)--(d) are available in vignettes accompanying the package.

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Bibliographic Info

Paper provided by Faculty of Economics and Statistics, University of Innsbruck in its series Working Papers with number 2014-10.

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Length: 14 pages
Date of creation: Mar 2014
Date of revision:
Handle: RePEc:inn:wpaper:2014-10

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Keywords: recursive partitioning; regression trees; classification trees; statistical learning; R;

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