Computational Statistics

, Volume 29, Issue 1–2, pp 3–35

Model-based boosting in R: a hands-on tutorial using the R package mboost

  • Benjamin Hofner
  • Andreas Mayr
  • Nikolay Robinzonov
  • Matthias Schmid
Original Paper

DOI: 10.1007/s00180-012-0382-5

Cite this article as:
Hofner, B., Mayr, A., Robinzonov, N. et al. Comput Stat (2014) 29: 3. doi:10.1007/s00180-012-0382-5

Abstract

We provide a detailed hands-on tutorial for the R add-on package mboost. The package implements boosting for optimizing general risk functions utilizing component-wise (penalized) least squares estimates as base-learners for fitting various kinds of generalized linear and generalized additive models to potentially high-dimensional data. We give a theoretical background and demonstrate how mboost can be used to fit interpretable models of different complexity. As an example we use mboost to predict the body fat based on anthropometric measurements throughout the tutorial.

Keywords

Boosting Component-wise functional gradient descent   Generalized additive models Tutorial 

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Benjamin Hofner
    • 1
  • Andreas Mayr
    • 1
  • Nikolay Robinzonov
    • 2
  • Matthias Schmid
    • 1
  1. 1.Department of Medical Informatics, Biometry and EpidemiologyFriedrich-Alexander-Universität Erlangen-NürnbergErlangenGermany
  2. 2.Department of StatisticsLudwig-Maximilians-Universität MünchenMunichGermany

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