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

• Published in: Computational statistics Vol. 29; no. 1-2; pp. 3 - 35
• Main Authors: Hofner, Benjamin, Mayr, Andreas, Robinzonov, Nikolay, Schmid, Matthias
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2014; Springer Nature B.V
• Subjects: Algorithms; Analysis; Anthropometry; Body fat; Datasets; Economic Theory/Quantitative Economics/Mathematical Methods; Estimates; Expected values; Feature selection; Fittings; Generalized linear models; Least squares method; Machine learning; Mathematical analysis; Mathematical models; Mathematics and Statistics; Optimization; Original Paper; Packages; Probability and Statistics in Computer Science; Probability Theory and Stochastic Processes; Software; Statistical analysis; Statistics; Studies; Variables; Tutorial; Boosting; Generalized additive models; Component-wise functional gradient descent
• ISSN: 0943-4062; 1613-9658
• DOI: 10.1007/s00180-012-0382-5

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.