Structured Additive Regression Models: AnRInterface toBayesX

Abstract

Structured additive regression (STAR) models provide a flexible framework for modeling possible nonlinear effects of covariates: They contain the well established frameworks of generalized linear models and generalized additive models as special cases but also allow a wider class of effects, e.g., for geographical or spatio-temporal data, allowing for specification of complex and realistic models. BayesX is standalone software package providing software for fitting general class of STAR models. Based on a comprehensive open-source regression toolbox written in C++, BayesX uses Bayesian inference for estimating STAR models based on Markov chain Monte Carlo simulation techniques, a mixed model representation of STAR models, or stepwise regression techniques combining penalized least squares estimation with model selection. BayesX not only covers models for responses from univariate exponential families, but also models from less-standard regression situations such as models for multi-categorical responses with either ordered or unordered categories, continuous time survival data, or continuous time multi-state models. This paper presents a new fully interactive R interface to BayesX: the R package R2BayesX. With the new package, STAR models can be conveniently specified using R's formula language (with some extended terms), fitted using the BayesX binary, represented in R with objects of suitable classes, and finally printed/summarized/plotted. This makes BayesX much more accessible to users familiar with R and adds extensive graphics capabilities for visualizing fitted STAR models. Furthermore, R2BayesX complements the already impressive capabilities for semiparametric regression in R by a comprehensive toolbox comprising in particular more complex response types and alternative inferential procedures such as simulation-based Bayesian inference.

Smoothing Parameter and Model Selection for General Smooth Models

“…However, for the special case of univariate P-splines (Eilers and Marx 1996;Marx and Eilers 1998) some comparison with existing methods is possible, in particular using R package gamlss Stasinopoulos 2005, 2014) and the BayesX package (Fahrmeir and Lang 2001;Fahrmeir, Kneib, and Lang 2004;Brezger and Lang 2006;Umlauf et al 2015;Belitz et al 2015, www.bayesx.org). For this special case both packages implement models using essentially the same penalized likelihoods used by the new method, but they optimize localized marginal likelihood scores within the penalized likelihood optimization algorithm to estimate the smoothing parameters.…”

Predators, food and social context shape the types of vigilance exhibited by kangaroos

“…Our procedure allowed us to investigate an individual's decisions among five options (not vigilant or engaged in one of the four types of vigilance). As these five options were treated as the response variable, we ran multinomial logistic regression models using the package 'R2BayesX' using the function bayesx() in the R software (Umlauf, Lang, Kneib, & Zeileis, 2011). In this procedure, we fixed the level 'nonvigilant' of the response variable (corresponding mainly to foraging activity, see Results) as the reference, allowing us to model the probabilities of a kangaroo exhibiting the four types of vigilance when she was foraging.…”

gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework

“…However, as it is relatively time-consuming to compute the neighborhood matrix from the boundary file and as we need it several times, we pre-compute it once. Note that R2BayesX (Umlauf, Kneib, Lang, and Zeileis 2013;Umlauf, Adler, Kneib, Lang, and Zeileis 2015;Belitz, Brezger, Kneib, Lang, and Umlauf 2015) needs to be loaded in order to use this function:…”