Structured Additive Regression Models: An R Interface to BayesX

Abstract

Structured additive regression (STAR) models provide a exible framework for modeling possible nonlinear e ects 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 e ects, e.g., for geographical or spatio-temporal data, allowing for speci - cation of complex and realistic models. BayesX is standalone software package providing software for tting 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 speci ed using R's formula language (with some extended terms), tted using the BayesX binary, represented in R with objects of suitable classes, and nally printed/summarized/plotted. This makes BayesX much more accessible to users familiar with R and adds extensive graphics capabilities for visualizing tted 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.