Structured Additive Regression Models: An R Interface to BayesX
Structured additive regression (STAR) models provide a flexible framework for model- ing 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.,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2015-02-01
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| Series: | Journal of Statistical Software |
| Online Access: | http://www.jstatsoft.org/index.php/jss/article/view/2236 |
| _version_ | 1818902074331496448 |
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| author | Nikolaus Umlauf Daniel Adler Thomas Kneib Stefan Lang Achim Zeileis |
| author_facet | Nikolaus Umlauf Daniel Adler Thomas Kneib Stefan Lang Achim Zeileis |
| author_sort | Nikolaus Umlauf |
| collection | DOAJ |
| description | Structured additive regression (STAR) models provide a flexible framework for model- ing 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 Rs 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. |
| first_indexed | 2024-12-19T20:29:52Z |
| format | Article |
| id | doaj.art-16195ebfaf9f4cd681c86588c894fb81 |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-12-19T20:29:52Z |
| publishDate | 2015-02-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-16195ebfaf9f4cd681c86588c894fb812022-12-21T20:06:44ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602015-02-0163114610.18637/jss.v063.i21840Structured Additive Regression Models: An R Interface to BayesXNikolaus UmlaufDaniel AdlerThomas KneibStefan LangAchim ZeileisStructured additive regression (STAR) models provide a flexible framework for model- ing 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 Rs 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.http://www.jstatsoft.org/index.php/jss/article/view/2236 |
| spellingShingle | Nikolaus Umlauf Daniel Adler Thomas Kneib Stefan Lang Achim Zeileis Structured Additive Regression Models: An R Interface to BayesX Journal of Statistical Software |
| title | Structured Additive Regression Models: An R Interface to BayesX |
| title_full | Structured Additive Regression Models: An R Interface to BayesX |
| title_fullStr | Structured Additive Regression Models: An R Interface to BayesX |
| title_full_unstemmed | Structured Additive Regression Models: An R Interface to BayesX |
| title_short | Structured Additive Regression Models: An R Interface to BayesX |
| title_sort | structured additive regression models an r interface to bayesx |
| url | http://www.jstatsoft.org/index.php/jss/article/view/2236 |
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