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spikeSlabGAM: Bayesian Variable Selection, Model Choice and Regularization for Generalized Additive Mixed Models in R

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  • Scheipl, Fabian

Abstract

The R package spikeSlabGAM implements Bayesian variable selection, model choice, and regularized estimation in (geo-)additive mixed models for Gaussian, binomial, and Poisson responses. Its purpose is to (1) choose an appropriate subset of potential covariates and their interactions, (2) to determine whether linear or more flexible functional forms are required to model the effects of the respective covariates, and (3) to estimate their shapes. Selection and regularization of the model terms is based on a novel spike-and-slab-type prior on coefficient groups associated with parametric and semi-parametric effects.

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  • Scheipl, Fabian, 2011. "spikeSlabGAM: Bayesian Variable Selection, Model Choice and Regularization for Generalized Additive Mixed Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 43(i14).
    • Handle: RePEc:jss:jstsof:v:043:i14
    DOI: http://hdl.handle.net/10.18637/jss.v043.i14
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    1. Rachel Carroll & Andrew B. Lawson & Delia Voronca & Chawarat Rotejanaprasert & John E. Vena & Claire Marjorie Aelion & Diane L. Kamen, 2014. "Spatial Environmental Modeling of Autoantibody Outcomes among an African American Population," IJERPH, MDPI, vol. 11(3), pages 1-16, March.
    2. Riccardo (Jack) Lucchetti & Luca Pedini, 2020. "ParMA: Parallelised Bayesian Model Averaging for Generalised Linear Models," Working Papers 2020:28, Department of Economics, University of Venice "Ca' Foscari".
    3. Benjamin Heuclin & Frédéric Mortier & Catherine Trottier & Marie Denis, 2021. "Bayesian varying coefficient model with selection: An application to functional mapping," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 24-50, January.
    4. T. Rajala & D. J. Murrell & S. C. Olhede, 2018. "Detecting multivariate interactions in spatial point patterns with Gibbs models and variable selection," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1237-1273, November.
    5. Xin Fang & Bo Fang & Chunfang Wang & Tian Xia & Matteo Bottai & Fang Fang & Yang Cao, 2019. "Comparison of Frequentist and Bayesian Generalized Additive Models for Assessing the Association between Daily Exposure to Fine Particles and Respiratory Mortality: A Simulation Study," IJERPH, MDPI, vol. 16(5), pages 1-20, March.
    6. Umlauf, Nikolaus & Adler, Daniel & Kneib, Thomas & Lang, Stefan & Zeileis, Achim, 2015. "Structured Additive Regression Models: An R Interface to BayesX," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i21).
    7. Goldsmith, Jeff & Scheipl, Fabian, 2014. "Estimator selection and combination in scalar-on-function regression," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 362-372.
    8. Jorge Castillo-Mateo & Jesús Asín & Ana C. Cebrián & Jesús Mateo-Lázaro & Jesús Abaurrea, 2023. "Bayesian Variable Selection in Generalized Extreme Value Regression: Modeling Annual Maximum Temperature," Mathematics, MDPI, vol. 11(3), pages 1-19, February.
    9. Yi Liu & Veronika Ročková & Yuexi Wang, 2021. "Variable selection with ABC Bayesian forests," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 453-481, July.
    10. Chase N. Joyner & Christopher S. McMahan & Joshua M. Tebbs & Christopher R. Bilder, 2020. "From mixed effects modeling to spike and slab variable selection: A Bayesian regression model for group testing data," Biometrics, The International Biometric Society, vol. 76(3), pages 913-923, September.
    11. Li He & Yu-Bo Wang & William C. Bridges & Zhulin He & S. Megan Che, 2023. "Bayesian Framework for Causal Inference with Principal Stratification and Clusters," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 114-140, April.

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