| pqrBayes-package {pqrBayes} | R Documentation |
Regularized Bayesian Quantile Varying Coefficient Model
Description
In this package, we implement a Bayesian quantile varying coefficient model for non-linear gene-environment interaction analysis. The varying coefficient functions capture the possible non-linear gene-environment interactions and they are approximated using linear combinations of B-spline basis. Quantile regression is adopted as it's robust to long-tailed distributions in the response/phenotype and provides the capability of describing the relationship between the response variable and predictors at different quantiles of the response variable. The default method (the proposed method) conducts variable selection by accounting for sparsity. In particular, the spike–and–slab priors are adopted to shrink the coefficients of unimportant effects to exactly zero. In addition to the default method, users can also choose the method without spike–and–slab priors.
Details
The user friendly, integrated interface pqrBayes() allows users to flexibly choose the fitting methods by specifying the following parameter:
| sparse: | whether to use the spike-and-slab priors to impose sparsity. |
The function pqrBayes() returns a pqrBayes object that contains the posterior estimates of each coefficients.
References
Zhou, F., Ren, J., Ma, S. and Wu, C. (2023). The Bayesian regularized quantile varying coefficient model. Computational Statistics & Data Analysis, 107808 doi:10.1016/j.csda.2023.107808
Ren, J., Zhou, F., Li, X., Ma, S., Jiang, Y. and Wu, C. (2023). Robust Bayesian variable selection for gene-environment interactions. Biometrics, 79(2), 684-694 doi:10.1111/biom.13670
Wu, C., and Ma, S. (2015). A selective review of robust variable selection with applications in bioinformatics. Briefings in Bioinformatics, 16(5), 873–883 doi:10.1093/bib/bbu046
Zhou, F., Ren, J., Lu, X., Ma, S. and Wu, C. (2021). Gene–Environment Interaction: a Variable Selection Perspective. Epistasis. Methods in Molecular Biology. 2212:191–223 https://link.springer.com/protocol/10.1007/978-1-0716-0947-7_13
Ren, J., Zhou, F., Li, X., Chen, Q., Zhang, H., Ma, S., Jiang, Y. and Wu, C. (2020) Semi-parametric Bayesian variable selection for gene-environment interactions. Statistics in Medicine, 39: 617– 638 doi:10.1002/sim.8434
Ren, J., Zhou, F., Li, X., Wu, C. and Jiang, Y. (2019) spinBayes: Semi-Parametric Gene-Environment Interaction via Bayesian Variable Selection. R package version 0.1.0. https://CRAN.R-project.org/package=spinBayes
Wu, C., Jiang, Y., Ren, J., Cui, Y. and Ma, S. (2018). Dissecting gene-environment interactions: A penalized robust approach accounting for hierarchical structures. Statistics in Medicine, 37:437–456 doi:10.1002/sim.7518
Wu, C., Shi, X., Cui, Y. and Ma, S. (2015). A penalized robust semiparametric approach for gene-environment interactions. Statistics in Medicine, 34 (30): 4016–4030 doi:10.1002/sim.6609
Wu, C., Cui, Y., and Ma, S. (2014). Integrative analysis of gene–environment interactions under a multi–response partially linear varying coefficient model. Statistics in Medicine, 33(28), 4988–4998 doi:10.1002/sim.6287
Wu, C., Zhong, P.S. and Cui, Y. (2018). Additive varying–coefficient model for nonlinear gene–environment interactions. Statistical Applications in Genetics and Molecular Biology, 17(2) doi:10.1515/sagmb-2017-0008
Wu, C., Zhong, P.S. and Cui, Y. (2013). High dimensional variable selection for gene-environment interactions. Technical Report. Michigan State University.