pqrBayes-package: Bayesian penalized quantile regression for linear, binary...

In pqrBayes: Bayesian Penalized Quantile Regression

Bayesian penalized quantile regression for linear, binary LASSO, group LASSO and varying coefficient models

Description

In this package, we implement Bayesian penalized quantile regression for linear regression (i.e., LASSO), binary LASSO, group LASSO and quantile varying coefficient (VC) models. Point-mass spike-and-slab priors have been incorporated in the Bayesian hierarchical models to facilitate Bayesian shrinkage estimation with exact sparsity in these models. The four default methods are Bayesian regularized quantile regression with spike-and-slab priors under the linear (i.e., LASSO), binary LASSO, group LASSO and VC model, correspondingly. In addition to default methods, users can also choose methods without robustness and/or spike–and–slab priors.

Details

The user friendly, integrated interface pqrBayes() allows users to flexibly choose fitting models by specifying the following parameters:

robust:	whether to fit a robust sparse quantile regression model (LASSO, binary LASSO,
	group LASSO or Varying Coefficient models) or their non-robust counterparts.
sparse:	whether to use the spike-and-slab priors to impose exact sparsity.
model:	whether to fit a linear model (i.e., LASSO), binary LASSO, group LASSO
	or a varying coefficient model.

The function pqrBayes() returns a pqrBayes object that stores the posterior estimates of regression coefficients.

References

Fan, K., Subedi, S., Yang, G., Lu, X., Ren, J. and Wu, C. (2024). Is Seeing Believing? A Practitioner's Perspective on High-dimensional Statistical Inference in Cancer Genomics Studies. Entropy, 26(9).794 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3390/e26090794")}

Zhou, F., Ren, J., Ma, S. and Wu, C. (2023). The Bayesian regularized quantile varying coefficient model. Computational Statistics & Data Analysis, 107808 \Sexpr[results=rd]{tools:::Rd_expr_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 \Sexpr[results=rd]{tools:::Rd_expr_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 \Sexpr[results=rd]{tools:::Rd_expr_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 \Sexpr[results=rd]{tools:::Rd_expr_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 \Sexpr[results=rd]{tools:::Rd_expr_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 \Sexpr[results=rd]{tools:::Rd_expr_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 \Sexpr[results=rd]{tools:::Rd_expr_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) \Sexpr[results=rd]{tools:::Rd_expr_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.

See Also

pqrBayes

pqrBayes documentation built on June 8, 2025, 12:35 p.m.