In this package, we implement Bayesian penalized quantile regression for the linear regression model, group LASSO and quantile varying coefficient (VC) model. 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 three default methods are Bayesian regularized quantile regression with spike-and-slab priors under the linear model, group LASSO and VC model, correspondingly. In addition to default methods, users can also choose methods without robustness and/or spike--and--slab priors.
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 or a quantile varying coefficient |
| model 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, group LASSO or a varying coefficient model. |
The function pqrBayes() returns a pqrBayes object that stores the posterior estimates of regression coefficients.
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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) tools:::Rd_expr_doi("10.1515/sagmb-2017-0008")
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pqrBayes