Bayenet-package {Bayenet}

Bayesian Quantile Elastic Net for Genetic Study

Description

In this package, we provide a set of robust Bayesian quantile variable selection methods for genetic analysis. A Bayesian formulation of the quantile regression has been adopted to accommodate data contamination and heavy-tailed distributions in the response. The proposed method conducts a robust quantile variable selection by accounting for structural sparsity. In particular, the spike-and-slab priors are imposed to identify important genetic effects. In addition to the default method, users can also choose different structures (robust or non-robust) and penalty (lasso or elastic net) with or without spike-and-slab priors.

Details

_package

The user friendly, integrated interface Bayenet() allows users to flexibly choose the fitting methods they prefer. There are three arguments in Bayenet() that control the fitting method: robust: whether to use robust methods; sparse: whether to use the spike-and-slab priors to create sparsity; penalty: use lasso or elastic net as penalty. The function Bayenet() returns a Bayenet object that contains the posterior estimates of each coefficients. predict.Bayenet() and print.Bayenet() are implemented for Bayenet objects. predict.Bayenet() takes a Bayenet object and returns the predicted values for new observations.

References

Lu, X. and Wu, C. (2023). Bayesian quantile elastic net with spike-and-slab priors.

Lu, X., Fan, K., Ren, J., and Wu, C. (2021). Identifying Gene–Environment Interactions With Robust Marginal Bayesian Variable Selection. Frontiers in Genetics, 12:667074 doi:10.3389/fgene.2021.667074

Zhou, F., Ren, J., Lu, X., Ma, S. and Wu, C. (2020). Gene–Environment Interaction: a Variable Selection Perspective. Epistasis. Methods in Molecular Biology. Humana Press (Accepted) https://arxiv.org/abs/2003.02930

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

Li, Q. and Lin, N. (2010). The Bayesian elastic net. Bayesian Anal, 5(1): 151-170 doi:10.1214/10-BA506

Li, Q., Xi, R. and Lin, N. (2010). The Bayesian regularized quantile regression. Bayesian Analysis, 5(3): 533-556 doi:10.1214/10-BA521

See Also

Bayenet