| emBayes-package {emBayes} | R Documentation |
Robust Bayesian Variable Selection via Expectation-Maximization
Description
This package provides the implementation of the spike-and-slab quantile LASSO (ssQLASSO) which combines the strength of Bayesian robust variable selection and the Expectation-Maximization (EM) coordinate descent approach. The alternative method spike-and-slab LASSO (ssLASSO) is also included in the package.
Details
Two user friendly, integrated interface cv.emBayes() and emBayes() allows users to flexibly choose the variable selection method by specifying the following parameter:
| quant: | to specify different quantiles when using robust methods. |
| func: | the model to perform variable selection. Two choices are available: |
| "ssLASSO" and "ssQLASSO". | |
| error: | to specify the difference between expectations of likelihood of two |
| consecutive iterations. It can be used to determine convergence. | |
| maxiter: | to specify the maximum number of iterations. |
Function cv.emBayes() returns cross-validation errors based on the check loss, least squares loss and Schwarz Information Criterion along with the corresponding optimal tuning parameters. Function emBayes() returns the estimated intercept, clinical coefficients, beta coefficients, scale parameter, probability parameter, number of iterations and expectation of likelihood at each iteration.
References
Liu, Y. and Wu, C. (2023). Spike-and-Slab Quantile LASSO. (to be submitted)
Ren, J., Zhou, F., Li, X., Ma, S., Jiang, Y., and Wu, C. (2022). Robust Bayesian variable selection for gene–environment interactions. Biometrics. doi:10.1111/biom.13670
Ren, J., Du, Y., Li, S., Ma, S., Jiang,Y. and Wu, C. (2019). Robust network-based regularization and variable selection for high dimensional genomics data in cancer prognosis. Genet. Epidemiol., 43:276-291 doi:10.1002/gepi.22194
Wu, C., Zhang, Q., Jiang,Y. and Ma, S. (2018). Robust network-based analysis of the associations between (epi)genetic measurements. J Multivar Anal., 168:119-130 doi:10.1016/j.jmva.2018.06.009
Tang, Z., Shen, Y., Zhang, X., and Yi, N. (2017). The spike-and-slab lasso generalized linear models for prediction and associated genes detection. Genetics, 205(1), 77-88 doi:10.1534/genetics.116.192195
Tang, Z., Shen, Y., Zhang, X., and Yi, N. (2017). The spike-and-slab lasso Cox model for survival prediction and associated genes detection. Bioinformatics, 33(18), 2799-2807 doi:10.1093/bioinformatics/btx300
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, Y. H., Ni, Z. X., and Li, Y. (2014). Quantile regression via the EM algorithm. Communications in Statistics-Simulation and Computation, 43(10), 2162-2172 doi:10.1080/03610918.2012.746980
Ročková, V., and George, E. I. (2014). EMVS: The EM approach to Bayesian variable selection. Journal of the American Statistical Association, 109(506), 828-846 doi:10.1080/01621459.2013.869223
Li, Q., Lin, N., and Xi, R. (2010). Bayesian regularized quantile regression. Bayesian Analysis, 5(3), 533-556 doi:10.1214/10-BA521
George, E. I., and McCulloch, R. E. (1993). Variable selection via Gibbs sampling. Journal of the American Statistical Association, 88(423), 881-889 doi:10.1080/01621459.1993.10476353