mixedBayes-package: Bayesian Longitudinal Regularized Quantile Mixed Model

In mixedBayes: Bayesian Longitudinal Regularized Quantile Mixed Model

Bayesian Longitudinal Regularized Quantile Mixed Model

Description

In this package, we provide implementations of a set of high-dimensional robust Bayesian mixed-effect models to dissect longitudinal gene-environment interactions. The proposed method conducts robust Bayesian variable selection on both the main and interaction effects corresponding to individual and group levels (i.e. bi-level), respectively. Alternatively, selections only on individual levels by ignoring the grouping structure can also be performed. In addition, intra-cluster correlations among repeated measures are modeled via random intercept-and-slope and/or random intercept models. Imposing exact sparsity through spike-and-slab priors can be conducted on fixed effects with bi-level and/or individual level. In total, package mixedBayes provides implementations on 2 (robust and non-robust) × 2 ( types of fixed effects) × 2 ( types of random effects) × 2 (spike-and-slab or Laplacian priors) = 16 methods. Please read the details below for how to configure the method used.

Details

The user friendly, integrated interface mixedBayes() allows users to flexibly choose the fitting methods by specifying the following parameter:

slope:	whether to use random intercept-and-slope model or random intercept model.
robust:	whether to use robust or non-robust methods.
quant:	to specify different quantiles when using robust methods.
structure:	whether to specify bi-level or individual level.
sparse:	whether to use the spike-and-slab priors to impose sparsity.

The function mixedBayes() returns a mixedBayes object that contains the posterior estimates of each coefficients. S3 generic functions selection()and print() are implemented for mixedBayes objects. selection() takes a mixedBayes object and returns the variable selection results.

References

Fan, K., Jiang, Y., Ma, S., Wang, W. and Wu, C. (2025). Robust Sparse Bayesian Regression for Longitudinal Gene-Environment Interactions. Journal of the Royal Statistical Society Series C: Applied Statistics, qlaf027 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/jrsssc/qlaf027")}

Zhou, F., Ren, J., Li, G., Jiang, Y., Li, X., Wang, W. and Wu, C. (2019). Penalized Variable Selection for Lipid-Environment Interactions in a Longitudinal Lipidomics Study. Genes, 10(12), 1002 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3390/genes10121002")}

Zhou, F., Ren, J., Liu, Y., Li, X., Wang, W., and Wu, C. (2022). Interep: An r package for high-dimensional interaction analysis of the repeated measurement data. Genes, 13(3), 544 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3390/genes13030544")}

Zhou, F., Lu, X., Ren, J., Fan, K., Ma, S., and Wu, C. (2022). Sparse group variable selection for gene–environment interactions in the longitudinal study. Genetic epidemiology, 46(5-6), 317-340 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/gepi.22461")}

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 \Sexpr[results=rd]{tools:::Rd_expr_doi("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")}

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., 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. (2013). High dimensional variable selection for gene-environment interactions. Technical Report. Michigan State University.

See Also

mixedBayes

mixedBayes documentation built on June 8, 2025, 11:04 a.m.