In this package, we further extend the sparse Bayesian quantile mixed models to nonlinear longitudinal interactions. Specifically, the proposed Bayesian quantile semiparametric model is robust not only to outliers and heavy‐tailed distributions of the response variable, but also to the misspecification of interaction effect in the forms other than non-linear interactions. We have developed the Gibbs sampler with the spike‐and‐slab priors to promote sparse identification of appropriate forms of main and interaction effects. In addition to the default method, users can also choose different selection structures for separation of constant and varying effects or not, methods without spike--and--slab priors and non-robust methods. In total, Blend provides 8 different methods (4 robust and 4 non-robust) under the random intercept and slope model. All the methods in this package are developed for the first time. Please read the Details below for how to configure the method used.
The user friendly, integrated interface Blend() allows users to flexibly choose the fitting methods by specifying the following parameter:
| robust: | whether to use robust methods for modelling. |
| quant: | |
| to specify different quantiles when using robust methods. | |
| structural: | whether to incorporate structural identification(separation of constant and varying effects) . |
| sparse: |
The function Blend() returns a Blend object that contains the posterior estimates of each coefficients and other useful information for selection(). S3 generic functions selection() and print() are implemented for Blend objects. selection() takes a Blend object and returns the variable selection results.
Fan, K., Ren, J., Ma, Shuangge and Wu, C. (2024). Bayesian Regularized Semiparametric Quantile Mixed Models in Longitudinal Studies. (submitted)
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.
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 tools:::Rd_expr_doi("10.1111/biom.13670")
Zhou, F., Ren, J., Ma, S. and Wu, C. (2023). The Bayesian regularized quantile varying coefficient model. Computational Statistics & Data Analysis, 187, 107808.
Zhou, F., Lu, X., Ren, J., Fan, K., Ma, S., & Wu, C. (2022). Sparse group variable selection for gene–environment interactions in the longitudinal study. Genetic epidemiology, 46(5-6), 317-340.
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 tools:::Rd_expr_doi("10.3390/genes10121002")
Ren, J., Zhou, F., Li, X., Ma, S., Jiang, Y. and Wu, C. (2020). roben: Robust Bayesian Variable Selection for Gene-Environment Interactions. R package version 0.1.1. https://CRAN.R-project.org/package=roben
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 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 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 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 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.
Blend