interep {interep} | R Documentation |
This function makes predictions for generalized estimating equation with a given value of lambda. Typical usage is to have the cv.interep function compute the optimal lambda, then provide it to the interep function.
interep(e, g, y, beta0, corre, pmethod, lam1, lam2, maxits)
e |
matrix of environment factors. |
g |
matrix of omics factors. In the case study, the omics measurements are lipidomics data. |
y |
the longitudinal response. |
beta0 |
the inital coefficient vector. |
corre |
the working correlation structure that is used in the estimation algorithm. interep provides three choices for the working correlation structure: "a" as AR-1", "i" as "independence" and "e" as "exchangeable". |
pmethod |
the penalization method. "mixed" refers to MCP penalty to individual main effects and group MCP penalty to interactions; "individual" means MCP penalty to all effects. |
lam1 |
the tuning parameter lambda1 for individual predictors. |
lam2 |
the tuning parameter lambda2 for interactions. |
maxits |
the maximum number of iterations that is used in the estimation algorithm. The default value is 30 |
coef |
the coefficient vector. |
Zhou, F., Ren, J., Li, G., Jiang, Y., Li, X., Wang, W.and Wu, C. (2019). Penalized variable selection for Lipid–environment interactions in the longitudinal lipidomics study.
Ren, J., Zhou, F., Li, X., Chen, Q., Zhang, H., Ma, S., Jiang,Y. and Wu, C. (2019). Semi-parametric Bayesian variable selection for Gene-Environment interactions.
Wu, C., Zhou, F., Ren, J., Li, X., Jiang, Y., Ma, S. (2019). A Selective Review of Multi-Level Omics Data Integration Using Variable Selection. High-Throughput, 8(1)
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)
Wu, C., Jiang, Y., Ren, J., Cui, Y., Ma, S. (2018). Dissecting gene-environment interactions: A penalized robust approach accounting for hierarchical structures. Statistics in Medicine, 37:437–456
Jiang, Y., Huang, Y., Du, Y., Zhao, Y., Ren, J., Ma, S., & Wu, C. (2017). Identification of prognostic genes and pathways in lung adenocarcinoma using a Bayesian approach. Cancer Inform, 1(7)
Wu, C., and Ma, S. (2015). A selective review of robust variable selection with applications in bioinformatics. Briefings in Bioinformatics, 16(5), 873–883
Wu, C., Shi, X., Cui, Y. and Ma, S. (2015). A penalized robust semiparametric approach for gene-environment interactions. Statistics in Medicine, 34 (30): 4016–4030
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
Wu, C. and Cui, Y. (2013). A novel method for identifying nonlinear gene–environment interactions in case–control association studies. Human Genetics, 132(12):1413–1425
Wu, C. and Cui, Y. (2013). Boosting signals in gene–based association studies via efficient SNP selection. Briefings in Bioinformatics, 15(2):279–291
Wu, C., Li, S., and Cui, Y. (2012). Genetic Association Studies: An Information Content Perspective. Current Genomics, 13(7), 566–573
data("dat") e=dat$e g=dat$z y=dat$y beta0=dat$coef index=dat$index b = interep(e, g, y,beta0,corre="e",pmethod="mixed",lam1=dat$lam1, lam2=dat$lam2,maxits=30) b[abs(b)<0.05]=0 pos = which(b != 0) tp = length(intersect(index, pos)) fp = length(pos) - tp list(tp=tp, fp=fp)