cv.interep | R Documentation |
This function does k-fold cross-validation for interep and returns the optimal value of lambda.
cv.interep(e, g, y, beta0, lambda1, lambda2, nfolds, corre, pmethod, 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 intial value for the coefficient vector. |
lambda1 |
a user-supplied sequence of |
lambda2 |
a user-supplied sequence of |
nfolds |
the number of folds for cross-validation. |
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. |
maxits |
the maximum number of iterations that is used in the estimation algorithm. |
When dealing with predictors with both main effects and interactions, this function returns two optimal tuning parameters,
\lambda_{1}
and \lambda_{2}
; when there are only main effects in the predictors, this function returns \lambda_{1}
,
which is the optimal tuning parameter for individual predictors containing main effects.
an object of class "cv.interep" is returned, which is a list with components:
lam1 |
the optimal |
lam2 |
the optimal |
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
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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3390/genes13030544")}Genes, 13(3): 554
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)
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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.8434")}Statistics in Medicine, 39(5): 617–638
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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3390/ht8010004")}High-Throughput, 8(1)
Ren, J., Du, Y., Li, S., Ma, S., Jiang, Y. and Wu, C. (2019). Robust network-based regularization and variable selection for high-dimensional genomic data in cancer prognosis. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/gepi.22194")}Genetic epidemiology, 43(3), 276-291
Ren, J., Jung, L., Du, Y., Wu, C., Jiang, Y. and Liu, J. (2019). regnet: Network-Based Regularization for Generalized Linear Models. R package, version 0.4.0
Wu, C., Zhang, Q., Jiang, Y. and Ma, S. (2018). Robust network-based analysis of the associations between (epi) genetic measurements. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.jmva.2018.06.009")}Journal of multivariate analysis, 168, 119-130
Wu, C., Zhong, P.-S., and Cui, Y. (2018). Additive varying-coefficient model for nonlinear gene-environment interactions. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1515/sagmb-2017-0008")} 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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.7518")}Statistics in Medicine, 37: 437–456
Ren, J., He, T., Li, Y., Liu, S., Du, Y., Jiang, Y. and Wu, C. (2017). Network-based regularization for high dimensional SNP data in the case–control study of Type 2 diabetes. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1186/s12863-017-0495-5")}BMC genetics, 18(1), 44
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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/1176935116684825")}Cancer Inform, 1(7)
Wu, C., and Ma, S. (2015). A selective review of robust variable selection with applications in bioinformatics. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/bib/bbu046")}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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.6609")}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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.6287")}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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s00439-013-1350-z")}Human Genetics, 132(12):1413–1425
Wu, C. and Cui, Y. (2013). Boosting signals in gene–based association studies via efficient SNP selection. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/bib/bbs087")}Briefings in Bioinformatics, 15(2):279–291
Wu, C., Zhong, P.S. and Cui, Y. (2013). High dimensional variable selection for gene-environment interactions. Technical Report, Michigan State University.
Wu, C., Li, S., and Cui, Y. (2012). Genetic Association Studies: An Information Content Perspective. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2174/138920212803251382")}Current Genomics, 13(7), 566–573
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.