cv.interep {interep}R Documentation

k-folds cross-validation for interep

Description

This function does k-fold cross-validation for interep and returns the optimal value of lambda.

Usage

cv.interep(e, g, y, beta0, lambda1, lambda2, nfolds, corre, pmethod,
  maxits)

Arguments

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 λ_{1} values, which serves as a tuning parameter for individual predictors.

lambda2

a user-supplied sequence of λ_{2} values, which serves as a tuning parameter for interactions.

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.

Details

When dealing with predictors with both main effects and interactions, this function returns two optimal tuning parameters, λ_{1} and λ_{2}; when there are only main effects in the predictors, this function returns λ_{1}, which is the optimal tuning parameter for individual predictors containing main effects.

Value

an object of class "cv.interep" is returned, which is a list with components:

lam1

the optimal λ_{1}.

lam2

the optimal λ_{2}.

References

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


[Package interep version 0.3.0 Index]