interep: fit generalized estimaitng equations with given tuning...

In interep: Interaction Analysis of Repeated Measure Data

fit generalized estimaitng equations with given tuning parameters

Description

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.

Usage

interep(e, g, y, beta0, corre, pmethod, lam1, lam2, 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 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

Value

coef	the coefficient vector.

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 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. Genes, 13(3): 554 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3390/genes13030544")}

Zhou, F., Ren, J., Lu, X., Ma, S. and Wu, C. (2021) Gene–Environment Interaction: a Variable Selection Perspective. Epistasis: Methods and Protocols, 191-223

Examples

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)

interep documentation built on May 29, 2024, 8:52 a.m.