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)an object of class "cv.interep" is returned, which is a list with components:
the optimal \(\lambda_{1}\).
the optimal \(\lambda_{2}\).
matrix of environment factors.
matrix of omics factors. In the case study, the omics measurements are lipidomics data.
the longitudinal response.
the intial value for the coefficient vector.
a user-supplied sequence of \(\lambda_{1}\) values, which serves as a tuning parameter for individual predictors.
a user-supplied sequence of \(\lambda_{2}\) values, which serves as a tuning parameter for interactions.
the number of folds for cross-validation.
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".
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.
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.
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