cv.interep {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 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. |
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.
an object of class "cv.interep" is returned, which is a list with components:
lam1 |
the optimal λ_{1}. |
lam2 |
the optimal λ_{2}. |
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