cv.springer {springer}

k-folds cross-validation for springer

Description

This function conducts k-fold cross-validation for springer and returns the optimal values of the tuning parameters.

Usage

cv.springer(
  clin = NULL,
  e,
  g,
  y,
  beta0,
  lambda1,
  lambda2,
  nfolds,
  func,
  corr,
  structure,
  maxits = 30,
  tol = 0.001
)

Arguments

clin

a matrix of clinical covariates. The default value is NULL. Whether to include the clinical covariates is decided by user.

e

a matrix of environment factors.

g

a matrix of genetic factors.

y

the longitudinal response.

beta0

the initial value for the coefficient vector.

lambda1

a user-supplied sequence of \lambda_{1} values, which serves as a tuning parameter for the individual-level penalty.

lambda2

a user-supplied sequence of \lambda_{2} values, which serves as a tuning parameter for the group-level penalty.

nfolds

the number of folds for cross-validation.

func

the framework to obtain the score equation. Two choices are available: "GEE" and "QIF".

corr

the working correlation structure adopted in the estimation algorithm. The springer provides three choices for the working correlation structure: exchangeable, AR-1,and independence.

structure

Three choices are available for structured variable selection. "bilevel" for sparse-group selection on both group-level and individual-level. "group" for selection on group-level only. "individual" for selection on individual-level only.

maxits

the maximum number of iterations that is used in the estimation algorithm. The default value is 30.

tol

The tolerance level. Coefficients with absolute values that are smaller than the tolerance level will be set to zero. The adhoc value can be chosen as 0.001.

Details

For bi-level sparse group selection, cv.springer returns two optimal tuning parameters, \lambda_{1} and \lambda_{2}; for group-level selection, this function returns the optimal \lambda_{2} with \lambda_{1}=0; for individual-level selection, this function returns the optimal \lambda_{1} with \lambda_{2}=0.

Value

an object of class "cv.springer" is returned, with is a list with components below:

lam1

the optimal \lambda_{1}.

lam2

the optimal \lambda_{2}.