cv.LassoGEE {LassoGEE}

Cross-validation for LassoGEE.

Description

Does k-fold cross-validation for LassoGEE to select tuning parameter value for longitudinal data with working independence structure.

Usage

cv.LassoGEE(
  X,
  y,
  id,
  family,
  method = c("CGD", "RWL"),
  scale.fix,
  scale.value,
  fold,
  lambda.vec,
  maxiter,
  tol
)

Arguments

X

A design matrix of dimension (nm) * p.

y

A response vector of length m * n.

id

A vector for identifying subjects/clusters.

family

A family object: a list of functions and expressions for defining link and variance functions. Families supported here is same as in PGEE which are binomial, gaussian, gamma and poisson.

method

The algorithms that are available. "CGD" represents the I-CGD algorithm, and "RWL" represents re-weighted least square algorithm.

scale.fix

A logical variable; if true, the scale parameter is fixed at the value of scale.value. The default value is TRUE.

scale.value

If scale.fix = TRUE, this assignes a numeric value to which the scale parameter should be fixed. The default value is 1.

fold

The number of folds used in cross-validation.

lambda.vec

A vector of tuning parameters that will be used in the cross-validation.

maxiter

The number of iterations that is used in the estimation algorithm. The default value is 50.

tol

The tolerance level that is used in the estimation algorithm. The default value is 1e^-3.

Value

An object class of cv.LassoGEE.

References

Li, Y., Gao, X., and Xu, W. (2020). Statistical consistency for generalized estimating equation with L_1 regularization.

See Also

LassoGEE