select_cste_bin: Select the optimal tuning parameters in CSTE estimation for...

In CSTE: Covariate Specific Treatment Effect (CSTE) Curve

Select the optimal tuning parameters in CSTE estimation for binary outcome.

Description

select lasso penalty parameter \lambda for \beta_1 and \beta_2 in CSTE estimation.

Usage

select_cste_bin(
  x,
  y,
  z,
  lam_seq,
  beta_ini = NULL,
  nknots = 1,
  max.iter = 2000,
  eps = 0.001
)

Arguments

x	samples of covariates which is a n*p matrix.
y	samples of binary outcome which is a n*1 vector.
z	samples of treatment indicator which is a n*1 vector.
lam_seq	a sequence for the choice of \lambda.
beta_ini	initial values for (\beta_1', \beta_2')', default value is NULL.
nknots	number of knots for the B-spline for estimating g_1 and g_2.
max.iter	maximum iteration for the algorithm.
eps	numeric scalar \geq 0, the tolerance for the estimation of \beta_1 and \beta_2.

Value

A list which includes

• optimal: optimal cste within the given the sequence of \lambda.

• bic: BIC for the sequence of \lambda.

• lam_seq: the sequence of \lambda that is used.

References

Guo W., Zhou X. and Ma S. (2021). Estimation of Optimal Individualized Treatment Rules Using a Covariate-Specific Treatment Effect Curve with High-dimensional Covariates, Journal of the American Statistical Association, 116(533), 309-321

See Also

cste_bin

CSTE documentation built on April 4, 2025, 12:27 a.m.