CSTE (version 2.0.0)

select_cste_bin: 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