select_cste_bin {CSTE} | R Documentation |
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 |
y |
samples of binary outcome which is a |
z |
samples of treatment indicator which is a |
lam_seq |
a sequence for the choice of |
beta_ini |
initial values for |
nknots |
number of knots for the B-spline for estimating |
max.iter |
maximum iteration for the algorithm. |
eps |
numeric scalar |
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