penC

samples of covariates which is a \(n*p\) matrix.

samples of binary outcome which is a \(n*1\) vector.

beta

value of the lasso penalty parameter \(\lambda\) for \(\beta_1\) and \(\beta_2\).

1: MCP estimator; 2: SCAD estimator.

Solve the penalized logistic regression.

A uniform statistical inferential tool in making individualized treatment decisions, which implements the methods of Ma et al. (2017)<DOI:10.1177/0962280214541724>
and Guo et al. (2021)<DOI:10.1080/01621459.2020.1865167>. It uses a flexible semiparametric modeling strategy for heterogeneous treatment effect estimation in high-dimensional settings and can gave valid confidence bands. Based on it, one can find the subgroups of patients that benefit from each treatment, thereby making individualized treatment selection.

Wenjie Hu

penC: Solve the penalized logistic regression.

Description

Usage

Arguments

Value