HDtweedie_kfold_aug

an n x p matrix of covariates for the zero part data, where each row is an observation
and each column is a predictor. MUST be ordered such that the first n_s rows align with the
observations in x_s and s

a length n vector of responses taking positive values

a length n vector of treatment variables with 1 indicating treatment and -1 indicating control

a length n vector of sample weights

tweedie mixing parameter. See <code><a rd-options="HDtweedie" href="/link/HDtweedie?package=personalized2part&version=0.0.1&to=HDtweedie" data-mini-rdoc="HDtweedie::HDtweedie">HDtweedie</a></code> for details

boolean variable of whether or not to fit model with interactions. For predictions,
interactions will be integrated out

interactions

Fits a penalized gamma augmentation model via cross fitting and
returns vector of length n of out of sample predictions on the link scale from cross fitting

Implements the methodology of Huling, Smith, and
Chen (2020) <doi:10.1080/01621459.2020.1801449>, which allows for subgroup identification
for semi-continuous outcomes by estimating individualized treatment rules. It uses a two-part
modeling framework to handle semi-continuous data by separately modeling the positive part
of the outcome and an indicator of whether each outcome is positive, but still results in a
single treatment rule. High dimensional data is handled with a cooperative lasso penalty,
which encourages the coefficients in the two models to have the same sign.

Jared Huling

personalized2part

Two-Part Estimation of Treatment Rules for Semi-Continuous Data

HDtweedie_kfold_aug function

tweedie mixing parameter. See <code><a rd-options='HDtweedie' href='HDtweedie'>HDtweedie</a></code> for details

HDtweedie_kfold_aug: Fit a penalized gamma augmentation model via cross fitting

Description

Usage

Arguments