CV.lambda: Cross-validation to decide minimum purity improvement for...

In Team-Wang-Lab/T-RL: Tree-based Reinforcement Learning for estimating optimal DTR.

Description

Cross-validation to decide minimum purity improvement for splitting.

Usage

CV.lambda(
  Y,
  A,
  H,
  g.opt,
  lambda.pct,
  K = 10,
  m.method = c("AIPW", "randomForest"),
  depth = 5,
  minsplit = 20
)

Arguments

Y	A vector of outcome of interest.
A	Treatment vector.
H	A matrix of covariates before assigning final treatment, excluding previous treatment variables.
g.opt	Optimal dynamic treatment regime.
K	Number of folds.
m.method	Method for calculating estimated conditional mean.
depth	Maximum tree depth.
minsplit	Minimal node size.

Team-Wang-Lab/T-RL documentation built on Jan. 3, 2020, 12:11 a.m.