newRWL-methods: Complete a Residual Weighted Learning Analysis

In DynTxRegime: Methods for Estimating Optimal Dynamic Treatment Regimes

Complete a Residual Weighted Learning Analysis

Description

Complete a Residual Weighted Learning Analysis

Usage

## S4 method for signature 'Kernel'
.newRWL(
  moPropen,
  moMain,
  responseType,
  data,
  response,
  txName,
  lambdas,
  cvFolds,
  surrogate,
  guess,
  kernel,
  fSet,
  suppress,
  ...
)

Arguments

moPropen	modelObj for propensity modeling
moMain	modelObj for main effects
responseType	Character indicating type of response
data	data.frame of covariates
response	vector of responses
txName	treatment variable column header in data
lambdas	tuning parameter(s)
cvFolds	number of cross-validation folds
surrogate	Surrogate object
guess	optional numeric vector providing starting values for optimization methods
kernel	Kernel object
fSet	Function or NULL defining subsets
suppress	T/F indicating if prints to screen are executed
...	Additional inputs for optimization

Value

An RWL object

DynTxRegime documentation built on Nov. 25, 2023, 1:09 a.m.