newLearning: Complete a Learning Analysis

In DynTxRegime: Methods for Estimating Optimal Dynamic Treatment Regimes

Complete a Learning Analysis

Description

Performs a weighted learning analysis.

Usage

.newLearning(fSet, kernel, ...)

## S4 method for signature ''NULL',Kernel'
.newLearning(
  fSet,
  kernel,
  ...,
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  lambdas,
  cvFolds,
  iter,
  surrogate,
  suppress,
  guess,
  createObj,
  prodPi = 1,
  index = NULL
)

## S4 method for signature ''function',Kernel'
.newLearning(
  fSet,
  kernel,
  ...,
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  lambdas,
  cvFolds,
  iter,
  surrogate,
  suppress,
  guess,
  createObj,
  prodPi = 1,
  index = NULL
)

## S4 method for signature ''function',SubsetList'
.newLearning(
  fSet,
  kernel,
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  lambdas,
  cvFolds,
  iter,
  surrogate,
  suppress,
  guess,
  createObj,
  prodPi = 1,
  index = NULL,
  ...
)

Arguments

fSet	NULL or function defining subset rules
kernel	Kernel object or SubsetList
...	Additional inputs for optimization
moPropen	modelObj for propensity model
moMain	modelObj for main effects of outcome model
moCont	modelObj for contrasts of outcome model
data	data.frame of covariates
response	Vector of responses
txName	Tx variable column header in data
lambdas	Tuning parameter(s)
cvFolds	Number of cross-validation folds
iter	Maximum number of iterations for outcome regression
surrogate	Surrogate object
suppress	T/F indicating if prints to screen are executed
guess	optional numeric vector providing starting values for optimization methods
createObj	A function name defining the method object for a specific learning algorithm
prodPi	A vector of propensity weights
index	The subset of individuals to be included in learning

Value

A Learning object

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