apply() for List objects

Description

Applies the specified function to each element of the List.

Usage

.cycleList(object, ...)

## S4 method for signature 'List'
.cycleList(object, func, trm = "object", nm = NULL, ...)

## S4 method for signature 'DecisionPointList'
.cycleList(object, func, trm = "object", nm = "dp=", ...)

## S4 method for signature 'SubsetList'
.cycleList(object, func, trm = "object", nm = "Subset=", ...)

Arguments

Value

If func returns a value object, a list containing the value objects returned by func.

Create a BOWL Object for First Step of BOWL Algorithm

Description

Create a BOWL Object for First Step of BOWL Algorithm

Usage

## S4 method for signature ''NULL''
.newBOWL(
  BOWLObj,
  moPropen,
  fSet,
  data,
  response,
  txName,
  lambdas,
  cvFolds,
  kernel,
  surrogate,
  suppress,
  guess,
  ...
)

## S4 method for signature 'BOWL'
.newBOWL(
  BOWLObj,
  moPropen,
  fSet,
  data,
  response,
  txName,
  lambdas,
  cvFolds,
  kernel,
  surrogate,
  suppress,
  guess,
  ...
)

Arguments

Create a CVInfo Object

Description

Dispatch appropriate cross-validation procedure.

Usage

.newCVInfo(lambdas, kernel, ...)

## S4 method for signature 'ANY,ANY'
.newCVInfo(lambdas, kernel, ...)

## S4 method for signature 'numeric,Kernel'
.newCVInfo(lambdas, kernel, methodObject, cvObject, suppress, ...)

## S4 method for signature 'numeric,MultiRadialKernel'
.newCVInfo(lambdas, kernel, methodObject, cvObject, suppress, ...)

## S4 method for signature 'array,Kernel'
.newCVInfo(lambdas, kernel, methodObject, cvObject, suppress, ...)

## S4 method for signature 'array,MultiRadialKernel'
.newCVInfo(lambdas, kernel, methodObject, cvObject, suppress, ...)

## S4 method for signature 'numeric,MultiRadialKernel'
.newCVInfo(lambdas, kernel, methodObject, cvObject, suppress, ...)

## S4 method for signature 'array,MultiRadialKernel'
.newCVInfo(lambdas, kernel, methodObject, cvObject, suppress, ...)

Arguments

Create a New CVInfoObj Object

Description

Call newCVInfo and stores result in @cvInfo

Usage

.newCVInfoObj(lambdas, kernel, ...)

## S4 method for signature 'ANY,Kernel'
.newCVInfoObj(lambdas, kernel, methodObject, cvObject, suppress, ...)

Arguments

Complete a Classification Regression Step

Description

Methods dispatch appropriate typed fit methods based on the modeling object specified by the user and the feasible tx definitions. The value object returned depends on the underlying typed fit method.

Usage

.newClassificationFit(moClass, txObj, ...)

## S4 method for signature 'modelObj,TxInfoNoSubsets'
.newClassificationFit(moClass, txObj, response, data, suppress, ...)

## S4 method for signature 'ModelObj_SubsetList,TxInfoWithSubsets'
.newClassificationFit(moClass, data, response, txObj, suppress, ...)

## S4 method for signature 'modelObj,TxInfoWithSubsets'
.newClassificationFit(moClass, txObj, response, data, suppress, ...)

Arguments

Create an Object of Class ClassificationFitObj

Description

Method calls .newClassificationFit() and stores the result in @classif.

Usage

.newClassificationObj(moClass, txObj, ...)

## S4 method for signature 'ANY'
.newClassificationObj(moClass, txObj, data, response, suppress, ...)

Arguments

Complete an EARL Analysis

Description

Complete an EARL Analysis

Usage

.newEARL(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  lambdas,
  cvFolds,
  surrogate,
  iter,
  guess,
  kernel,
  fSet,
  suppress,
  ...
)

Arguments

Value

An EARL object

Complete First Stage Analysis of Contrasts for Interactive Q-Learning Algorithm

Description

Performs regression on the fitted contrasts of the second stage regression.

Usage

.newIQLearnFS_C(moMain, moCont, response, ...)

## S4 method for signature 'modelObj,modelObj,IQLearnSS'
.newIQLearnFS_C(moMain, moCont, response, data, txName, iter, suppress)

## S4 method for signature 'modelObj,'NULL',IQLearnSS'
.newIQLearnFS_C(moMain, moCont, response, data, txName, iter, suppress)

## S4 method for signature ''NULL',modelObj,IQLearnSS'
.newIQLearnFS_C(moMain, moCont, response, data, txName, iter, suppress)

Complete First Stage Analysis of Main Effects for Interactive Q-Learning Algorithm

Description

Performs regression on the fitted main effects the second stage regression.

Usage

.newIQLearnFS_ME(moMain, moCont, response, ...)

## S4 method for signature 'modelObj,modelObj,IQLearnSS'
.newIQLearnFS_ME(moMain, moCont, data, response, txName, iter, suppress)

## S4 method for signature 'modelObj,'NULL',IQLearnSS'
.newIQLearnFS_ME(moMain, moCont, data, response, txName, iter, suppress)

## S4 method for signature ''NULL',modelObj,IQLearnSS'
.newIQLearnFS_ME(moMain, moCont, data, response, txName, iter, suppress)

Complete First Stage Analysis of Residuals for Interactive Q-Learning Algorithm

Description

Performs log-linear regression on the residuals.

Usage

.newIQLearnFS_VHet(object, moMain, moCont, ...)

## S4 method for signature 'IQLearnFS_C,modelObj,modelObj'
.newIQLearnFS_VHet(object, moMain, moCont, data, iter, suppress)

## S4 method for signature 'IQLearnFS_C,modelObj,'NULL''
.newIQLearnFS_VHet(object, moMain, moCont, data, iter, suppress)

## S4 method for signature 'IQLearnFS_C,'NULL',modelObj'
.newIQLearnFS_VHet(object, moMain, moCont, data, iter, suppress)

Complete Second Stage Analysis of Interactive Q-Learning Algorithm

Description

Performs the regression of the outcome.

Usage

.newIQLearnSS(moMain, moCont, ...)

## S4 method for signature 'modelObj,modelObj'
.newIQLearnSS(moMain, moCont, data, response, txName, iter, suppress)

## S4 method for signature 'modelObj,'NULL''
.newIQLearnSS(moMain, moCont, data, response, txName, iter, suppress)

## S4 method for signature ''NULL',modelObj'
.newIQLearnSS(moMain, moCont, data, response, txName, iter, suppress)

Create a KernelObj

Description

Processes input to determine type of kernel, creates it, and stores in @slot kernel.

Usage

.newKernelObj(kernel, model, ...)

## S4 method for signature 'character,formula'
.newKernelObj(kernel, model, data, kparam = NULL, ...)

## S4 method for signature 'list,list'
.newKernelObj(kernel, model, data, kparam = NULL, ...)

Arguments

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

Value

A Learning object

Combine model object models

Description

Combines moMain and moCont into a single modeling object.

Usage

.newModel(moMain, moCont, ...)

## S4 method for signature 'modelObj,modelObj'
.newModel(moMain, moCont, txName, suppress)

## S4 method for signature 'modelObj,'NULL''
.newModel(moMain, moCont, txName, suppress)

## S4 method for signature ''NULL',modelObj'
.newModel(moMain, moCont, txName, suppress)

Complete an OWL Analysis

Description

Complete an OWL Analysis

Usage

.newOWL(
  moPropen,
  data,
  response,
  txName,
  lambdas,
  cvFolds,
  kernel,
  fSet,
  surrogate,
  suppress,
  guess,
  ...
)

Arguments

Value

An OWL object

Complete an Optimization Step

Description

Dispatches appropriate methods to optimize an object function.

Usage

.newOptim(kernel, ...)

## S4 method for signature 'LinearKernel'
.newOptim(kernel, lambda, methodObject, suppress, ...)

## S4 method for signature 'Kernel'
.newOptim(kernel, lambda, methodObject, suppress, ...)

Create an OptimObj Object

Description

Call newOptim and stores result under common name

Usage

.newOptimObj(methodObject, kernel, ...)

## S4 method for signature 'ANY'
.newOptimObj(methodObject, lambda, suppress, ...)

## S4 method for signature '.rwl'
.newOptimObj(methodObject, lambda, suppress, ...)

Arguments

Estimate the Optimal Treatment and Value Using Classification

Description

Method dispatches the appropriate function to obtain estimates for the optimal treatment and value using classification.

Usage

.newOptimalClass(response, ...)

## S4 method for signature 'vector'
.newOptimalClass(
  moPropen,
  moMain,
  moCont,
  moClass,
  data,
  response,
  txName,
  iter,
  fSet,
  suppress,
  ...
)

## S4 method for signature 'OptimalClass'
.newOptimalClass(
  moPropen,
  moMain,
  moCont,
  moClass,
  data,
  response,
  txName,
  iter,
  fSet,
  suppress,
  ...
)

Complete a the Coarsened/Missing Data Analysis

Description

Dispatches appropriate coarsened or missing data perspective method.

Usage

.newOptimalSeq(moPropen, moMain, moCont, fSet, ...)

## S4 method for signature 
## 'ModelObj_DecisionPointList,
##   ModelObj_DecisionPointList,
##   ModelObj_DecisionPointList,
##   list'
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 
## 'ModelObj_DecisionPointList,ModelObj_DecisionPointList,‘NULL',list’
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 
## 'ModelObj_DecisionPointList,‘NULL',ModelObj_DecisionPointList,list’
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 'ModelObj_DecisionPointList,'NULL','NULL',list'
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 
## 'ModelObj_DecisionPointList,
##   ModelObj_DecisionPointList,
##   ModelObj_DecisionPointList,
##   ‘NULL'’
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 
## 'ModelObj_DecisionPointList,ModelObj_DecisionPointList,‘NULL','NULL'’
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 
## 'ModelObj_DecisionPointList,‘NULL',ModelObj_DecisionPointList,'NULL'’
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 'ModelObj_DecisionPointList,'NULL','NULL','NULL''
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 'modelObj,modelObj,modelObj,'NULL''
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 'modelObj,modelObj,'NULL','NULL''
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 'modelObj,'NULL',modelObj,'NULL''
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 'modelObj,'NULL','NULL','NULL''
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 'modelObj,modelObj,modelObj,'function''
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 'modelObj,modelObj,'NULL','function''
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 'modelObj,'NULL',modelObj,'function''
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 'modelObj,'NULL','NULL','function''
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 'ModelObj_SubsetList,modelObj,modelObj,'function''
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 'ModelObj_SubsetList,modelObj,'NULL','function''
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 'ModelObj_SubsetList,'NULL',modelObj,'function''
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 'ModelObj_SubsetList,'NULL','NULL','function''
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 
## 'ModelObj_SubsetList,
##   ModelObj_SubsetList,
##   ModelObj_SubsetList,
##   ‘function'’
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 
## 'ModelObj_SubsetList,ModelObj_SubsetList,‘NULL','function'’
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 
## 'ModelObj_SubsetList,‘NULL',ModelObj_SubsetList,'function'’
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 
## 'modelObj,ModelObj_SubsetList,ModelObj_SubsetList,‘function'’
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 'modelObj,ModelObj_SubsetList,'NULL','function''
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

## S4 method for signature 'modelObj,'NULL',ModelObj_SubsetList,'function''
.newOptimalSeq(
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimesObj,
  fSet,
  iter,
  suppress,
  argsList,
  ...
)

Arguments

Perform an Outcome Regression Step

Description

Dispatch appropriate methods to perform outcome regression step.

Usage

.newOutcomeFit(moMain, moCont, txObj, iter, ...)

## S4 method for signature ''NULL','NULL',TxObj,'NULL''
.newOutcomeFit(moMain, moCont, txObj, iter, data, response, suppress)

## S4 method for signature 'modelObj,modelObj,TxInfoNoSubsets,'NULL''
.newOutcomeFit(moMain, moCont, txObj, iter, data, response, suppress)

## S4 method for signature 'modelObj,'NULL',TxInfoNoSubsets,'NULL''
.newOutcomeFit(moMain, moCont, txObj, iter, data, response, suppress)

## S4 method for signature ''NULL',modelObj,TxInfoNoSubsets,'NULL''
.newOutcomeFit(moMain, moCont, txObj, iter, data, response, suppress)

## S4 method for signature 'modelObj,modelObj,TxInfoWithSubsets,'NULL''
.newOutcomeFit(moMain, moCont, txObj, iter, data, response, suppress)

## S4 method for signature 'modelObj,'NULL',TxInfoWithSubsets,'NULL''
.newOutcomeFit(moMain, moCont, txObj, iter, data, response, suppress)

## S4 method for signature ''NULL',modelObj,TxInfoWithSubsets,'NULL''
.newOutcomeFit(moMain, moCont, txObj, iter, data, response, suppress)

## S4 method for signature 'modelObj,modelObj,TxInfoWithSubsets,integer'
.newOutcomeFit(moMain, moCont, response, txObj, data, iter, suppress)

## S4 method for signature 'modelObj,modelObj,TxInfoNoSubsets,integer'
.newOutcomeFit(moMain, moCont, response, txObj, data, iter, suppress)

## S4 method for signature 
## 'ModelObj_SubsetList,ModelObj_SubsetList,TxInfoWithSubsets,integer'
.newOutcomeFit(moMain, moCont, response, txObj, data, iter, suppress)

## S4 method for signature 
## 'ModelObj_SubsetList,ModelObj_SubsetList,TxInfoWithSubsets,ANY'
.newOutcomeFit(moMain, moCont, txObj, data, response, iter, suppress)

## S4 method for signature 'ModelObj_SubsetList,'NULL',TxInfoWithSubsets,ANY'
.newOutcomeFit(moMain, moCont, txObj, data, response, iter, suppress)

## S4 method for signature ''NULL',ModelObj_SubsetList,TxInfoWithSubsets,ANY'
.newOutcomeFit(moMain, moCont, txObj, data, response, iter, suppress)

Arguments

Create a new OutcomeObj object

Description

Calls newOutcomeFit and stores in @outcome.

Usage

.newOutcomeObj(moMain, moCont, txObj, iter, ...)

## S4 method for signature 'ANY,ANY,ANY'
.newOutcomeObj(moMain, moCont, txObj, data, response, iter, suppress)

## S4 method for signature 
## 'ModelObj_DecisionPointList,ModelObj_DecisionPointList,TxInfoList'
.newOutcomeObj(moMain, moCont, txObj, data, response, iter, suppress, ...)

## S4 method for signature 'ModelObj_DecisionPointList,ANY,TxInfoList'
.newOutcomeObj(moMain, moCont, txObj, data, response, iter, suppress, ...)

## S4 method for signature ''NULL',ModelObj_DecisionPointList,TxInfoList'
.newOutcomeObj(moMain, moCont, txObj, data, response, iter, suppress, ...)

Arguments

Complete a Propensity Regression Step

Description

Dispatches appropriate method for completing propensity regressions.

Usage

.newPropensityFit(moPropen, txObj, ...)

## S4 method for signature 'modelObj,TxInfoNoSubsets'
.newPropensityFit(moPropen, txObj, data, suppress)

## S4 method for signature 'modelObj,TxInfoWithSubsets'
.newPropensityFit(moPropen, txObj, data, suppress)

## S4 method for signature 'ModelObj_SubsetList,TxInfoWithSubsets'
.newPropensityFit(moPropen, txObj, data, suppress)

Arguments

Create a new PropensityObj object

Description

Calls newPropensityFit and stores result in @propen.

Usage

.newPropensityObj(moPropen, txObj, data, suppress, ...)

## S4 method for signature 'ANY,ANY'
.newPropensityObj(moPropen, txObj, data, suppress)

## S4 method for signature 'ModelObj_DecisionPointList,TxInfoList'
.newPropensityObj(moPropen, txObj, data, suppress)

Arguments

Perform a Step of the Q-Learning Algorithm

Description

Method performs all necessary regression and predictions steps for a single step of the Q-learning algorithm.

Usage

.newQLearn(response, ...)

## S4 method for signature 'vector'
.newQLearn(moMain, moCont, fSet, response, data, txName, iter, suppress)

## S4 method for signature 'QLearn'
.newQLearn(moMain, moCont, fSet, response, data, txName, iter, suppress)

Arguments

Value

an object of class QLearn.

Complete a Residual Weighted Learning Analysis

Description

Complete a Residual Weighted Learning Analysis

Usage

.newRWL(kernel, ...)

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

Arguments

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

Value

An RWL object

Create a new Regime object

Description

Create a new Regime object

Usage

.newRegime(object)

## S4 method for signature ''function''
.newRegime(object)

Arguments

Create a New RegimeObj Object

Description

Calls newRegime and stores object in @regime.

Usage

.newRegimeObj(object)

## S4 method for signature ''function''
.newRegimeObj(object)

## S4 method for signature 'list'
.newRegimeObj(object)

Arguments

Create TxObj Object

Description

Creates appropriate TxObj based on class of fSet and txName.

Usage

.newTxObj(fSet, txName, ...)

## S4 method for signature 'ANY,character'
.newTxObj(fSet, txName, ...)

## S4 method for signature ''NULL',character'
.newTxObj(fSet, txName, data, suppress, verify = TRUE)

## S4 method for signature ''function',character'
.newTxObj(fSet, txName, data, suppress, verify = TRUE)

## S4 method for signature 'list,list'
.newTxObj(fSet, txName, data, suppress, verify = TRUE)

## S4 method for signature ''NULL',list'
.newTxObj(fSet, txName, data, suppress, verify = TRUE)

Create TxSubset Object

Description

Processes input to determine ptsSubset and singleton to create a TxSubset object

Usage

.newTxSubset(fSet, superset, ...)

## S4 method for signature ''function',ANY'
.newTxSubset(fSet, superset, ..., data, verify, suppress)

## S4 method for signature ''function',integer'
.newTxSubset(fSet, superset, txName, data, verify, ...)

## S4 method for signature ''function',character'
.newTxSubset(fSet, superset, ..., txName, data, verify)

Complete a Regression Step

Description

This function completes a regression step and stores a character object used to identify the purpose of the step, such as a propensity or outcome regression.

Usage

.newTypedFit(modelObj, txObj, ...)

## S4 method for signature 'modelObj,TxInfoNoSubsets'
.newTypedFit(modelObj, txObj, response, data, type, suppress)

## S4 method for signature 'ModelObj_SubsetList,TxInfoWithSubsets'
.newTypedFit(modelObj, txObj, data, response, type, suppress)

## S4 method for signature 'modelObj,TxInfoWithSubsets'
.newTypedFit(modelObj, txObj, data, response, type, suppress)

Arguments

Create a new TypedFitObj object

Description

Call newTypedFit and stores result in @fit

Usage

.newTypedFitObj(modelObj, txObj, ...)

## S4 method for signature 'ANY'
.newTypedFitObj(modelObj, txObj, response, data, type, suppress)

Arguments

Perform Classification Step

Description

Perform Classification Step

Usage

.optimalClass(
  moPropen,
  moMain,
  moCont,
  moClass,
  data,
  response,
  txName,
  iter,
  fSet,
  suppress,
  step
)

Arguments

Value

an object of class OptimalClass

Define the Objective Function

Description

Method is defined by inheriting classes to define the objective function optmized by the genetic algorithm.

Usage

.seqFunc(eta, txObj, ...)

## S4 method for signature 'numeric,TxInfoList'
.seqFunc(eta, txObj, regimesObj, l.data, outcomeObj, propenObj, response)

## S4 method for signature 'numeric,TxObj'
.seqFunc(eta, txObj, regimesObj, l.data, outcomeObj, propenObj, response)

Class BOWL

Description

Class BOWL contains results from a single step of BOWL algorithm.

Slots

step

Integer indicating step of the algorithm

analysis

Contains a Learning or LearningMulti object.

analysis@txInfo

Feasible tx information.

analysis@propen

Propensity regression analysis.

analysis@outcome

Outcome regression analysis.

analysis@cvInfo

Cross-validation analysis if single regime.

analysis@optim

Optimization analysis if single regime.

analysis@optimResult

list of cross-validation and optimization results if multiple regimes. optimResult[[i]]@cvInfo and optimResult[[i]]@optim.

analysis@optimal

Estimated optimal Tx and value.

analysis@call

Unevaluated call to statistical method.

prodPi

Vector of the products of the propensity for the tx received

sumR

Vector of the sum of the rewards

index

Vector indicating compliance with estimated optimal regime

Methods Available for Objects of Class BOWL

Description

Methods Available for Objects of Class BOWL

Usage

## S4 method for signature 'BOWL'
print(x, ...)

## S4 method for signature 'BOWL'
show(object)

Class BOWLBasic

Description

Class BOWLBasic contains the results for a single OWL analysis and the weights needed for next iteration

Slots

analysis

Contains a Learning or LearningMulti object.

analysis@txInfo

Feasible tx information.

analysis@propen

Propensity regression analysis.

analysis@outcome

Outcome regression analysis.

analysis@cvInfo

Cross-validation analysis if single regime.

analysis@optim

Optimization analysis if single regime.

analysis@optimResult

list of cross-validation and optimization results if multiple regimes. optimResult[[i]]@cvInfo and optimResult[[i]]@optim.

analysis@optimal

Estimated optimal Tx and value.

analysis@call

Unevaluated call to statistical method.

prodPi

Vector of the products of the propensity for the tx received

sumR

Vector of the sum of the rewards

index

Vector indicating compliance with estimated optimal regime

Methods Available for Objects of Class BOWLBasic

Description

Methods Available for Objects of Class BOWLBasic

Usage

## S4 method for signature 'BOWLBasic'
Call(name, ...)

## S4 method for signature 'BOWLBasic'
coef(object, ...)

## S4 method for signature 'BOWLBasic'
cvInfo(object, ...)

## S4 method for signature 'BOWLBasic'
estimator(x, ...)

## S4 method for signature 'BOWLBasic'
fitObject(object, ...)

## S4 method for signature 'BOWLBasic'
optimObj(object, ...)

## S4 method for signature 'BOWLBasic,data.frame'
optTx(x, newdata, ...)

## S4 method for signature 'BOWLBasic,missing'
optTx(x, newdata, ...)

## S4 method for signature 'BOWLBasic'
outcome(object, ...)

## S4 method for signature 'BOWLBasic,ANY'
plot(x, suppress = FALSE, ...)

## S4 method for signature 'BOWLBasic'
print(x, ...)

## S4 method for signature 'BOWLBasic'
propen(object, ...)

## S4 method for signature 'BOWLBasic'
regimeCoef(object, ...)

## S4 method for signature 'BOWLBasic'
show(object)

## S4 method for signature 'BOWLBasic'
summary(object, ...)

Class BOWLObj

Description

Class BOWLObj contains product and sum information required for iteration

Slots

prodPi

Vector of the products of the propensity for the tx received

sumR

Vector of the sum of the rewards

index

Vector indicating compliance with estimated optimal regime

Class CVBasic

Description

Class CVBasic holds cross-validation procedure parameters

Slots

folds

An integer

sample

A lsit

Class CVInfo

Description

Class CVInfo holds cross-validation procedure results

Slots

value

Values obtained for each parameter combination

params

list of parameter values considered

optimal

list of optimal parameter values

Methods Available for Objects of Class CVInfo

Description

Methods Available for Objects of Class CVInfo

.getPars retrieves parameters considered in cross-validation.

.getOptimal retrieves optimal parameters identified in cross-validation.

.getValue retrieves values obtained in cross-validation.

cvInfo retrieves cross-validation information.

print print cross-validation results.

show display cross-validation results.

summary summarize cross-validation results.

Usage

## S4 method for signature 'CVInfo'
.getPars(object)

## S4 method for signature 'CVInfo'
.getOptimal(object)

## S4 method for signature 'CVInfo'
.getValue(object)

## S4 method for signature 'CVInfo'
cvInfo(object)

## S4 method for signature 'CVInfo'
print(x, ...)

## S4 method for signature 'CVInfo'
show(object)

## S4 method for signature 'CVInfo'
summary(object, ...)

Class CVInfo2Par

Description

Class CVInfo2Par holds information regarding cross-validation procedure when multiple kernel parameters and tuning parameters are considered.

Slots

value

Matrix of values at parameters considered

Methods Available for Objects of Class CVInfo2Par

Description

Methods Available for Objects of Class CVInfo2Par

Class CVInfoLambda

Description

Class CVInfoLambda holds information regarding cross-validation procedure when only multiple lambda values are considered.

Slots

value

Array of values at tuning parameters considered

Methods Available for Objects of Class CVInfoLambda

Description

Methods Available for Objects of Class CVInfoLambda

Class CVInfoObj

Description

Class CVInfoObj holds information regarding cross-validation procedure under a common name.

Slots

cvInfo

ANY expected to be CVInfo or NULL

Methods Available for Objects of Class CVInfoObj

Description

Call methods equivalently named for object inheriting from CVInfo. Methods dispached depend on object in @cvInfo.

Usage

## S4 method for signature 'CVInfoObj'
.getPars(object)

## S4 method for signature 'CVInfoObj'
.getOptimal(object)

## S4 method for signature 'CVInfoObj'
.getValue(object)

## S4 method for signature 'CVInfoObj'
cvInfo(object)

## S4 method for signature 'CVInfoObj'
print(x, ...)

## S4 method for signature 'CVInfoObj'
show(object)

## S4 method for signature 'CVInfoObj'
summary(object, ...)

Class CVInfokParam

Description

Class CVInfokParam holds information regarding cross-validation procedure when only multiple kernel parameters values are considered.

Slots

value

Array of values at parameters considered

Methods Available for Objects of Class CVInfokParam

Description

Methods Available for Objects of Class CVInfokParam

Retrieve Unevaluated Original Call

Description

Returns the unevaluated original call to a DynTxRegime statistical method.

Usage

Call(name, ...)

Arguments

Details

Methods are defined for all statistical methods implemented in DynTxRegime.

Class ClassificationFit

Description

Class ClassificationFit combines a TypedFit object and a TxInfoNoSubsets object to define a classification regression result when subsets are not identified.

Methods Available for Objects of Class ClassificationFit

Description

.predictAll(object, newdata) predicts optimal treatment

Usage

## S4 method for signature 'ClassificationFit'
classif(object, ...)

## S4 method for signature 'ClassificationFit'
coef(object, ...)

## S4 method for signature 'ClassificationFit'
fitObject(object, ...)

## S4 method for signature 'ClassificationFit,ANY'
plot(x, suppress = FALSE, ...)

## S4 method for signature 'ClassificationFit'
predict(object, ...)

## S4 method for signature 'ClassificationFit,data.frame'
.predictAll(object, newdata, ...)

## S4 method for signature 'ClassificationFit'
print(x, ...)

## S4 method for signature 'ClassificationFit'
show(object)

## S4 method for signature 'ClassificationFit'
summary(object, ...)

Class ClassificationFit_SubsetList

Description

Class ClassificationFit_SubsetList contains a TypedFit_SubsetList object to define classification regression results when subsets are identified and modeled uniquely.

Methods Available for Objects of Class ClassificationFit_SubsetList

Description

.predictAll(object, newdata) predicts optimal treatment

Usage

## S4 method for signature 'ClassificationFit_SubsetList'
classif(object, ...)

## S4 method for signature 'ClassificationFit_SubsetList'
coef(object, ...)

## S4 method for signature 'ClassificationFit_SubsetList'
fitObject(object, ...)

## S4 method for signature 'ClassificationFit_SubsetList'
predict(object, ...)

## S4 method for signature 'ClassificationFit_SubsetList,data.frame'
.predictAll(object, newdata, ...)

## S4 method for signature 'ClassificationFit_SubsetList'
summary(object, ...)

Class ClassificationFit_fSet

Description

Class ClassificationFit_fSet contains a TypedFit_fSet object to define a classification regression result when subsets are identified but not modeled uniquely.

Methods Available for Objects of Class ClassificationFit_fSet

Description

.predictAll(object, newdata) predicts optimal treatment

Usage

## S4 method for signature 'ClassificationFit_fSet'
classif(object, ...)

## S4 method for signature 'ClassificationFit_fSet'
coef(object, ...)

## S4 method for signature 'ClassificationFit_fSet'
fitObject(object, ...)

## S4 method for signature 'ClassificationFit_fSet,data.frame'
.predictAll(object, newdata, ...)

## S4 method for signature 'ClassificationFit_fSet'
summary(object, ...)

Class ClassificationObj

Description

Stores classification regression results under a common name.

Slots

classif

ANY - required to be NA, ClassificationFit, ClassificationFit_fSet, or
ClassificationFit_SubsetList.

Methods Available for Objects of Class ClassificationObj

Description

.predictAll(object, newdata) predicts optimal treatment

Usage

## S4 method for signature 'ClassificationObj'
classif(object, ...)

## S4 method for signature 'ClassificationObj'
coef(object, ...)

## S4 method for signature 'ClassificationObj'
fitObject(object, ...)

## S4 method for signature 'ClassificationObj,ANY'
plot(x, suppress = FALSE, ...)

## S4 method for signature 'ClassificationObj'
predict(object, ...)

## S4 method for signature 'ClassificationObj,data.frame'
.predictAll(object, newdata, ...)

## S4 method for signature 'ClassificationObj'
print(x, ...)

## S4 method for signature 'ClassificationObj'
show(object)

## S4 method for signature 'ClassificationObj'
summary(object, ...)

Identify Statistical Method Used to Obtain Result

Description

Prints are displays a brief description of the statistical method used to obtain the input object.

Usage

DTRstep(object)

Arguments

Details

Methods are defined for all statistical methods implemented in DynTxRegime.

Class DecisionPointList

Description

Class DecisionListList represents a List for decision points. This class extends List to require non-zero length.

Usage

## S4 method for signature 'DecisionPointList'
initialize(.Object, ...)

Methods Available for Objects of Class DecisionPointList

Description

Methods Available for Objects of Class DecisionPointList

plot(x,suppress) generates plots of the regression analysis for each decision point. If suppress = FALSE, titles of plot will include the decision point identifier.

print(x) adds decision point information to print statements. Each decision point is preceded by 'dp=x' where x is the decision point number.

show(object) adds decision point information to show statements. Each decision point is preceded by 'dp=x' where x is the decision point number.

Usage

## S4 method for signature 'DecisionPointList,ANY'
plot(x, suppress = FALSE, ...)

## S4 method for signature 'DecisionPointList'
print(x, ...)

## S4 method for signature 'DecisionPointList'
show(object)

Class DynTxRegime

Description

Class DynTxRegime is a component of all statistical methods implemented in the package. This class contains the estimated optimal Tx, decision functions if applicable, the estimated value and the original unevaluated call. It extends internal class OptimalObj.

Slots

call

object of class call or NULL

Hidden methods

Description

Hidden methods

Retrieve the Decision Point to which modelObj Pertains

Retrieve the Subset to which modelObj Pertains

Constructor method of SubsetList Class

Compare Equivalence of Provided Treatment Vectors

Convert a -1/1 Tx to User Provided Tx

Convert a User Provided Tx Variable to Binary -1/1

Convert Provided Treatment Vector to Appropriate Class

Get Treatment Levels

Retrieve Superset

Retrieve Treatment Variable Name

Ensure Validity of Provided Treatment Vector

Make Predictions for all Treatments.

Make Predictions for all Treatments.

Uses .predictAll() defined for OutcomeObj objects

Uses optTx defined for DynTxRegime objects

Print Q-Learning Information

Show Q-Learning Information

Usage

tmp(x)

## S4 method for signature 'List'
initialize(.Object, ...)

## S4 replacement method for signature 'List'
x[[i]] <- value

## S4 replacement method for signature 'DecisionPointList'
x[[i]] <- value

.getDecisionPoint(object)

.getSubset(object)

## S4 method for signature 'SubsetList'
initialize(.Object, ...)

## S4 replacement method for signature 'SubsetList'
x[[i]] <- value

## S4 method for signature 'ModelObj_SubsetList,ANY'
plot(x, y, ...)

## S4 method for signature 'ModelObj_DecisionPointList,ANY'
plot(x, y, ...)

.compareTx(object, vec1, vec2)

.convertFromBinary(txObj, ...)

.convertToBinary(txObj, ...)

.convertTx(object, txVec)

.getLevels(object, txVec)

.getSuperset(object)

.getTxName(object)

.validTx(object, txVec)

.getPtsSubset(object)

.getSingleton(object)

.getSubsetRule(object)

.getSubsets(object)

## S4 method for signature 'ANY'
.getSubsetRule(object)

.identifySubsets(fSetResult, input, ...)

## S4 method for signature 'list,data.frame'
.identifySubsets(fSetResult, input, ..., fSet)

## S4 method for signature 'list,list'
.identifySubsets(fSetResult, input, ..., fSet)

## S4 method for signature 'ANY,data.frame'
.identifySubsets(fSetResult, input, ..., fSet)

## S4 method for signature 'ANY,list'
.identifySubsets(fSetResult, input, ..., fSet)

## S4 method for signature 'ANY,ANY'
.identifySubsets(fSetResult, input, ..., fSet)

## S4 method for signature 'TxInfoList'
initialize(.Object, ...)

.predictAll(object, newdata, ...)

.predictMu(object, data, ...)

## S4 method for signature 'QLearn'
Call(name, ...)

## S4 method for signature 'QLearn'
coef(object, ...)

## S4 method for signature 'QLearn'
DTRstep(object)

## S4 method for signature 'QLearn'
estimator(x, ...)

## S4 method for signature 'QLearn'
fitObject(object, ...)

## S4 method for signature 'QLearn,data.frame'
optTx(x, newdata, ...)

## S4 method for signature 'QLearn,missing'
optTx(x, newdata, ...)

## S4 method for signature 'QLearn'
outcome(object, ...)

## S4 method for signature 'QLearn,missing'
plot(x, y, suppress = FALSE, ...)

## S4 method for signature 'QLearn'
print(x, ...)

## S4 method for signature 'QLearn'
show(object)

## S4 method for signature 'QLearn'
summary(object, ...)

## S4 method for signature 'IQLearnSS'
DTRstep(object)

## S4 method for signature 'IQLearnFS_C'
DTRstep(object)

## S4 method for signature 'IQLearnFS_ME'
DTRstep(object)

## S4 method for signature 'IQLearnFS_VHet'
DTRstep(object)

.getNumPars(object)

.getParNames(object)

.getPars(object)

.getRegimeFunction(object)

.predictOptimalTx(x, newdata, ...)

.setPars(object, pars)

## S4 method for signature 'OptimalSeq'
Call(name, ...)

## S4 method for signature 'OptimalSeq'
coef(object, ...)

## S4 method for signature 'OptimalSeq'
DTRstep(object)

## S4 method for signature 'OptimalSeq'
estimator(x, ...)

## S4 method for signature 'OptimalSeq'
fitObject(object, ...)

## S4 method for signature 'OptimalSeq,data.frame'
optTx(x, newdata, ...)

## S4 method for signature 'OptimalSeq,missing'
optTx(x, newdata, ...)

## S4 method for signature 'OptimalSeq'
outcome(object, ...)

## S4 method for signature 'OptimalSeq,missing'
plot(x, y, suppress = FALSE, ...)

## S4 method for signature 'OptimalSeq'
propen(object, ...)

## S4 method for signature 'OptimalSeq'
regimeCoef(object, ...)

## S4 method for signature 'OptimalSeq'
summary(object, ...)

## S4 method for signature 'OptimalClass'
Call(name, ...)

## S4 method for signature 'OptimalClass'
coef(object, ...)

## S4 method for signature 'OptimalClass'
DTRstep(object)

## S4 method for signature 'OptimalClass'
estimator(x, ...)

## S4 method for signature 'OptimalClass'
fitObject(object, ...)

## S4 method for signature 'OptimalClass,data.frame'
optTx(x, newdata, ...)

## S4 method for signature 'OptimalClass,missing'
optTx(x, newdata, ...)

## S4 method for signature 'OptimalClass'
outcome(object, ...)

## S4 method for signature 'OptimalClass,missing'
plot(x, suppress = FALSE, ...)

## S4 method for signature 'OptimalClass'
propen(object, ...)

## S4 method for signature 'OptimalClass'
summary(object, ...)

## S4 method for signature 'Kernel'
initialize(.Object, data, model, kparam, ...)

.getKernelX(data, object, ...)

.kernel(object, x1, x2, ...)

.kernelNumPars(object, ...)

.dPhiFunc(surrogate, ...)

.optim(surrogate, ...)

.phiFunc(surrogate, ...)

.dobjFn(par, methodObject, kernel, ...)

.objFn(par, methodObject, kernel, ...)

.subsetObject(methodObject, ...)

.valueFunc(methodObject, ...)

.optimFunc(methodObject, ...)

## S4 method for signature 'CVBasic'
initialize(.Object, cvFolds, txVec, ...)

.getValue(object)

.getOptimal(object)

## S4 method for signature 'OWL'
Call(name, ...)

## S4 method for signature 'OWL'
coef(object, ...)

## S4 method for signature 'OWL'
cvInfo(object, ...)

## S4 method for signature 'OWL'
DTRstep(object)

## S4 method for signature 'OWL'
estimator(x, ...)

## S4 method for signature 'OWL'
fitObject(object, ...)

## S4 method for signature 'OWL'
optimObj(object, ...)

## S4 method for signature 'OWL,data.frame'
optTx(x, newdata, ...)

## S4 method for signature 'OWL,missing'
optTx(x, newdata, ...)

## S4 method for signature 'OWL'
outcome(object, ...)

## S4 method for signature 'OWL,missing'
plot(x, y, suppress = FALSE, ...)

## S4 method for signature 'OWL'
propen(object, ...)

## S4 method for signature 'OWL'
regimeCoef(object, ...)

## S4 method for signature 'OWL'
summary(object, ...)

## S4 method for signature 'RWL'
Call(name, ...)

## S4 method for signature 'RWL'
coef(object, ...)

## S4 method for signature 'RWL'
cvInfo(object, ...)

## S4 method for signature 'RWL'
DTRstep(object)

## S4 method for signature 'RWL'
estimator(x, ...)

## S4 method for signature 'RWL'
fitObject(object, ...)

## S4 method for signature 'RWL'
optimObj(object, ...)

## S4 method for signature 'RWL,data.frame'
optTx(x, newdata, ...)

## S4 method for signature 'RWL,missing'
optTx(x, newdata, ...)

## S4 method for signature 'RWL'
outcome(object, ...)

## S4 method for signature 'RWL,missing'
plot(x, y, suppress = FALSE, ...)

## S4 method for signature 'RWL'
propen(object, ...)

## S4 method for signature 'RWL'
regimeCoef(object, ...)

## S4 method for signature 'RWL'
residuals(object, ...)

## S4 method for signature 'RWL'
summary(object, ...)

.newBOWL(BOWLObj, ...)

## S4 method for signature 'BOWL'
Call(name, ...)

## S4 method for signature 'BOWL'
cvInfo(object, ...)

## S4 method for signature 'BOWL'
coef(object, ...)

## S4 method for signature 'BOWL'
DTRstep(object)

## S4 method for signature 'BOWL'
estimator(x, ...)

## S4 method for signature 'BOWL'
fitObject(object, ...)

## S4 method for signature 'BOWL'
optimObj(object, ...)

## S4 method for signature 'BOWL,data.frame'
optTx(x, newdata, ...)

## S4 method for signature 'BOWL,missing'
optTx(x, newdata, ...)

## S4 method for signature 'BOWL'
outcome(object, ...)

## S4 method for signature 'BOWL,missing'
plot(x, y, suppress = FALSE, ...)

## S4 method for signature 'BOWL'
propen(object, ...)

## S4 method for signature 'BOWL'
regimeCoef(object, ...)

## S4 method for signature 'BOWL'
summary(object, ...)

## S4 method for signature 'EARL'
Call(name, ...)

## S4 method for signature 'EARL'
coef(object, ...)

## S4 method for signature 'EARL'
cvInfo(object, ...)

## S4 method for signature 'EARL'
DTRstep(object)

## S4 method for signature 'EARL'
estimator(x, ...)

## S4 method for signature 'EARL'
fitObject(object, ...)

## S4 method for signature 'EARL'
optimObj(object, ...)

## S4 method for signature 'EARL,data.frame'
optTx(x, newdata, ...)

## S4 method for signature 'EARL,missing'
optTx(x, newdata, ...)

## S4 method for signature 'EARL'
outcome(object, ...)

## S4 method for signature 'EARL,missing'
plot(x, y, suppress = FALSE, ...)

## S4 method for signature 'EARL'
propen(object, ...)

## S4 method for signature 'EARL'
regimeCoef(object, ...)

## S4 method for signature 'EARL'
summary(object, ...)

Methods Available for Objects of Class DynTxRegime

Description

Methods Available for Objects of Class DynTxRegime

Call(name) retrieves the unevaluated call to the original statistical method

Usage

## S4 method for signature 'DynTxRegime'
Call(name, ...)

Class EARL

Description

Class EARL contains results for an EARL analysis.

Slots

analysis

Contains a Learning or LearningMulti object.

analysis@txInfo

Feasible tx information.

analysis@propen

Propensity regression analysis.

analysis@outcome

Outcome regression analysis.

analysis@cvInfo

Cross-validation analysis if single regime.

analysis@optim

Optimization analysis if single regime.

analysis@optimResult

list of cross-validation and optimization results if multiple regimes. optimResult[[i]]@cvInfo and optimResult[[i]]@optim.

analysis@optimal

Estimated optimal Tx and value.

analysis@call

Unevaluated call to statistical method.

Methods For Post-Processing of Regression Analysis

outcome

: Retrieve value object returned by outcome regression methods.

propen

: Retrieve value object returned by propensity regression methods.

coef

: Retrieve parameter estimates for all regression steps.

fitObject

: Retrieve value object returned by regression methods.

plot

: Generate plots for regression analyses.

Methods For Post-Processing of Optimization Analysis

cvInfo

: Retrieve cross-validation results.

optimObj

: Retrieve value object returned by optimization method(s).

regimeCoef

: Retrieve estimated parameters for optimal tx regime.

Methods For Accessing Main Results

DTRstep

: Retrieve description of method used to create object.

estimator

: Retrieve the estimated value of the estimated optimal regime for the training data set.

optTx

: Retrieve/predict the estimated decision functions and/or optimal tx.

print

: Print main results of analysis.

show

: Show main results of analysis.

summary

: Retrieve summary information.

Methods Available for Objects of Class EARL

Description

Methods Available for Objects of Class EARL

Usage

## S4 method for signature 'EARL'
print(x, ...)

## S4 method for signature 'EARL'
show(object)

Class ExpSurrogate

Description

Exponential surrogate for 0/1 loss.

Methods Available for Objects of Class ExpSurrogate

Description

Methods Available for Objects of Class ExpSurrogate

.phiFunc calculates exponential surrogate loss-function

.dphiFunc calculates derivative of exponential surrogate loss-function

Usage

## S4 method for signature 'ExpSurrogate'
.phiFunc(surrogate, u)

## S4 method for signature 'ExpSurrogate'
.dPhiFunc(surrogate, u, du)

Class HingeSurrogate

Description

Hinge surrogate for 0/1 loss function.

Methods Available for Objects of Class HingeSurrogate

Description

Utilizes dfoptim::hjk to obtain parameter estimates. Requires that the objective function be defined by the calling learning method. Returns NULL if optimization is not successful due to problems or the list object returned by dfoptim::hjk if optimization is successful.

Usage

## S4 method for signature 'HingeSurrogate'
.phiFunc(surrogate, u)

## S4 method for signature 'HingeSurrogate'
.dPhiFunc(surrogate, u, du)

## S4 method for signature 'HingeSurrogate'
.optim(surrogate, par, lambda, fn, gr, suppress, ...)

Class HuberHingeSurrogate

Description

Huberized hinge surrogate for 0/1 loss function.

Methods Available for Objects of Class HuberHingeSurrogate

Description

Methods Available for Objects of Class HuberHingeSurrogate

.phiFunc calculates huberized hinge surrogate loss-function

.dphiFunc calculates derivative of huberized hinge surrogate loss-function

Usage

## S4 method for signature 'HuberHingeSurrogate'
.phiFunc(surrogate, u)

## S4 method for signature 'HuberHingeSurrogate'
.dPhiFunc(surrogate, u, du)

Class IQLearnFS

Description

Class IQLearnFS contains results for a component of the first stage analysis of the interactive Q-Learning algorithm.

Methods Available for Objects of Class IQLearnFS

Description

Employs methods defined for QLearn

Usage

## S4 method for signature 'IQLearnFS'
print(x, ...)

## S4 method for signature 'IQLearnFS'
show(object)

## S4 method for signature 'IQLearnFS'
summary(object, ...)

Class IQLearnFS_C

Description

Class IQLearnFS_C contains the results for the first stage contrasts component of the interactive Q-Learning algorithm. Objects of this class are returned by iqLearnFSC().

Slots

txVec

: A numeric. treatment vector from training data

residuals

: A numeric. residuals of the fit

step

: Not used in this context.

outcome

: The outcome regression analysis

txInfo

: The feasible tx information

optimal

: The estimated optimal tx, decision function, and value

Methods For Post-Processing of Regression Analysis

outcome

: Retrieve value object returned by outcome regression methods.

coef

: Retrieve parameter estimates for all regression steps.

fitObject

: Retrieve value object returned by regression methods.

plot

: Generate plots for regression analyses.

Methods For Accessing Main Results

DTRstep

: Retrieve description of method used to create object.

estimator

: Retrieve the estimated value of the estimated optimal regime for the training data set.

optTx

: Retrieve/predict the estimated decision functions and/or optimal tx.

print

: Print main results of analysis.

show

: Show main results of analysis.

summary

: Retrieve summary information.

residuals

:Retrieve the residuals of the regression.

sd

:Retrieve the standard deviation of the residuals.

Methods Available for Objects of Class IQLearnFS_C

Description

Methods Available for Objects of Class IQLearnFS_C

Usage

## S4 method for signature 'IQLearnFS_C'
print(x, ...)

## S4 method for signature 'IQLearnFS_C'
show(object)

Class IQLearnFS_ME

Description

Class IQLearnFS_ME contains the results for the first stage main effects component of the interactive Q-Learning algorithm. Objects of this class are returned by iqLearnFSM().

Slots

step

: Not used in this context.

outcome

: The outcome regression analysis

txInfo

: The feasible tx information

optimal

: The estimated optimal tx, decision function, and value

Methods For Post-Processing of Regression Analysis

outcome

: Retrieve value object returned by outcome regression methods.

coef

: Retrieve parameter estimates for all regression steps.

fitObject

: Retrieve value object returned by regression methods.

plot

: Generate plots for regression analyses.

Methods For Accessing Main Results

DTRstep

: Retrieve description of method used to create object.

estimator

: Retrieve the estimated value of the estimated optimal regime for the training data set.

optTx

: Retrieve/predict the estimated decision functions and/or optimal tx.

print

: Print main results of analysis.

show

: Show main results of analysis.

summary

: Retrieve summary information.

Methods Available for Objects of Class IQLearnFS_ME

Description

Methods Available for Objects of Class IQLearnFS_ME

Usage

## S4 method for signature 'IQLearnFS_ME'
print(x, ...)

## S4 method for signature 'IQLearnFS_ME'
show(object)

Class IQLearnFS_VHet

Description

Class IQLearnFS_VHet contains the results for the first stage residuals component of the interactive Q-Learning algorithm. Objects of this class are returned by iqLearnFSV().

Slots

residuals

: Standardized residuals of contrast after modeling

scale

: Scaling factor for stdization

step

: Not used in this context.

outcome

: The outcome regression analysis

txInfo

: The feasible tx information

optimal

: The estimated optimal tx, decision function, and value

Methods For Post-Processing of Regression Analysis

outcome

: Retrieve value object returned by outcome regression methods.

coef

: Retrieve parameter estimates for all regression steps.

fitObject

: Retrieve value object returned by regression methods.

plot

: Generate plots for regression analyses.

Methods For Accessing Main Results

DTRstep

: Retrieve description of method used to create object.

estimator

: Retrieve the estimated value of the estimated optimal regime for the training data set.

optTx

: Retrieve/predict the estimated decision functions and/or optimal tx.

print

: Print main results of analysis.

show

: Show main results of analysis.

summary

: Retrieve summary information.

residuals

:Retrieve the residuals of the regression.

qqplot

QQ plot of the residuals for the interactive Q-Learning algorithm.

Methods Available for Objects of Class IQLearnFS_VHet

Description

Methods Available for Objects of Class IQLearnFS_VHet

Usage

## S4 method for signature 'IQLearnFS_VHet'
print(x, ...)

## S4 method for signature 'IQLearnFS_VHet'
qqplot(
  x,
  y,
  plot.it = TRUE,
  xlab = deparse1(substitute(x)),
  ylab = deparse1(substitute(y)),
  ...,
  conf.level = NULL,
  conf.args = list(exact = NULL, simulate.p.value = FALSE, B = 2000, col = NA, border =
    NULL)
)

## S4 method for signature 'IQLearnFS_VHet'
show(object)

Functions

• qqplot(IQLearnFS_VHet):

Class IQLearnSS

Description

Class IQLearnSS contains the results for the second stage of the interactive Q-Learning algorithm. Objects of this class are returned by iqLearnSS().

Slots

yContHat

: A numeric. Estimated contrast component

yMainHat

: A numeric. Estimated main effects component

delta

: A numeric. Indicator of compliance * response used for value calc

step

: Not used in this context.

outcome

: The outcome regression analysis

txInfo

: The feasible tx information

optimal

: The estimated optimal tx, decision function, and value

Methods For Post-Processing of Regression Analysis

outcome

: Retrieve value object returned by outcome regression methods.

coef

: Retrieve parameter estimates for all regression steps.

fitObject

: Retrieve value object returned by regression methods.

plot

: Generate plots for regression analyses.

fittCont

:Retrieve the contrasts component of the regression.

fittMain

:Retrieve the main effects component of the regression.

Methods For Accessing Main Results

DTRstep

: Retrieve description of method used to create object.

estimator

: Retrieve the estimated value of the estimated optimal regime for the training data set.

optTx

: Retrieve/predict the estimated decision functions and/or optimal tx.

print

: Print main results of analysis.

show

: Show main results of analysis.

summary

: Retrieve summary information.

Methods Available for Objects of Class IQLearnSS

Description

Methods Available for Objects of Class IQLearnSS

Usage

## S4 method for signature 'IQLearnSS'
print(x, ...)

## S4 method for signature 'IQLearnSS'
show(object)

Class Kernel

Description

Class Kernel holds information regarding the decision function kernel

Slots

model

An formula. Defines the covariates of the kernel.

X

A matrix. The covariates of the kernel

kparam

ANY. The kernel parameter

Methods Available for Objects of Class Kernel

Description

Methods Available for Objects of Class Kernel

.getKernelX retrieves the covariates matrix of the kernel.

.kernelNumPars retrieves the number of covariates of the kernel.

.kernel calculates the kernel

print prints kernel model.

show displays kernel model.

summary returns a list containing the kernel model.

Usage

## S4 method for signature 'data.frame,Kernel'
.getKernelX(data, object)

## S4 method for signature 'Kernel'
.kernelNumPars(object, ...)

## S4 method for signature 'Kernel,missing,missing'
.kernel(object, x1, x2, ...)

## S4 method for signature 'Kernel,data.frame,data.frame'
.kernel(object, x1, x2, ...)

## S4 method for signature 'Kernel,vector,vector'
.kernel(object, x1, x2, ...)

## S4 method for signature 'Kernel,vector,data.frame'
.kernel(object, x1, x2, ...)

## S4 method for signature 'Kernel,data.frame,vector'
.kernel(object, x1, x2, ...)

## S4 method for signature 'Kernel,matrix,data.frame'
.kernel(object, x1, x2, ...)

## S4 method for signature 'Kernel,data.frame,matrix'
.kernel(object, x1, x2, ...)

## S4 method for signature 'Kernel,vector,matrix'
.kernel(object, x1, x2, ...)

## S4 method for signature 'Kernel,matrix,vector'
.kernel(object, x1, x2, ...)

## S4 method for signature 'Kernel,matrix,matrix'
.kernel(object, x1, x2, ...)

## S4 method for signature 'Kernel'
print(x, ...)

## S4 method for signature 'Kernel'
show(object)

## S4 method for signature 'Kernel'
summary(object, ...)

Class KernelObj

Description

Class KernelObj holds decision function kernel information under a common name.

Usage

## S4 method for signature 'KernelObj'
.kernelNumPars(object, ...)

Slots

kernel

ANY expected to be Kernel or SubsetList

Methods Available for Objects of Class KernelObj

Description

Methods Available for Objects of Class KernelObj

.getKernelX not allowed.

.kernel not allowed.

print prints kernel model. Includes "Kernel" as header.

show displays kernel model. Includes "Kernel" as header.

summary not allowed.

Usage

## S4 method for signature 'data.frame,KernelObj'
.getKernelX(data, object)

## S4 method for signature 'KernelObj,ANY,ANY'
.kernel(object, x1, x2, ...)

## S4 method for signature 'KernelObj,missing,missing'
.kernel(object, x1, x2, ...)

## S4 method for signature 'KernelObj,missing,ANY'
.kernel(object, x1, x2, ...)

## S4 method for signature 'KernelObj,ANY,missing'
.kernel(object, x1, x2, ...)

## S4 method for signature 'KernelObj'
print(x, ...)

## S4 method for signature 'KernelObj'
show(object)

## S4 method for signature 'KernelObj'
summary(object, ...)

Class Learning

Description

Class Learning contains results for a learning analysis with one regime.

Slots

txInfo

Feasible tx information

propen

Propensity regression analysis

outcome

Outcome regression analysis

optim

Optimization analysis

Methods Available for Objects of Class Learning

Description

Methods Available for Objects of Class Learning

Usage

## S4 method for signature 'Learning'
Call(name, ...)

## S4 method for signature 'Learning'
cvInfo(object, ...)

## S4 method for signature 'Learning'
coef(object, ...)

## S4 method for signature 'Learning'
estimator(x, ...)

## S4 method for signature 'Learning'
fitObject(object, ...)

## S4 method for signature 'Learning'
optimObj(object, ...)

## S4 method for signature 'Learning,data.frame'
optTx(x, newdata)

## S4 method for signature 'Learning,missing'
optTx(x, newdata, ...)

## S4 method for signature 'Learning'
outcome(object, ...)

## S4 method for signature 'Learning,ANY'
plot(x, suppress = FALSE, ...)

## S4 method for signature 'Learning'
print(x, ...)

## S4 method for signature 'Learning'
propen(object, ...)

## S4 method for signature 'Learning'
regimeCoef(object, ...)

## S4 method for signature 'Learning'
show(object)

## S4 method for signature 'Learning'
summary(object, ...)

Class LearningMulti

Description

Class LearningMulti contains results for a learning analysis with multiple regimes.

Slots

optimResult

ANY containing a list of OptimStep results

Methods Available for Objects of Class LearningMulti

Description

Methods Available for Objects of Class LearningMulti

Usage

## S4 method for signature 'LearningMulti'
Call(name, ...)

## S4 method for signature 'LearningMulti'
cvInfo(object, ...)

## S4 method for signature 'LearningMulti'
coef(object, ...)

## S4 method for signature 'LearningMulti'
estimator(x, ...)

## S4 method for signature 'LearningMulti'
fitObject(object, ...)

## S4 method for signature 'LearningMulti'
optimObj(object, ...)

## S4 method for signature 'LearningMulti,data.frame'
optTx(x, newdata, ...)

## S4 method for signature 'LearningMulti,missing'
optTx(x, newdata, ...)

## S4 method for signature 'LearningMulti'
outcome(object, ...)

## S4 method for signature 'LearningMulti,ANY'
plot(x, suppress = FALSE, ...)

## S4 method for signature 'LearningMulti'
print(x, ...)

## S4 method for signature 'LearningMulti'
propen(object, ...)

## S4 method for signature 'LearningMulti'
regimeCoef(object, ...)

## S4 method for signature 'LearningMulti'
show(object)

## S4 method for signature 'LearningMulti'
summary(object, ...)

Class LearningObject

Description

Class LearningObject contains stores parameters required for weighted learning optimization step

Slots

mu

Matrix of outcome regression

txVec

Vector of treatment coded as -1/1

invPi

Vector of inverse propensity for treatment received

response

Vector of the response

Methods Available for Objects of Class LearningObject

Description

Methods Available for Objects of Class LearningObject

Create LearningObject

Usage

## S4 method for signature 'LearningObject'
.subsetObject(methodObject, subset)

## S4 method for signature 'numeric,LearningObject,Kernel'
.objFn(par, methodObject, kernel, lambda, ...)

## S4 method for signature 'numeric,LearningObject,Kernel'
.dobjFn(par, methodObject, kernel, lambda, ...)

## S4 method for signature 'LearningObject'
.valueFunc(methodObject, optTx, ...)

.createLearningObject(
  kernel,
  surrogate,
  mu,
  txVec,
  response,
  prWgt,
  guess = NULL,
  ...
)

Arguments

Value

A LearningObject object

Class LinearKernel

Description

Class LinearKernel holds information regarding decision function when kernel is linear

Methods Available for Objects of Class LinearKernel

Description

Methods Available for Objects of Class LinearKernel

Usage

## S4 method for signature 'LinearKernel'
.kernelNumPars(object, ...)

## S4 method for signature 'LinearKernel,matrix,matrix'
.kernel(object, x1, x2, ...)

## S4 method for signature 'LinearKernel'
print(x, ...)

## S4 method for signature 'LinearKernel'
show(object)

## S4 method for signature 'LinearKernel'
summary(object, ...)

Class List

Description

Class List mimics a list.

Slots

names

Character vector of names for elements

Class LogitSurrogate

Description

Logistic surrogate for 0/1 loss function.

Methods Available for Objects of Class LogitSurrogate

Description

Methods Available for Objects of Class LogitSurrogate

.phiFunc calculates logistic surrogate loss-function

.dphiFunc calculates derivative of logistic surrogate loss-function

Usage

## S4 method for signature 'LogitSurrogate'
.phiFunc(surrogate, u)

## S4 method for signature 'LogitSurrogate'
.dPhiFunc(surrogate, u, du)

Class MethodObject

Description

Class MethodObject stores parameters required for optimization step

Slots

x

Matrix of covariates for kernel

surrogate

The Surrogate for the loss-function

pars

Vector of regime parameters

kernel

The Kernel defining the decision function

Methods Available for Objects of Class MethodObject

Description

Methods Available for Objects of Class MethodObject

Create method object

Usage

## S4 method for signature 'MethodObject'
.subsetObject(methodObject, subset)

## S4 method for signature 'numeric,MethodObject,Kernel'
.objFn(par, methodObject, kernel, lambda, ...)

## S4 method for signature 'numeric,MethodObject,Kernel'
.dobjFn(par, methodObject, kernel, lambda, ...)

## S4 method for signature 'MethodObject'
.valueFunc(methodObject, optTx, ...)

.createMethodObject(kernel, surrogate, guess = NULL, ...)

Arguments

Value

A MethodObject object

Class ModelObjSubset

Description

Class ModelObjSubset stores a modelObj object with pertinent subset information

Slots

decisionPoint

integer indicating the decision point for modelObj

subset

character indicating the subset for modelObj

import modelObj

Methods Available for Objects of Class ModelObjSubset

Description

Methods Available for Objects of Class ModelObjSubset

.getDecisionPoint(object) retrieves the decision point to which object pertains

.getSubset(object) retrieves the subset to which object pertains

Usage

## S4 method for signature 'ModelObjSubset'
.getDecisionPoint(object)

## S4 method for signature 'ModelObjSubset'
.getSubset(object)

Class ModelObj_DecisionPointList

Description

Class ModelObj_DecisionPointList represents a List for multiple decision points. Contents can be other modelObj or ModeObj_SubsetList.

Usage

## S4 method for signature 'ModelObj_DecisionPointList'
initialize(.Object, ...)

Class ModelObj_SubsetList

Description

Class ModelObj_SubsetList represents a List for subset modelObj.

Usage

## S4 method for signature 'ModelObj_SubsetList'
initialize(.Object, ...)

Class MultiRadialKernel

Description

Class MultiRadialKernel holds information regarding decision function when kernel is radial and multiple kernel parameters

Methods Available for Objects of Class MultiRadialKernel

Description

Methods Available for Objects of Class MultiRadialKernel

.kernel not allowed.

print not allowed.

show not allowed.

summary not allowed.

Usage

## S4 method for signature 'MultiRadialKernel,matrix,matrix'
.kernel(object, x1, x2, ...)

## S4 method for signature 'MultiRadialKernel'
print(x, ...)

## S4 method for signature 'MultiRadialKernel'
show(object)

## S4 method for signature 'MultiRadialKernel'
summary(object, ...)

Class OWL

Description

Class OWL contains results for an OWL analysis.

Slots

analysis

Contains a Learning or LearningMulti object.

analysis@txInfo

Feasible tx information.

analysis@propen

Propensity regression analysis.

analysis@outcome

Outcome regression analysis.

analysis@cvInfo

Cross-validation analysis if single regime.

analysis@optim

Optimization analysis if single regime.

analysis@optimResult

list of cross-validation and optimization results if multiple regimes. optimResult[[i]]@cvInfo and optimResult[[i]]@optim.

analysis@optimal

Estimated optimal Tx and value.

analysis@call

Unevaluated call to statistical method.

Methods For Post-Processing of Regression Analysis

propen

: Retrieve value object returned by propensity regression methods.

coef

: Retrieve parameter estimates for all regression steps.

fitObject

: Retrieve value object returned by regression methods.

plot

: Generate plots for regression analyses.

Methods For Post-Processing of Optimization Analysis

cvInfo

: Retrieve cross-validation results.

optimObj

: Retrieve value object returned by optimization method(s).

regimeCoef

: Retrieve estimated parameters for optimal tx regime.

Methods For Accessing Main Results

DTRstep

: Retrieve description of method used to create object.

estimator

: Retrieve the estimated value of the estimated optimal regime for the training data set.

optTx

: Retrieve/predict the estimated decision functions and/or optimal tx.

print

: Print main results of analysis.

show

: Show main results of analysis.

summary

: Retrieve summary information.

Methods Available for Objects of Class OWL

Description

Methods Available for Objects of Class OWL

Usage

## S4 method for signature 'OWL'
print(x, ...)

## S4 method for signature 'OWL'
show(object)

Class OptimBasic

Description

Class OptimBasic holds results of an optimization step when linear kernel is used for decision function.

Slots

lambda

A numeric, tuning parameter

optim

A list, value object returned by optimization method expected optimization methods are optim and hjk

surrogate

A Surrogate object specifying loss-function surrogate

Methods Available for Objects of Class OptimBasic

Description

Methods Available for Objects of Class OptimBasic

Usage

## S4 method for signature 'OptimBasic'
optimObj(object, ...)

## S4 method for signature 'OptimBasic,matrix'
.predictOptimalTx(x, newdata)

## S4 method for signature 'OptimBasic,data.frame'
.predictOptimalTx(x, newdata, ...)

## S4 method for signature 'OptimBasic,missing'
.predictOptimalTx(x, newdata, ...)

## S4 method for signature 'OptimBasic'
print(x, ...)

## S4 method for signature 'OptimBasic'
regimeCoef(object, ...)

## S4 method for signature 'OptimBasic'
show(object)

## S4 method for signature 'OptimBasic'
summary(object, ...)

Class OptimKernel

Description

Class OptimKernel holds results of an optimization step when non-linear kernel is used for decision function.

Methods Available for Objects of Class OptimKernel

Description

Methods Available for Objects of Class OptimKernel

Usage

## S4 method for signature 'OptimKernel,matrix'
.predictOptimalTx(x, newdata)

## S4 method for signature 'OptimKernel,data.frame'
.predictOptimalTx(x, newdata, ...)

## S4 method for signature 'OptimKernel,missing'
.predictOptimalTx(x, newdata, ...)

Class OptimObj

Description

Class OptimObj stores the optimization results under a common name for weighted learning methods.

Slots

optim

ANY - expected to be OptimBasic or OptimKernel

Methods Available for Objects of Class OptimObj

Description

Methods Available for Objects of Class OptimObj

Usage

## S4 method for signature 'OptimObj'
optimObj(object, ...)

## S4 method for signature 'OptimObj,matrix'
.predictOptimalTx(x, newdata, ...)

## S4 method for signature 'OptimObj,data.frame'
.predictOptimalTx(x, newdata, ...)

## S4 method for signature 'OptimObj,missing'
.predictOptimalTx(x, newdata, ...)

## S4 method for signature 'OptimObj'
print(x, ...)

## S4 method for signature 'OptimObj'
regimeCoef(object, ...)

## S4 method for signature 'OptimObj'
show(object)

## S4 method for signature 'OptimObj'
summary(object, ...)

Complete Cross-Validation Step and Final Optimization

Description

Complete Cross-Validation Step and Final Optimization

Usage

.OptimStep(methodObject, lambdas, cvFolds, txVec, suppress, ...)

Arguments

Class OptimStep Class OptimStep holds results of a combined cross-validation and final optimization step for weighted learning methods.

Description

Class OptimStep Class OptimStep holds results of a combined cross-validation and final optimization step for weighted learning methods.

Methods Available for Objects of Class OptimStep

Description

Methods Available for Objects of Class OptimStep

Usage

## S4 method for signature 'OptimStep'
Call(name, ...)

## S4 method for signature 'OptimStep'
cvInfo(object)

## S4 method for signature 'OptimStep'
estimator(x, ...)

## S4 method for signature 'OptimStep'
optimObj(object)

## S4 method for signature 'OptimStep,missing'
optTx(x, newdata, ...)

## S4 method for signature 'OptimStep,matrix'
.predictOptimalTx(x, newdata, ...)

## S4 method for signature 'OptimStep,data.frame'
.predictOptimalTx(x, newdata, ...)

## S4 method for signature 'OptimStep,missing'
.predictOptimalTx(x, newdata, ...)

## S4 method for signature 'OptimStep'
print(x, ...)

## S4 method for signature 'OptimStep'
regimeCoef(object)

## S4 method for signature 'OptimStep'
show(object)

## S4 method for signature 'OptimStep'
summary(object, ...)

Class OptimalClass

Description

Class OptimalClass contains results for a single decision point when estimates are obtained from the classification perspective. Objects of this class are returned by optimalClass().

Slots

step

Step in the algorithm.

analysis

Analysis results.

Methods For Post-Processing of Regression Analysis

outcome

: Retrieve value object returned by outcome regression methods.

propen

: Retrieve value object returned by propensity regression methods.

classif

: Retrieve value object returned by classification regression methods.

coef

: Retrieve parameter estimates for all regression steps.

fitObject

: Retrieve value object returned by regression methods.

plot

: Generate plots for regression analyses.

Methods For Accessing Main Results

DTRstep

: Retrieve description of method used to create object.

estimator

: Retrieve the estimated value of the estimated optimal regime for the training data set.

optTx

: Retrieve/predict the estimated decision functions and/or optimal tx.

print

: Print main results of analysis.

show

: Show main results of analysis.

summary

: Retrieve summary information.

Methods Available for Objects of Class OptimalClass

Description

Methods Available for Objects of Class OptimalClass

Usage

## S4 method for signature 'OptimalClass'
print(x, ...)

## S4 method for signature 'OptimalClass'
show(object)

Class OptimalClassObj

Description

Class OptimalClassObj contains results for a single decision point when estimates are obtained from the classification perspective. Objects of this class are returned by optimalClass().

Slots

class

Results of the classification step.

outcome

Results of the outcome regression step.

propen

Results of the propensity step.

optimal

Estimated optimal tx and value

Call

Unevaluated call.

Methods For Post-Processing of Regression Analysis

outcome

: Retrieve value object returned by outcome regression methods.

propen

: Retrieve value object returned by propensity regression methods.

classif

: Retrieve value object returned by classification regression methods.

coef

: Retrieve parameter estimates for all regression steps.

fitObject

: Retrieve value object returned by regression methods.

plot

: Generate plots for regression analyses.

Methods For Accessing Main Results

DTRstep

: Retrieve description of method used to create object.

estimator

: Retrieve the estimated value of the estimated optimal regime for the training data set.

optTx

: Retrieve/predict the estimated decision functions and/or optimal tx.

print

: Print main results of analysis.

show

: Show main results of analysis.

summary

: Retrieve summary information.

Class OptimalInfo

Description

Class OptimalInfo stores the estimated optimal tx, decision functions, and estimated value.

Slots

optimalTx

a vector of the estimated optimal tx

estimatedValue

a vector of the estimated value

decisionFunc

a vector or matrix containing the values used to determine @optimalTx (if applicable)

Methods Available for Objects of Class OptimalInfo

Description

Methods Available for Objects of Class OptimalInfo

estimator(x) defines the estimated value to be the mean of the vector stored in @estimatedValue

optTx(x) returns the contents of @optimalTx and @decisionFunc as a list

optTx(x, newdata) returns an error

print(x) Prints a summary table of the recommended tx for the training data and the estimated value

show(object) Displays a summary table of the recommended tx for the training data and the estimated value

summary(object) Returns a list containing a summary table of the recommended tx for the training data and the estimated value

Usage

## S4 method for signature 'OptimalInfo'
estimator(x)

## S4 method for signature 'OptimalInfo,missing'
optTx(x, newdata, ...)

## S4 method for signature 'OptimalInfo,ANY'
optTx(x, newdata, ...)

## S4 method for signature 'OptimalInfo'
print(x, ...)

## S4 method for signature 'OptimalInfo'
show(object)

## S4 method for signature 'OptimalInfo'
summary(object, ...)

Class OptimalObj

Description

Class OptimalObj stores the estimated optimal Tx, decision functions and estimated value under a common name.

Slots

optimal

ANY - must be OptimalInfo or DecisionPointList of OptimalInfo

Methods Available for Objects of Class OptimalObj

Description

Methods Available for Objects of Class OptimalObj

estimator(x) retrieves the estimated value obtained by a statistical method. Method called determined by class of @optimal.

optTx(x) returns the estimated decision function and/or optimal tx Method called determined by class of @optimal.

optTx(x, newdata) returns an error

print(x) Prints summary information regarding recommended tx and the estimated value. Method called determined by class of @optimal.

show(object) Displays summary information regarding recommended tx and the estimated value. Method called determined by class of @optimal.

summary(object) Returns a summary of estimated decision functions and/or optimal tx. Method called determined by class of @optimal.

Usage

## S4 method for signature 'OptimalObj'
estimator(x)

## S4 method for signature 'OptimalObj,missing'
optTx(x, newdata, ...)

## S4 method for signature 'OptimalObj,data.frame'
optTx(x, newdata, ...)

## S4 method for signature 'OptimalObj'
print(x, ...)

## S4 method for signature 'OptimalObj'
show(object)

## S4 method for signature 'OptimalObj'
summary(object, ...)

Class OptimalSeq

Description

Class OptimalSeq contains the results for the estimated optimal tx and value when estimated from a coarsened or missing data perspective.

Slots

genetic

A list containing the results from the genetic algorithm

propen

Results of the propensity regression step

outcome

Results of the outcome regression step

regime

Results for the regime.

optimal

Results for the estimated optimal tx and value

Call

The unevaluated call.

Methods For Post-Processing of Regression Analysis

outcome

: Retrieve value object returned by outcome regression methods.

propen

: Retrieve value object returned by propensity regression methods.

coef

: Retrieve parameter estimates for all regression steps.

fitObject

: Retrieve value object returned by regression methods.

plot

: Generate plots for regression analyses.

Methods For Accessing Main Results

regimeCoef

: Retrieve the estimated regime parameters.

DTRstep

: Retrieve description of method used to create object.

estimator

: Retrieve the estimated value of the estimated optimal regime for the training data set.

optTx

: Retrieve/predict the estimated decision functions and/or optimal tx.

print

: Print main results of analysis.

show

: Show main results of analysis.

summary

: Retrieve summary information.

Methods Available for Objects of Class OptimalSeq

Description

Methods Available for Objects of Class OptimalSeq

print(x) prints main results of a coarsened/missing data analysis

show(object) displays main results of a coarsened/missing data analysis

Usage

## S4 method for signature 'OptimalSeq'
print(x, ...)

## S4 method for signature 'OptimalSeq'
show(object)

Class Contains Results for the Coarsened Data IPW/AIPW Method

Description

Methods for multiple decision point analyses. Class inherits directly from OptimalSeq and all methods defined for objects of class OptimaSeq are defined for this class.

Methods Available for Objects of Class OptimalSeqCoarsened

Description

Methods Available for Objects of Class OptimalSeqCoarsened

Call(name) returns the unevaluated call to method

coef(object) retrieves coefficients of model functions. Calls method defined for OptimalSeq.

DTRstep(x) print statement indicating the coarsened data perspective

estimator(x) retrieves the estimated value. Calls method defined for OptimalSeq.

fitObject(object) retrieves value objects of model functions. Calls method defined for OptimalSeq.

genetic(object) retrieves genetic algorithm results. Calls method defined for OptimalSeq.

optTx(x,newdata) estimates optimal tx. Calls method defined for OptimalSeq.

optTx(x) retrieves the optimal tx. Calls method defined for OptimalSeq.

outcome(object) retrieves value object returned by outcome model functions. Calls method defined for OptimalSeq.

plot(x,suppress) generates plot for model functions. Calls method defined for OptimalSeq.

print(x) Extends method defined for OptimalSeq to include DTRStep()

propen(object) retrieves value object returned by propensity model functions. Calls method defined for OptimalSeq.

regimeCoef(object) retrieves estimated tx regime parameters. Calls method defined for OptimalSeq.

show(object) Extends method defined for OptimalSeq to include DTRStep()

summary(object) retrieves summary information. Calls method defined for OptimalSeq.

Usage

## S4 method for signature 'OptimalSeqCoarsened'
Call(name, ...)

## S4 method for signature 'OptimalSeqCoarsened'
coef(object, ...)

## S4 method for signature 'OptimalSeqCoarsened'
DTRstep(object)

## S4 method for signature 'OptimalSeqCoarsened'
estimator(x, ...)

## S4 method for signature 'OptimalSeqCoarsened'
fitObject(object, ...)

## S4 method for signature 'OptimalSeqCoarsened'
genetic(object, ...)

## S4 method for signature 'OptimalSeqCoarsened,data.frame'
optTx(x, newdata, ..., dp = 1)

## S4 method for signature 'OptimalSeqCoarsened,missing'
optTx(x, newdata, ...)

## S4 method for signature 'OptimalSeqCoarsened'
outcome(object, ...)

## S4 method for signature 'OptimalSeqCoarsened,ANY'
plot(x, suppress = FALSE, ...)

## S4 method for signature 'OptimalSeqCoarsened'
print(x, ...)

## S4 method for signature 'OptimalSeqCoarsened'
propen(object, ...)

## S4 method for signature 'OptimalSeqCoarsened'
regimeCoef(object, ...)

## S4 method for signature 'OptimalSeqCoarsened'
show(object)

## S4 method for signature 'OptimalSeqCoarsened'
summary(object, ...)

Class Contains Results for the Missing Data IPW/AIPW Method

Description

Methods for single decision point analyses. Class inherits directly from OptimalSeq and all methods defined for objects of class OptimaSeq are defined for this class.

Methods Available for Objects of Class OptimalSeqMissing

Description

Methods Available for Objects of Class OptimalSeqMissing

Call(name) returns the unevaluated call to method

Retrieve coefficients of fits

DTRstep(x) print statement indicating the coarsened data perspective

estimator(x) retrieves the estimated value. Calls method defined for OptimalSeq.

fitObject(object) retrieves value objects of model functions. Calls method defined for OptimalSeq.

genetic(object) retrieves genetic algorithm results. Calls method defined for OptimalSeq.

Predict Optimal Treatment and Decision Function Based on a Missing Data AIPW Analysis

optTx(x) retrieves the optimal tx. Calls method defined for OptimalSeq.

outcome(object) retrieves value object returned by outcome model functions. Calls method defined for OptimalSeq.

plot(x,suppress) generates plot for model functions. Calls method defined for OptimalSeq.

print(x) Extends method defined for OptimalSeq to include DTRStep()

propen(object) retrieves value object returned by propensity model functions. Calls method defined for OptimalSeq.

regimeCoef(object) retrieves estimated tx regime parameters. Calls method defined for OptimalSeq.

show(object) Extends method defined for OptimalSeq to include DTRStep()

summary(object) retrieves summary information. Calls method defined for OptimalSeq.

Usage

## S4 method for signature 'OptimalSeqMissing'
Call(name, ...)

## S4 method for signature 'OptimalSeqMissing'
coef(object, ...)

## S4 method for signature 'OptimalSeqMissing'
DTRstep(object)

## S4 method for signature 'OptimalSeqMissing'
estimator(x, ...)

## S4 method for signature 'OptimalSeqMissing'
fitObject(object, ...)

## S4 method for signature 'OptimalSeqMissing'
genetic(object, ...)

## S4 method for signature 'OptimalSeqMissing,data.frame'
optTx(x, newdata, ...)

## S4 method for signature 'OptimalSeqMissing,missing'
optTx(x, newdata, ...)

## S4 method for signature 'OptimalSeqMissing'
outcome(object, ...)

## S4 method for signature 'OptimalSeqMissing,ANY'
plot(x, suppress = FALSE, ...)

## S4 method for signature 'OptimalSeqMissing'
print(x, ...)

## S4 method for signature 'OptimalSeqMissing'
propen(object, ...)

## S4 method for signature 'OptimalSeqMissing'
regimeCoef(object, ...)

## S4 method for signature 'OptimalSeqMissing'
show(object)

## S4 method for signature 'OptimalSeqMissing'
summary(object, ...)

Class OutcomeIterateFit

Description

Class OutcomeIterateFit is a an outcome regression step completed using the iterative algorithm.

Slots

fitObjC

Contrast Result

fitObjM

Main Effects Result

Methods Available for Objects of Class OutcomeIterateFit

Description

Methods call equivalently named methods defined for OutcomeSimpleFit, OutcomeSimpleFit_fSet, or OutcomeSimpleFit_SubsetList. Exact method dispatched depends on classes of @fitObjC and @fitObjM. When a value object is returned, it is a list.

.predictAll(object, newdata) combines the two components into a single optimal tx and decision function

Usage

## S4 method for signature 'OutcomeIterateFit'
coef(object, ...)

## S4 method for signature 'OutcomeIterateFit'
fitObject(object, ...)

## S4 method for signature 'OutcomeIterateFit'
outcome(object, ...)

## S4 method for signature 'OutcomeIterateFit,ANY'
plot(x, suppress = FALSE, ...)

## S4 method for signature 'OutcomeIterateFit'
predict(object, ...)

## S4 method for signature 'OutcomeIterateFit,data.frame'
.predictAll(object, newdata, ...)

## S4 method for signature 'OutcomeIterateFit'
print(x, ...)

## S4 method for signature 'OutcomeIterateFit'
show(object)

## S4 method for signature 'OutcomeIterateFit'
summary(object, ...)

Class OutcomeNoFit

Description

Class OutcomeNoFit designates that an outcome regression step was not performed. This acts as a place holder for IPW based methods.

Methods Available for Objects of Class OutcomeNoFit

Description

Methods return NULL, NA or zero values.

.predictAll(object, newdata) returns a list containing the optimal tx as a vector of NA values and the decision function as a matrix of 0

.predictMu(object, newdata) predicts outcome for all tx options. Returns the matrix of outcomes predicted for all tx. Predicted outcomes for tx not available to a pt are NA.

Usage

## S4 method for signature 'OutcomeNoFit,data.frame'
.predictAll(object, newdata)

## S4 method for signature 'OutcomeNoFit,data.frame'
.predictMu(object, data, ...)

## S4 method for signature 'OutcomeNoFit'
outcome(object, ...)

## S4 method for signature 'OutcomeNoFit'
coef(object, ...)

## S4 method for signature 'OutcomeNoFit'
fitObject(object, ...)

## S4 method for signature 'OutcomeNoFit,ANY'
plot(x, suppress = FALSE, ...)

## S4 method for signature 'OutcomeNoFit'
predict(object, ...)

## S4 method for signature 'OutcomeNoFit'
print(x, ...)

## S4 method for signature 'OutcomeNoFit'
show(object)

## S4 method for signature 'OutcomeNoFit'
summary(object, ...)

Class OutcomeObj

Description

Class OutcomeObj groups outcome regression results under a common name

Slots

outcome

ANY - expected to be OutcomeNoFit, OutcomeSimpleFit, OutcomeSimpleFit_fSet, OutcomeSimpleFit_SubsetList, OutcomeIterateFit, or DecisionPointList.

Methods Available for Objects of Class OutcomeObj

Description

Most value objects returned are a list with one element 'outcome'. Methods dispatched and objects returned in the element 'outcome' depend on class of @outcome. Exceptions are noted below.

outcome(object) does not return the overarching list structure, but only the contents of list[[ outcome ]].

plot(x) concatenated 'outcome' to the title if suppress = FALSE.

.predictAll(object, newdata) does not return the overarching list structure, but only the contents of list[[ outcome ]].

.predictMu(object, newdata) predicts outcome for all tx options. Returns the matrix of outcomes predicted for all tx. Predicted outcomes for tx not available to a pt are NA.

predict(object) does not return the overarching list structure, but only the contents of list[[ outcome ]].

Usage

## S4 method for signature 'OutcomeObj'
coef(object, ...)

## S4 method for signature 'OutcomeObj'
fitObject(object, ...)

## S4 method for signature 'OutcomeObj'
outcome(object, ...)

## S4 method for signature 'OutcomeObj,ANY'
plot(x, suppress = FALSE, ...)

## S4 method for signature 'OutcomeObj,data.frame'
.predictAll(object, newdata, ...)

## S4 method for signature 'OutcomeObj,data.frame'
.predictMu(object, data, ...)

## S4 method for signature 'OutcomeObj'
predict(object, ...)

## S4 method for signature 'OutcomeObj'
print(x, ...)

## S4 method for signature 'OutcomeObj'
show(object)

## S4 method for signature 'OutcomeObj'
summary(object, ...)

Class OutcomeSimpleFit

Description

Class OutcomeSimpleFit is a TypedFit identified as being for an outcome regression step.

Methods Available for Objects of Class OutcomeSimpleFit

Description

Methods call equivalently named methods defined for TypedFit

.predictAll(object, newdata) predicts outcome for all tx options. Returns a list containing 'optimalTx' the tx yielding the largest predicted outcome and 'decisionFunc' the matrix of outcomes predicted for all tx.

.predictMu(object, newdata) predicts outcome for all tx options. Returns the matrix of outcomes predicted for all tx. Predicted outcomes for tx not available to a pt are NA.

Usage

## S4 method for signature 'OutcomeSimpleFit'
coef(object, ...)

## S4 method for signature 'OutcomeSimpleFit'
fitObject(object, ...)

## S4 method for signature 'OutcomeSimpleFit'
outcome(object, ...)

## S4 method for signature 'OutcomeSimpleFit,ANY'
plot(x, suppress = FALSE, ...)

## S4 method for signature 'OutcomeSimpleFit'
predict(object, ...)

## S4 method for signature 'OutcomeSimpleFit,data.frame'
.predictAll(object, newdata)

## S4 method for signature 'OutcomeSimpleFit,data.frame'
.predictMu(object, data, ...)

## S4 method for signature 'OutcomeSimpleFit'
print(x, ...)

## S4 method for signature 'OutcomeSimpleFit'
show(object)

## S4 method for signature 'OutcomeSimpleFit'
summary(object, ...)

Class OutcomeSimpleFit_SubsetList

Description

Class OutcomeSimpleFit_SubsetList is a TypedFit_SubsetList identified as being for an outcome regression step.

Methods Available for Objects of Class OutcomeSimpleFit_SubsetList

Description

Methods call equivalently named methods defined for TypedFit_SubsetList

.predictAll(object, newdata) predicts outcome for all tx options. Returns a list containing 'optimalTx' the tx yielding the largest predicted outcome and 'decisionFunc' the matrix of outcomes predicted for all tx. Predicted outcomes for tx not available to a pt are NA.

.predictMu(object, data) predicts outcome for all tx options. Returns the matrix of outcomes predicted for all tx. Predicted outcomes for tx not available to a pt are NA.

Usage

## S4 method for signature 'OutcomeSimpleFit_SubsetList'
outcome(object, ...)

## S4 method for signature 'OutcomeSimpleFit_SubsetList'
predict(object, newdata, ...)

## S4 method for signature 'OutcomeSimpleFit_SubsetList,data.frame'
.predictAll(object, newdata, ...)

## S4 method for signature 'OutcomeSimpleFit_SubsetList,data.frame'
.predictMu(object, data, ...)

Class OutcomeSimpleFit_fSet

Description

Class OutcomeSimpleFit_fSet is a TypedFit_fSet identified as being for an outcome regression step.

Methods Available for Objects of Class OutcomeSimpleFit_fSet

Description

Methods call equivalently named methods defined for TypedFit_fSet

.predictAll(object, newdata) predicts outcome for all tx options. Returns a list containing 'optimalTx' the tx yielding the largest predicted outcome and 'decisionFunc' the matrix of outcomes predicted for all tx. Predicted outcomes for tx not available to a pt are NA.

.predictMu(object, newdata) predicts outcome for all tx options. Returns the matrix of outcomes predicted for all tx. Predicted outcomes for tx not available to a pt are NA.

Usage

## S4 method for signature 'OutcomeSimpleFit_fSet'
outcome(object, ...)

## S4 method for signature 'OutcomeSimpleFit_fSet,data.frame'
.predictAll(object, newdata, ...)

## S4 method for signature 'OutcomeSimpleFit_fSet,data.frame'
.predictMu(object, data, ...)

Class PolyKernel

Description

Class PolyKernel holds information regarding decision function when kernel is polynomial

Methods Available for Objects of Class PolyKernel

Description

Methods Available for Objects of Class PolyKernel

Usage

## S4 method for signature 'PolyKernel,matrix,matrix'
.kernel(object, x1, x2, ...)

## S4 method for signature 'PolyKernel'
print(x, ...)

## S4 method for signature 'PolyKernel'
show(object)

## S4 method for signature 'PolyKernel'
summary(object, ...)

Class PropensityFit

Description

Class PropensityFit is a TypedFit identified as being for a propensity regression step.

Slots

small

A logical TRUE indicates that the smallest valued tx is missing; FALSE indicates that the largest valued tx is missing

levs

A vector; the set of treatment options included in fit.

Methods Available for Objects of Class PropensityFit

Description

Methods call equivalently named methods defined for TypedFit

.predictAll(object, newdata) predicts propensity for all tx options. Returns a matrix of propensities predicted for all tx.

Usage

## S4 method for signature 'PropensityFit'
coef(object, ...)

## S4 method for signature 'PropensityFit'
fitObject(object, ...)

## S4 method for signature 'PropensityFit,ANY'
plot(x, suppress = FALSE, ...)

## S4 method for signature 'PropensityFit'
predict(object, ...)

## S4 method for signature 'PropensityFit,data.frame'
.predictAll(object, newdata, suppress = TRUE)

## S4 method for signature 'PropensityFit'
print(x, ...)

## S4 method for signature 'PropensityFit'
propen(object, ...)

## S4 method for signature 'PropensityFit'
show(object)

## S4 method for signature 'PropensityFit'
summary(object, ...)

Class PropensityFit_SubsetList

Description

Class PropensityFit_SubsetList is a TypedFit_SubsetList identified as being for a propensity regression step.

Slots

small

A logical vector TRUE indicates that the smallest valued tx is missing; FALSE indicates that the largest valued tx is missing

levs

A list; the set of treatment options included in each fit.

Methods Available for Objects of Class PropensityFit_SubsetList

Description

Most methods call equivalently named methods defined for TypedFit_SubsetList

Usage

## S4 method for signature 'PropensityFit_SubsetList'
coef(object, ...)

## S4 method for signature 'PropensityFit_SubsetList'
fitObject(object, ...)

## S4 method for signature 'PropensityFit_SubsetList,data.frame'
.predictAll(object, newdata, suppress = TRUE)

## S4 method for signature 'PropensityFit_SubsetList'
propen(object, ...)

## S4 method for signature 'PropensityFit_SubsetList'
summary(object, ...)

Class PropensityFit_fSet

Description

Class PropensityFit_fSet is a TypedFit_fSet identified as being for a propensity regression step.

Slots

small

A logical TRUE indicates that the smallest valued tx is missing; FALSE indicates that the largest valued tx is missing

levs

A vector; the set of treatment options included in fit.

Methods Available for Objects of Class PropensityFit_fSet

Description

Methods call equivalently named methods defined for TypedFit_fSet

.predictAll(object, newdata) predicts propensity for all tx options. Returns a matrix of propensities predicted for all tx. Tx options not available to a pt are coded as NA.

Usage

## S4 method for signature 'PropensityFit_fSet'
coef(object, ...)

## S4 method for signature 'PropensityFit_fSet'
fitObject(object, ...)

## S4 method for signature 'PropensityFit_fSet,data.frame'
.predictAll(object, newdata, suppress = TRUE)

## S4 method for signature 'PropensityFit_fSet'
propen(object, ...)

## S4 method for signature 'PropensityFit_fSet'
summary(object, ...)

Class PropensityObj

Description

Class PropensityObj groups Propensity regression results under a common name.

Slots

Propensity

ANY - expected to be PropensityFit, PropensityFit_fSet, PropensityFit_SubsetList, or DecisionPointList.

Methods Available for Objects of Class PropensityObj

Description

Most value objects returned are a list with one element 'propen'. Methods dispatched and objects returned in the element 'propen' depend on class of @propen. Exceptions are noted below.

plot(x) concatenates 'Propensity' to the title if suppress = FALSE.

.predictAll(object, newdata) does not return the overarching list structure, but only the contents of list[[ propen ]].

predict(object) does not return the overarching list structure, but only the contents of list[[ propen ]].

propen(object) does not return the overarching list structure, but only the contents of list[[ propen ]].

Usage

## S4 method for signature 'PropensityObj'
coef(object, ...)

## S4 method for signature 'PropensityObj'
fitObject(object, ...)

## S4 method for signature 'PropensityObj,ANY'
plot(x, suppress = FALSE, ...)

## S4 method for signature 'PropensityObj,data.frame'
.predictAll(object, newdata, ...)

## S4 method for signature 'PropensityObj'
predict(object, ...)

## S4 method for signature 'PropensityObj'
print(x, ...)

## S4 method for signature 'PropensityObj'
propen(object, ...)

## S4 method for signature 'PropensityObj'
show(object)

## S4 method for signature 'PropensityObj'
summary(object, ...)

Class QLearn

Description

Class QLearn contains the results for a Q-Learning step

Slots

step

An integer indicating the step of the Q-Learning algorithm.

outcome

The outcome regression analysis

txInfo

The feasible tx information

optimal

The estimated optimal tx, decision function, and value

Methods For Post-Processing of Regression Analysis

outcome

: Retrieve value object returned by outcome regression methods.

coef

: Retrieve parameter estimates for all regression steps.

fitObject

: Retrieve value object returned by regression methods.

plot

: Generate plots for regression analyses.

Methods For Accessing Main Results

DTRstep

: Retrieve description of method used to create object.

estimator

: Retrieve the estimated value of the estimated optimal regime for the training data set.

optTx

: Retrieve/predict the estimated decision functions and/or optimal tx.

print

: Print main results of analysis.

show

: Show main results of analysis.

summary

: Retrieve summary information.

Class QLearnObj

Description

Class QLearnObj contains the results for a Q-Learning step

Slots

outcome

The outcome regression analysis

txInfo

The feasible tx information

optimal

The estimated optimal tx, decision function, and value

Methods For Post-Processing of Regression Analysis

outcome

: Retrieve value object returned by outcome regression methods.

coef

: Retrieve parameter estimates for all regression steps.

fitObject

: Retrieve value object returned by regression methods.

plot

: Generate plots for regression analyses.

Methods For Accessing Main Results

DTRstep

: Retrieve description of method used to create object.

estimator

: Retrieve the estimated value of the estimated optimal regime for the training data set.

optTx

: Retrieve/predict the estimated decision functions and/or optimal tx.

print

: Print main results of analysis.

show

: Show main results of analysis.

summary

: Retrieve summary information.

Class RWL

Description

Class RWL contains results for an RWL analysis.

Slots

responseType

character indicating type of response

residuals

vector of outcome residuals

beta

vector of regime parameters

analysis

Contains a Learning or LearningMulti object

analysis@txInfo

Feasible tx information

analysis@propen

Propensity regression analysis

analysis@outcome

Outcome regression analysis

analysis@cvInfo

Cross-validation analysis if single regime

analysis@optim

Optimization analysis if single regime

analysis@optimResult

list of cross-validation and optimization results if multiple regimes. optimResult[[i]]@cvInfo and optimResult[[i]]@optim

analysis@optimal

Estimated optimal Tx and value

analysis@Call

Unevaluated Call

Methods For Post-Processing of Regression Analysis

outcome

: Retrieve value object returned by outcome regression methods.

propen

: Retrieve value object returned by propensity regression methods.

coef

: Retrieve parameter estimates for all regression steps.

fitObject

: Retrieve value object returned by regression methods.

plot

: Generate plots for regression analyses.

Methods For Post-Processing of Optimization Analysis

cvInfo

: Retrieve cross-validation results.

optimObj

: Retrieve value object returned by optimization method(s).

regimeCoef

: Retrieve estimated parameters for optimal tx regime.

Methods For Accessing Main Results

DTRstep

: Retrieve description of method used to create object.

estimator

: Retrieve the estimated value of the estimated optimal regime for the training data set.

optTx

: Retrieve/predict the estimated decision functions and/or optimal tx.

print

: Print main results of analysis.

show

: Show main results of analysis.

summary

: Retrieve summary information.

Methods Available for Objects of Class RWL

Description

Methods Available for Objects of Class RWL

Usage

## S4 method for signature 'RWL'
print(x, ...)

## S4 method for signature 'RWL'
show(object)

Class RadialKernel

Description

Class RadialKernel holds information regarding decision function when kernel is radial

Methods Available for Objects of Class RadialKernel

Description

Methods Available for Objects of Class RadialKernel

Usage

## S4 method for signature 'RadialKernel,matrix,matrix'
.kernel(object, x1, x2, ...)

## S4 method for signature 'RadialKernel'
print(x, ...)

## S4 method for signature 'RadialKernel'
show(object)

## S4 method for signature 'RadialKernel'
summary(object, ...)

Class Regime

Description

Class Regime holds information regarding regimes communicated through functions.

Slots

nVars

An integer. The number of parameters to be estimated

vNames

A character. The names of the parameters to be estimated

func

A function. The user specified function that defines the regime

pars

A numeric. The estimated parameters

Methods Available for Objects of Class Regime

Description

Methods Available for Objects of Class Regime

.getNumPars retrieves the number of parameters in the regime to be estimated.

.getParNames retrieves the parameter names in the regime.

.getPars retrieves current estimates for regime parameters.

.getRegimeFunction retrieves the user specified function definition of the regime.

.predictOptimalTx executes user specified function using current estimated parameters and provided data to determine recommended tx.

print prints the current estimates for the regime parameters.

regimeCoef retrieves the current estimates for the regime parameters

.setPars sets the parameter estimates to the provided values.

show displays the current estimates for the regime parameters.

summary retrieves the current estimates for the regime parameters

Usage

## S4 method for signature 'Regime'
.getNumPars(object)

## S4 method for signature 'Regime'
.getParNames(object)

## S4 method for signature 'Regime'
.getPars(object)

## S4 method for signature 'Regime'
.getRegimeFunction(object)

## S4 method for signature 'Regime,data.frame'
.predictOptimalTx(x, newdata, ...)

## S4 method for signature 'Regime'
print(x, ...)

## S4 method for signature 'Regime'
regimeCoef(object, ...)

## S4 method for signature 'Regime,numeric'
.setPars(object, pars)

## S4 method for signature 'Regime'
show(object)

## S4 method for signature 'Regime'
summary(object, ...)

Class RegimeObj

Description

Class RegimeObj holds information regarding regimes communicated through functions under a common name.

Slots

regime

ANY expected to be Regime or DecisionPointList

Methods Available for Objects of Class RegimeObj

Description

Methods dispatch equivalantly named functions defined for Regime or DecisionPointList objects. Method dispatched dictated by object stored in @regime.

Usage

## S4 method for signature 'RegimeObj'
.getNumPars(object)

## S4 method for signature 'RegimeObj'
.getParNames(object)

## S4 method for signature 'RegimeObj'
.getPars(object)

## S4 method for signature 'RegimeObj'
.getRegimeFunction(object)

## S4 method for signature 'RegimeObj,data.frame'
.predictOptimalTx(x, newdata, dp = 1L, ...)

## S4 method for signature 'RegimeObj'
print(x, ...)

## S4 method for signature 'RegimeObj'
regimeCoef(object, ...)

## S4 method for signature 'RegimeObj,numeric'
.setPars(object, pars)

## S4 method for signature 'RegimeObj'
show(object)

## S4 method for signature 'RegimeObj'
summary(object, ...)

Class SmoothRampSurrogate

Description

Components of smoothed ramp surrogate for 0/1 loss function.

Methods Available for Objects of Class SmoothRampSurrogate

Description

Methods Available for Objects of Class SmoothRampSurrogate

.phiFunc calculates smoothed ramp surrogate loss-function

.dphiFunc calculates derivative of smoothed ramp surrogate loss-function

Usage

## S4 method for signature 'SmoothRampSurrogate'
.phiFunc(surrogate, u, res)

## S4 method for signature 'SmoothRampSurrogate'
.dPhiFunc(surrogate, u, du, res)

Class SqHingeSurrogate

Description

Squared hinge surrogate for 0/1 loss function

Methods Available for Objects of Class SqHingeSurrogate

Description

Methods Available for Objects of Class SqHingeSurrogate

.phiFunc calculates squared hinge surrogate loss-function

.dphiFunc calculates derivative of squared hinge surrogate loss-function

Usage

## S4 method for signature 'SqHingeSurrogate'
.phiFunc(surrogate, u)

## S4 method for signature 'SqHingeSurrogate'
.dPhiFunc(surrogate, u, du)

Class SubsetList

Description

Class SubsetList represents a List for subset specifications. This class extends List to require non-zero length and named elements.

Methods Available for Objects of Class SubsetList

Description

Methods Available for Objects of Class SubsetList

Usage

## S4 method for signature 'SubsetList,ANY'
plot(x, suppress = FALSE, ...)

## S4 method for signature 'SubsetList'
print(x, ...)

## S4 method for signature 'SubsetList'
show(object)

Class Surrogate

Description

General class for surrogate objects.

Slots

we

included to avoid VIRTUAL designation

Methods Available for Objects of Class Surrogate

Description

Utilizes stats::optim to obtain parameter estimates. Requires that the objective function and its derivative are defined by the calling learning method. Returns NULL if optimization is not successful due to problems; a vector of the current parameter estimates if optimization is not successful because it hit the maximum number if iterations; and the list object returned by stats::optim if optimization is successful

Usage

## S4 method for signature 'Surrogate'
.optim(surrogate, par, lambda, fn, gr, suppress, ...)

Class TxInfoBasic

Description

Class TxInfoBasic stores basic treatment information.

Slots

superset

A vector of all possible tx options.

txName

A character - column header of data.frame that contains tx variable

Methods Available for Objects of Class TxInfoBasic

Description

Methods Available for Objects of Class TxInfoBasic

.compareTx(object, vec1, vec2) not allowed

.convertFromBinary(txObj, txVec) converts a -1/1 Tx to user provided tx coding

.convertToBinary(txObj, data) converts user specified tx variable to binary -1/1

.convertTx(object, txVec) not allowed

.getLevels(object, txVec) not allowed

.getSuperset(object) retrieves superset information

.getTxName(object) retrieve tx variable name

.validTx(object, txVec) ensures all elements in txVec are allowed by superset

Usage

## S4 method for signature 'TxInfoBasic,ANY,ANY'
.compareTx(object, vec1, vec2)

## S4 method for signature 'TxInfoBasic'
.convertFromBinary(txObj, txVec, ...)

## S4 method for signature 'TxInfoBasic'
.convertToBinary(txObj, ..., txVec)

## S4 method for signature 'TxInfoBasic'
.convertTx(object, txVec)

## S4 method for signature 'TxInfoBasic,ANY'
.getLevels(object, txVec)

## S4 method for signature 'TxInfoBasic'
.getSuperset(object)

## S4 method for signature 'TxInfoBasic'
.getTxName(object)

## S4 method for signature 'TxInfoBasic'
.validTx(object, txVec)

Class TxInfoFactor

Description

Class TxInfoFactor extends TxInfoBasic to identify treatments as factor

Slots

superset

character of all allowed tx options

Methods Available for Objects of Class TxInfoFactor

Description

Methods Available for Objects of Class TxInfoFactor

.compareTx(object, vec1, vec2) compares vec1 and vec2 to identify equivalent elements.

.convertFromBinary(txObj, txVec) converts a -1/1 Tx to user provided tx coding

.compareTx(object, vec1, vec2) converts txVec to factor.

.getLevels(object, txVec) determines tx levels contains in txVec.

Usage

## S4 method for signature 'TxInfoFactor,factor,factor'
.compareTx(object, vec1, vec2)

## S4 method for signature 'TxInfoFactor'
.convertFromBinary(txObj, txVec, ...)

## S4 method for signature 'TxInfoFactor'
.convertTx(object, txVec)

## S4 method for signature 'TxInfoFactor,factor'
.getLevels(object, txVec)

Class TxInfoInteger

Description

Class TxInfoInteger extends TxInfoBasic to identify treatments as integer.

Methods Available for Objects of Class TxInfoInteger

Description

Methods Available for Objects of Class TxInfoInteger

.compareTx(object, vec1, vec2) compares vec1 and vec2 to identify equivalent elements.

.convertFromBinary(txObj, txVec) converts a -1/1 Tx to user provided tx coding

.compareTx(object, vec1, vec2) converts txVec to factor.

.getLevels(object, txVec) determines tx levels contains in txVec.

Usage

## S4 method for signature 'TxInfoInteger,integer,integer'
.compareTx(object, vec1, vec2)

## S4 method for signature 'TxInfoInteger'
.convertFromBinary(txObj, txVec, ...)

## S4 method for signature 'TxInfoInteger'
.convertTx(object, txVec)

## S4 method for signature 'TxInfoInteger,integer'
.getLevels(object, txVec)

Class TxInfoList

Description

Class TxInfoList extends class TxObj to indicate that \@txInfo is of class List. Each element of that list corresponds to a decision point. All methods called with this object throw errors.

Slots

txInfo

A List object

Methods Available for Objects of Class TxInfoList

Description

Methods Available for Objects of Class TxInfoList

.getPtsSubset(object) not allowed.

.getSingleton(object) not allowed.

.getSubsetRule(object) not allowed.

.getSubsets(object) not allowed.

.getSuperset(object) not allowed.

.getTxName(object) not allowed.

.validTx(object, txVec) not allowed.

.compareTx(object, vec1, vec2) not allowed.

.convertTx(object, txVec) not allowed.

.getLevels(object, txVec) not allowed.

Usage

## S4 method for signature 'TxInfoList'
.getPtsSubset(object)

## S4 method for signature 'TxInfoList'
.getSingleton(object)

## S4 method for signature 'TxInfoList'
.getSubsetRule(object)

## S4 method for signature 'TxInfoList'
.getSubsets(object)

## S4 method for signature 'TxInfoList'
.getSuperset(object)

## S4 method for signature 'TxInfoList'
.getTxName(object)

## S4 method for signature 'TxInfoList'
.validTx(object)

## S4 method for signature 'TxInfoList,ANY,ANY'
.compareTx(object)

## S4 method for signature 'TxInfoList'
.convertTx(object)

## S4 method for signature 'TxInfoList,ANY'
.getLevels(object)

Class TxInfoNoSubsets

Description

Class TxInfoNoSubsets extends class TxObj to indicate that \@txInfo is of class TxInfoBasic and thus no subsets were identified.

Slots

txInfo

A TxInfoBasic object

Methods Available for Objects of Class TxInfoNoSubsets

Description

Methods Available for Objects of Class TxInfoNoSubsets

Class TxInfoWithSubsets

Description

Class TxInfoWithSubsets extends class TxObj to indicate that \@txInfo is of class TxInfoSubset and thus subsets were identified.

Slots

txInfo

A TxSubset object

Methods Available for Objects of Class TxInfoWithSubsets

Description

Methods Available for Objects of Class TxInfoWithSubsets

.getPtsSubset(object) retrieves subset name to which each pt is a member. Method dispatched depends on class of @txInfo.

.getSingleton(object) retrieves T/F indicating if >1 tx is available to each pt. Method dispatched depends on class of @txInfo.

.getSubsetRule(object) retrieves feasible tx function. Method dispatched depends on class of @txInfo.

.getSubsets(object) retrieves feasible tx information. Method dispatched depends on class of @txInfo.

Usage

## S4 method for signature 'TxInfoWithSubsets'
.getPtsSubset(object)

## S4 method for signature 'TxInfoWithSubsets'
.getSingleton(object)

## S4 method for signature 'TxInfoWithSubsets'
.getSubsetRule(object)

## S4 method for signature 'TxInfoWithSubsets'
.getSubsets(object)

Class TxObj

Description

Storage Class to group tx information under a common name.

Slots

txInfo

Any object – expected to be of class TxInfoBasic, TxInfoSubset, or DecisionPointList

Methods Available for Objects of Class TxObj

Description

Methods dispatched depend on class of @txInfo.

Usage

## S4 method for signature 'TxObj,ANY,ANY'
.compareTx(object, vec1, vec2)

## S4 method for signature 'TxObj'
.convertFromBinary(txObj, ...)

## S4 method for signature 'TxObj'
.convertToBinary(txObj, ...)

## S4 method for signature 'TxObj'
.convertTx(object, txVec)

## S4 method for signature 'TxObj,ANY'
.getLevels(object, txVec)

## S4 method for signature 'TxObj'
.getSuperset(object)

## S4 method for signature 'TxObj'
.getTxName(object)

## S4 method for signature 'TxObj'
.validTx(object, txVec)

Class TxSubset

Description

Class TxSubset stores subset information for tx

Slots

ptsSubset

A character vector. The name of the subset of which each patient is a member

subsetRule

A function. The fSet function provided by user.

subsets

A list. The feasible treatments for each subset. The elements must be named and contain tx subsets

singleton

A logical vector. TRUE indicates if 1 tx is available to each patient

Methods Available for Objects of Class TxSubset

Description

Methods Available for Objects of Class TxSubset

.convertFromBinary(txObj, txVec) converts a -1/1 Tx to user provided tx coding.

.convertToBinary(txObj, data) converts user specified tx variable to binary -1/1.

.getPtsSubset(object) retrieve subset name for which each pt is a member.

.getSingleton(object) retrieve T/F indicator of only 1 tx option available to each pt.

.getSubsetRule(object) retrieve feasible set identification rule.

.getSubsets(object) retrieve subset names and tx options.

.validTx(object, txVec) ensures all elements in txVec are allowed by superset.

Usage

## S4 method for signature 'TxSubset'
.convertFromBinary(txObj, ..., txVec)

## S4 method for signature 'TxSubset'
.convertToBinary(txObj, ...)

## S4 method for signature 'TxSubset'
.getPtsSubset(object)

## S4 method for signature 'TxSubset'
.getSingleton(object)

## S4 method for signature 'TxSubset'
.getSubsetRule(object)

## S4 method for signature 'TxSubset'
.getSubsets(object)

## S4 method for signature 'TxSubset'
.validTx(object, txVec)

Class TxSubsetFactor

Description

Class TxSubsetFactor stores subset information for tx when tx is a factor

Methods Available for Objects of Class TxSubsetFactor

Description

Methods Available for Objects of Class TxSubsetFactor

.convertFromBinary(txObj, txVec) converts a -1/1 Tx to user provided tx coding. Call method defined for TxSubset.

.convertToBinary(txObj, data) converts user specified tx variable to binary -1/1. Call method defined for TxSubset.

.getPtsSubset(object) retrieve subset name for which each pt is a member. Call method defined for TxSubset.

.getSingleton(object) retrieve T/F indicator of only 1 tx option available to each pt. Call method defined for TxSubset.

.getSubsetRule(object) retrieve feasible set identification rule. Call method defined for TxSubset.

.getSubsets(object) retrieve subset names and tx options. Call method defined for TxSubset.

.compareTx(object, vec1, vec2) compares vec1 and vec2 to identify equivalent elements.

.compareTx(object, vec1, vec2) converts txVec to factor.

.getLevels(object, txVec) determines tx levels contains in txVec.

.getSuperset(object) retrieves superset. Uses method defined for TxInfoFactor objects.

.getTxName(object) retrieves tx variable name. Uses method defined for TxInfoFactor objects.

.validTx(object, txVec) ensures all elements in txVec are allowed by superset.

Usage

## S4 method for signature 'TxSubsetFactor'
.convertFromBinary(txObj, ..., txVec)

## S4 method for signature 'TxSubsetFactor'
.convertToBinary(txObj, ..., txVec, data)

## S4 method for signature 'TxSubsetFactor'
.getPtsSubset(object)

## S4 method for signature 'TxSubsetFactor'
.getSingleton(object)

## S4 method for signature 'TxSubsetFactor'
.getSubsetRule(object)

## S4 method for signature 'TxSubsetFactor'
.getSubsets(object)

## S4 method for signature 'TxSubsetFactor,factor,factor'
.compareTx(object, vec1, vec2)

## S4 method for signature 'TxSubsetFactor'
.convertTx(object, txVec)

## S4 method for signature 'TxSubsetFactor,factor'
.getLevels(object, txVec)

## S4 method for signature 'TxSubsetFactor'
.getSuperset(object)

## S4 method for signature 'TxSubsetFactor'
.getTxName(object)

## S4 method for signature 'TxSubsetFactor'
.validTx(object, txVec)

Class TxSubsetInteger

Description

Class TxSubsetInteger stores subset information for treatment

Methods Available for Objects of Class TxSubsetInteger

Description

Methods Available for Objects of Class TxSubsetInteger

.convertFromBinary(txObj, txVec) converts a -1/1 Tx to user provided tx coding. Call method defined for TxSubset.

.convertToBinary(txObj, data) converts user specified tx variable to binary -1/1. Call method defined for TxSubset.

.getPtsSubset(object) retrieve subset name for which each pt is a member. Call method defined for TxSubset.

.getSingleton(object) retrieve T/F indicator of only 1 tx option available to each pt. Call method defined for TxSubset.

.getSubsetRule(object) retrieve feasible set identification rule. Call method defined for TxSubset.

.getSubsets(object) retrieve subset names and tx options. Call method defined for TxSubset.

.compareTx(object, vec1, vec2) compares vec1 and vec2 to identify equivalent elements.

.compareTx(object, vec1, vec2) converts txVec to factor.

.getLevels(object, txVec) determines tx levels contains in txVec.

.getSuperset(object) retrieves superset. Uses method defined for TxInfoInteger objects.

.getTxName(object) retrieves tx variable name. Uses method defined for TxInfoInteger objects.

.validTx(object, txVec) ensures all elements in txVec are allowed by superset.

Usage

## S4 method for signature 'TxSubsetInteger'
.convertFromBinary(txObj, txVec, ...)

## S4 method for signature 'TxSubsetInteger'
.convertToBinary(txObj, ..., txVec, data)

## S4 method for signature 'TxSubsetInteger'
.getPtsSubset(object)

## S4 method for signature 'TxSubsetInteger'
.getSingleton(object)

## S4 method for signature 'TxSubsetInteger'
.getSubsetRule(object)

## S4 method for signature 'TxSubsetInteger'
.getSubsets(object)

## S4 method for signature 'TxSubsetInteger,integer,integer'
.compareTx(object, vec1, vec2)

## S4 method for signature 'TxSubsetInteger'
.convertTx(object, txVec)

## S4 method for signature 'TxSubsetInteger,integer'
.getLevels(object, txVec)

## S4 method for signature 'TxSubsetInteger'
.getSuperset(object)

## S4 method for signature 'TxSubsetInteger'
.getTxName(object)

## S4 method for signature 'TxSubsetInteger'
.validTx(object, txVec)

Class TypedFit

Description

Class TypedFit is a modelObjFit combined with a character to identify its purpose.

Methods Available for Objects of Class TypedFit

Description

Methods call equivalently named methods defined for modelObjFit objects.

Usage

## S4 method for signature 'TypedFit'
coef(object, ...)

## S4 method for signature 'TypedFit'
fitObject(object, ...)

## S4 method for signature 'TypedFit,ANY'
plot(x, suppress = FALSE, ...)

## S4 method for signature 'TypedFit'
print(x, ...)

## S4 method for signature 'TypedFit'
show(object)

## S4 method for signature 'TypedFit'
summary(object, ...)

Class TypedFitObj

Description

Class TypedFit_SubsetList allows for TypedFit based objects to be grouped under a common name.

Slots

fit

ANY - expected to be TypedFit, TypedFit_fSet. TypedFit_SubsetList or DecisionPointList of these.

Methods Available for Objects of Class TypedFitObj

Description

Methods call equivalently named methods defined for TypedFit, TypedFit_fSet, TypedFit_SubsetList or DecisionPointList objects. The methods dispatched and objects returned depend on the class of @fit.

Usage

## S4 method for signature 'TypedFitObj'
coef(object, ...)

## S4 method for signature 'TypedFitObj'
fitObject(object, ...)

## S4 method for signature 'TypedFitObj,ANY'
plot(x, suppress = FALSE, ...)

## S4 method for signature 'TypedFitObj'
predict(object, ...)

## S4 method for signature 'TypedFitObj'
print(x, ...)

## S4 method for signature 'TypedFitObj'
show(object)

## S4 method for signature 'TypedFitObj'
summary(object, ...)

Class TypedFit_SubsetList

Description

Class TypedFit_SubsetList is SubsetList of TypedFit used when subsets are identified and modeled independently.

Methods Available for Objects of Class TypedFit_SubsetList

Description

Methods call equivalently named methods defined for TypedFit objects. When a value object is returned, it is a list. The element names of the list are the subset names to which the result pertains.

predict(object, ...) Patients not in subset are NA.

Usage

## S4 method for signature 'TypedFit_SubsetList'
coef(object, ...)

## S4 method for signature 'TypedFit_SubsetList'
fitObject(object, ...)

## S4 method for signature 'TypedFit_SubsetList,ANY'
plot(x, suppress = FALSE, ...)

## S4 method for signature 'TypedFit_SubsetList'
predict(object, newdata, ...)

## S4 method for signature 'TypedFit_SubsetList'
print(x, ...)

## S4 method for signature 'TypedFit_SubsetList'
show(object)

## S4 method for signature 'TypedFit_SubsetList'
summary(object, ...)

Class TypedFit_fSet

Description

Class TypedFit_fSet is a TypedFit when subsets are identified but not modeled independently.

Methods Available for Objects of Class TypedFit_fSet

Description

Methods call equivalently named methods defined for TypedFit objects.

predict(object, ...) Patients with only 1 tx option are NA.

Usage

## S4 method for signature 'TypedFit_fSet'
coef(object, ...)

## S4 method for signature 'TypedFit_fSet'
fitObject(object, ...)

## S4 method for signature 'TypedFit_fSet,ANY'
plot(x, suppress = FALSE, ...)

## S4 method for signature 'TypedFit_fSet'
predict(object, newdata, ...)

## S4 method for signature 'TypedFit_fSet'
print(x, ...)

## S4 method for signature 'TypedFit_fSet'
show(object)

## S4 method for signature 'TypedFit_fSet'
summary(object, ...)

Adolescent BMI dataset (generated toy example)

Description

A dataset generated to mimic data from a two-stage randomized clinical trial that studied the effect of meal replacement shakes on adolescent obesity. The dataset contains the following covariates collected at the start of the first stage: "gender," "race," "parentBMI," and "baselineBMI." At the second-stage, "month4BMI" was collected. Variables "A1" and "A2" are the randomized treatments at stages one and two, and "month12BMI" is the primary outcome collected at the end of stage two.

Format

A matrix with rows corresponding to patients.

Source

Generated by Kristin A. Linn in R

Backwards Outcome Weighted Learning.

Description

Function performs a single step of the bowl method. Multiple decision points can be analyzed by repeated calls, as is done for qLearn() and optimalClass().

Usage

bowl(
  ...,
  moPropen,
  data,
  reward,
  txName,
  regime,
  response,
  BOWLObj = NULL,
  lambdas = 2,
  cvFolds = 0L,
  kernel = "linear",
  kparam = NULL,
  fSet = NULL,
  surrogate = "hinge",
  verbose = 2L
)

Arguments

Value

a BOWL-class object

References

Yingqi Zhao, Donglin Zeng, Eric B. Laber, Michael R. Kosorok (2015) New statistical learning methods for estimating optimal dynamic treatment regimes. Journal of the American Statistical Association, 110:510, 583–598.

See Also

Other statistical methods: earl(), iqLearn, optimalClass(), optimalSeq(), owl(), qLearn(), rwl()

Other weighted learning methods: earl(), owl(), rwl()

Other multiple decision point methods: iqLearn, optimalClass(), optimalSeq(), qLearn()

Examples

 
# Load and process data set
data(bmiData)

# define the negative 12 month change in BMI from baseline
y12 <- -100*(bmiData[,6L] - bmiData[,4L])/bmiData[,4L]

# define the negative 4 month change in BMI from baseline
y4 <- -100*(bmiData[,5L] - bmiData[,4L])/bmiData[,4L]

# reward for second stage
rewardSS <- y12 - y4

#### Second-stage regression

# Constant propensity model
moPropen <- buildModelObj(model = ~1,
                          solver.method = 'glm',
                          solver.args = list('family'='binomial'),
                          predict.method = 'predict.glm',
                          predict.args = list(type='response'))

fitSS <- bowl(moPropen = moPropen,
              data = bmiData, reward = rewardSS,  txName = 'A2', 
              regime = ~ parentBMI + month4BMI)

##Available methods

  # Coefficients of the propensity score regression
  coef(fitSS)

  # Description of method used to obtain object
  DTRstep(fitSS)

  # Estimated value of the optimal treatment regime for training set
  estimator(fitSS)

  # Value object returned by propensity score regression method
  fitObject(fitSS)

  # Summary of optimization routine
  optimObj(fitSS)

  # Estimated optimal treatment for training data
  optTx(fitSS)

  # Estimated optimal treatment for new data
  optTx(fitSS, bmiData)

  # Plots if defined by propensity regression method
  dev.new()
  par(mfrow = c(2,4))

  plot(fitSS)
  plot(fitSS, suppress = TRUE)

  # Value object returned by propensity score regression method
  propen(fitSS)

  # Parameter estimates for decision function
  regimeCoef(fitSS)

  # Show main results of method
  show(fitSS)

  # Show summary results of method
  summary(fitSS)
 
#### First-stage regression

# Constant propensity model
fitFS <- bowl(moPropen = moPropen,
              data = bmiData, reward = y4,  txName = 'A1', 
              regime = ~ gender + parentBMI,
              BOWLObj = fitSS, lambdas = c(0.5, 1.0), cvFolds = 4L)

##Available methods for fitFS are as shown above for fitSS

  # Results of the cross-validation
  cvInfo(fitFS)

Create Model Objects for Subsets of Data

Description

Extends the buildModelObj() function of package modelObj. Here, the returned model object includes a specification of the decision point and subset of the data to which the model is to be applied.

Usage

buildModelObjSubset(
  ...,
  model,
  solver.method,
  solver.args = NULL,
  predict.method = NULL,
  predict.args = NULL,
  dp = 1L,
  subset = NA
)

Arguments

solver.method = "glm"
solver.args = list("family"=binomial)

predict.method = "predict.glm"
predict.args = list("type"="response").

Details

In some settings, an analyst may want to use different models for unique subsets of the data. buildModelObjSubset() provides a mechanism for users to define models for such subset. Specifically, models are specified in connection with the decision point and subset to which they are to be applied.

See ?modelObj for further details

Value

An object of class ModelObjSubset, which contains a complete description of the conditions under which a model is to be used and the R methods to be used to obtain parameter estimates and predictions.

Examples

# Consider a 2 decision point trial. At the 1st decision point, the subset of 
# treatment options available to each patient is always set "set1."
# At the 2nd decision point, some patients are eligible to receive
# treatment from set "set2a" and others from set "set2b." The outcome
# for these subsets will be modeled as ~ x1 + x2 and ~ x2 + x3, respectively.
#
# All parameter estimates are to be obtained used lm and predictions obtained using predict.
#
# The following illustrates how to build these model objects.

  model <- list()

  model[[1]] <- buildModelObjSubset(dp = 1, subset = "set1",
                                    model = ~ x1 + x2 + x3, solver.method = 'lm')

  model[[2]] <- buildModelObjSubset(dp = 2, subset = "set2a",
                                    model = ~ ~ x1 + x2, solver.method = 'lm')

  model[[3]] <- buildModelObjSubset(dp = 2, subset = "set2b",
                                    model = ~ x2 + x3, solver.method = 'lm')

Retrieve Classification Regression Analysis

Description

Method retrieves the value object returned by the user specified classification regression modeling object(s). Exact structure of the returned object will vary.

Usage

classif(object, ...)

## S4 method for signature 'OptimalClass'
classif(object, ...)

Arguments

Extract Model Coefficients From Objects Returned by Modeling Functions

Description

A list is returned, one element for each regression step required by the statistical method.

Usage

coef(object, ...)

Arguments

Details

Methods are defined for all statistical methods implemented in DynTxRegime.

The exact structure of the returned list will vary depending on the statistical method. For methods that include a propensity regression, the returned list will include an element named 'propen'. For methods that include an outcome regression, the returned list will include an element named 'outcome'.

Create method object for EARL

Description

Create method object for EARL

Usage

.createearl(kernel, txVec, response, prWgt, surrogate, guess = NULL, mu, ...)

Arguments

Value

An .earl object

Create method object for Outcome Weighted Learning

Description

Create method object for Outcome Weighted Learning

Usage

.createowl(..., kernel, txVec, response, prWgt, surrogate, guess = NULL, mu)

Arguments

Value

An .owl object

Create method object for Residual Weighted Learning

Description

Create method object for Residual Weighted Learning

Create method object for Residual Weighted Learning

Usage

.createrwl(kernel, txVec, response, prWgt, surrogate, guess = NULL, mu, ...)

.createrwlcount(
  kernel,
  txVec,
  response,
  prWgt,
  surrogate,
  guess = NULL,
  mu,
  ...
)

Arguments

Value

An .rwl object

An .rwl object

Extract Cross-Validation Results

Description

Extract cross-validation results from the value object returned by a weighted learning statistical method of DynTxRegime.

Usage

cvInfo(object, ...)

Arguments

Details

Methods are developed for all weighted learning methods implemented in DynTxRegime. Specifically, OWL, RWL, BOWL, and EARL.

Efficient Augmentation and Relaxation Learning

Description

Efficient Augmentation and Relaxation Learning

Usage

earl(
  ...,
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regime,
  iter = 0L,
  fSet = NULL,
  lambdas = 0.5,
  cvFolds = 0L,
  surrogate = "hinge",
  kernel = "linear",
  kparam = NULL,
  verbose = 2L
)

Arguments

Value

an EARL object

References

Ying-Qi Zhao, Eric Laber, Sumona Saha and Bruce E. Sands (2016+) Efficient augmentation and relaxation learning for treatment regimes using observational data

See Also

Other statistical methods: bowl(), iqLearn, optimalClass(), optimalSeq(), owl(), qLearn(), rwl()

Other single decision point methods: optimalClass(), optimalSeq(), owl(), qLearn(), rwl()

Other weighted learning methods: bowl(), owl(), rwl()

Examples

# Load and process data set
data(bmiData)

# define the negative 12 month change in BMI from baseline
y12 <- -100*(bmiData[,6L] - bmiData[,4L])/bmiData[,4L]

# propensity model
moPropen <- buildModelObj(model = ~parentBMI+month4BMI,
                          solver.method = 'glm',
                          solver.args = list('family'='binomial'),
                          predict.method = 'predict.glm',
                          predict.args = list(type='response'))

# outcome model
moMain <- buildModelObj(model = ~parentBMI+month4BMI,
                        solver.method = 'lm')

moCont <- buildModelObj(model = ~parentBMI+month4BMI,
                        solver.method = 'lm')

fitEARL <- earl(moPropen = moPropen, moMain = moMain, moCont = moCont,
              data = bmiData, response = y12,  txName = 'A2', 
              regime = ~ parentBMI + month4BMI,
              surrogate = 'logit', kernel = 'poly', kparam = 2)

##Available methods

  # Coefficients of the regression objects
  coef(fitEARL)

  # Description of method used to obtain object
  DTRstep(fitEARL)

  # Estimated value of the optimal treatment regime for training set
  estimator(fitEARL)

  # Value object returned by regression methods
  fitObject(fitEARL)

  # Summary of optimization routine
  optimObj(fitEARL)

  # Estimated optimal treatment for training data
  optTx(fitEARL)

  # Estimated optimal treatment for new data
  optTx(fitEARL, bmiData)

  # Value object returned by outcome regression method
  outcome(fitEARL)

  # Plots if defined by regression methods
  dev.new()
  par(mfrow = c(2,4))

  plot(fitEARL)
  plot(fitEARL, suppress = TRUE)

  # Value object returned by propensity score regression method
  propen(fitEARL)

  # Parameter estimates for decision function
  regimeCoef(fitEARL)

  # Show main results of method
  show(fitEARL)

  # Show summary results of method
  summary(fitEARL)
 

Retrieve the Estimated Value

Description

Retrieve the value as estimated by the statistical method.

Usage

estimator(x, ...)

## S4 method for signature 'IQLearnFS'
estimator(x, w = NULL, y = NULL, z = NULL, dens = NULL)

## S4 method for signature 'IQLearnSS'
estimator(x, w = NULL, y = NULL, z = NULL, dens = NULL)

Arguments

Defining the fSet Input Variable

Description

Several of the statistical methods implemented in package DynTxRegime allow for subset modeling or limiting of feasible treatment options. This section details how this input is to be defined.

Details

In general, input fSet is used to define subsets of patients within an analysis. These subsets can be specified to (1) limit available treatments, (2) use different models for the propensity score and/or outcome regressions, and/or (3) use different decision function models for each subset of patients. The combination of inputs moPropen, moMain, moCont, fSet, and/or regimes determines which of these scenarios is being considered. We cover some common situations below.

Regardless of the purpose for specifying fSet, it must be a function that returns a list. There are two options for defining the function. Version 1 is that of the original DynTxRegime package. In this version, fSet defines the rules for determining the subset of treatment options for an INDIVIDUAL. The first element of the returned list is a character, which we term the subset 'nickname.' This nickname is for bookkeeping purposes and is used to link models to subsets. The second element of the returned list is a vector of available treatment options for the subset. The formal arguments of the function must include (i) 'data' or (ii) individual covariate names as given by the column headers of data. An example using the covariate name input form is

fSet <- function(a1) {
  if (a1 > 1) {
    subset <- list('subA',c(1,2))
  } else {
    subset <- list('subB',c(3,4) )
  }
  return(subset)
}

This function indicates that if an individual has covariate a1 > 1, they are a member of subset 'subA' and their feasible treatment options are {1,2}. If a1 <= 1, they are a member of subset 'subB' and their feasible treatment options are {3,4}.

A more efficient implementation for fSet is now accepted. In the second form, fSet defines the subset of treatment options for the full DATASET. It is again a function with formal arguments (i) 'data' or (ii) individual covariate names as given by the column headers of data. The function returns a list containing two elements: 'subsets' and 'txOpts.' Element 'subsets' is a list comprising all treatment subsets; each element of the list contains the nickname and treatment options for a single subset. Element 'txOpts' is a character vector indicating the subset of which each individual is a member. In this new format, the equivalent definition of fSet as that given above is:

fSet <- function(a1) {
  subsets <- list(list('subA', c(1,2)),
                  list('subB', c(3,4)))
  txOpts <- rep('subB', length(x = a1))
  txOpts[a1 > 1] <- 'subA'

  return(list("subsets" = subsets,
              "txOpts" = txOpts))
}

Though a bit more complicated, this version is much more efficient as it processes the entire dataset at once rather than each individual separately.

The simplest scenario involving fSet is to define feasible treatment options and the rules that dictate how those treatment options are determined. For example, responder/non-responder scenarios are often encountered in multiple-decision-point settings. An example of this scenario is: patients that respond to the first stage treatment remain on the original treatment; those that do not respond to the first stage treatment have all treatment options available to them at the second stage. In this case, the propensity score models for the second stage are fit using only 'non-responders' for whom more than 1 treatment option is available.

An example of an appropriate fSet function for the second-stage is

 
fSet <- function(data) { 
   if (data\$responder  == 0L) { 
     subset <- list('subA',c(1L,2L))
   } else if (data\$tx1 == 1L) { 
     subset <- list('subB',c(1L) )
   } else if (data\$tx1 == 2L) { 
     subset <- list('subC',c(2L) )
   } 
   return(subset) 
} 

for version 1 or for version 2

fSet <- function(data) {
  subsets <- list(list('subA', c(1L,2L)),
                  list('subB', c(1L)),
                  list('subC', c(2L)))
  txOpts <- character(nrow(x = data))
  txOpts[data$tx1 == 1L] <- 'subB'
  txOpts[data$tx1 == 2L] <- 'subC'
  txOpts[data$responder == 0L] <- 'subA'

  return(list("subsets" = subsets,
              "txOpts" = txOpts))
}

The functions above specify that patients with covariate responder = 0 receive treatments from subset 'subA,' which comprises treatments A = (1,2). Patients with covariate responder = 1 receive treatment from subset 'subB' or 'subC' depending on the first stage treatment received. If fSet is specified in this way, the form of the model object depends on the training data. Specifically, if the training data obeys the feasible treatment rule (here, all individuals with responder = 1 received tx in accordance with fSet), moPropen would be a "modelObj"; the propensity model will be fit using only those patients with responder = 0; those with responder = 1 always receive the appropriate second stage treatment with probability 1.0. However, if the data are from an observation study and the training data do not obey the feasible treatment rules (here, some individuals with responder = 1 received tx = 0; others tx = 1), the responder = 1 data must be modeled and moPropen must be provided as one or more ModelObjSubset() objects.

If outcome regression is used by the method, moMain and moCont can be either objects of class "modelObj" if only responder = 0 patients are to be used to obtain parameter estimates or as lists of objects of class "ModelObjSubset" if subsets are to be analyzed individually or combined for a single fit of all data.

For a scenario where all patients have the same set of treatment options available, but subsets of patients are to be analyzed using different models. We cane define fSet as

 
fSet <- function(data) { 
   if (data\$a1 == 1) { 
     subset <- list('subA',c(1L,2L))
   } else { 
     subset <- list('subB',c(1L,2L) )
   } 
   return(subset) 
} 

for version 1 or in the format of version 2

fSet <- function(data)
{
  subsets <- list(list('subA', c(1L,2L)),
                  list('subB', c(1L,2L)))
  txOpts <- rep('subB', nrow(x = data))
  txOpts[data$a1 == 1L] <- 'subA'

  return(list("subsets" = subsets,
              "txOpts" = txOpts))
}

where all patients have the same treatment options available, A = (1,2), but different regression models will be fit for each subset (case 2 above) and/or different decision function models (case 3 above) for each subset. If different propensity score models are used, moPropen must be a list of objects of class "modelObjSubset." Perhaps,

 
  propenA <- buildModelObjSubset(model = ~1,
                                 solver.method = 'glm',
                                 solver.args = list('family'='binomial'),
                                 predict.method = 'predict.glm',
                                 predict.args = list(type='response'),
                                 subset = 'subA')

  propenB <- buildModelObjSubset(model = ~1,
                                 solver.method = 'glm',
                                 solver.args = list('family'='binomial'),
                                 predict.method = 'predict.glm',
                                 predict.args = list(type='response'),
                                 subset = 'subB')

  moPropen <- list(propenA, propenB)
 

If different decision function models are to be fit, regimes would take a form similar to

 
  regimes <- list( 'subA' = ~x1 + x2,
                   'subB' = ~x2 )

Notice that the names of the elements of regimes and the subsets passed to buildModelObjSubset() correspond to the names defined by fSet, i.e., 'subA' or 'subB.' These nicknames are used for bookkeeping and link subsets to the appropriate models.

For a single-decision-point analysis, fSet is a single function. For multiple-decision-point analyses, fSet is a list of functions where each element of the list corresponds to the decision point (1st element <- 1st decision point, etc.)

Objects Returned by Modeling Functions

Description

Returns a list of the objects returned by all modeling functions

Usage

fitObject(object, ...)

Arguments

Details

Methods are defined for all statistical methods implemented in DynTxRegime.

The exact structure of the returned list will vary depending on the statistical method. For methods that include a propensity regression, the returned list will include an element named 'propen'. For methods that include an outcome regression, the returned list will include an element named 'outcome'.

Retrieve the Fitted Contrast Component from Second Stage IQ-Learning

Description

Extracts the contrasts component of the fitted outcome regression the second-stage analysis of the interactive Q-Learning algorithm.

Usage

fittedCont(object, ...)

## S4 method for signature 'IQLearnSS'
fittedCont(object, ...)

Arguments

Retrieve the Fitted Main Effects Component from Second Stage IQ-Learning

Description

Extracts the main effects component of the fitted outcome regression for the second-stage analysis of the interactive Q-Learning algorithm.

Usage

fittedMain(object, ...)

## S4 method for signature 'IQLearnSS'
fittedMain(object, ...)

Arguments

Retrieve the Genetic Algorithm Results

Description

Retrieve the value object returned by rgenoud() in optimalSeq().

Usage

genetic(object, ...)

## S4 method for signature 'OptimalSeq'
genetic(object, ...)

Arguments

Retrieve Outcome for Both Tx Options When Tx is Binary

Description

Retrieve Outcome for Both Tx Options When Tx is Binary

Retrieve Outcome for Both Tx Options When Tx is Binary

Usage

.getOutcome(outcomeObj, txObj, data)

.getOutcome2(outcomeObj, txObj, data)

Arguments

Value

matrix of outcome under binary tx.

matrix of outcome under binary tx.

Retrieve Propensity for Tx Received

Description

Retrieve Propensity for Tx Received

Usage

.getPrWgt(propenObj, txObj, data)

Arguments

Value

vector of propensity for tx received.

Class .earl

Description

Class .earl stores parameters required for EARL optimization step.

Slots

x

Matrix of covariates for kernel

wp

Vector of positive weights

wn

Vector of negative weights

mu

Matrix of outcome regression

txVec

Vector of treatment coded as -1/1

invPi

Vector of inverse propensity for treatment received

response

Vector of the response

surrogate

The Surrogate for the loss-function

par

Vector of regime parameters

kernel

The Kernel defining the decision function

Methods Available for Objects of Class .earl

Description

Methods Available for Objects of Class .earl

.objFn not allowed for EARL with multiple radial kernels

.dobjFn not allowed for EARL with multiple radial kernels

Usage

## S4 method for signature '.earl'
.subsetObject(methodObject, subset)

## S4 method for signature 'numeric,.earl,LinearKernel'
.objFn(par, methodObject, kernel, lambda, ...)

## S4 method for signature 'numeric,.earl,LinearKernel'
.dobjFn(par, methodObject, kernel, lambda, ...)

## S4 method for signature 'numeric,.earl,Kernel'
.objFn(par, methodObject, kernel, lambda, ...)

## S4 method for signature 'numeric,.earl,Kernel'
.dobjFn(par, methodObject, kernel, lambda, ...)

## S4 method for signature 'numeric,.earl,MultiRadialKernel'
.objFn(par, methodObject, kernel, lambda, ...)

## S4 method for signature 'numeric,.earl,MultiRadialKernel'
.dobjFn(par, methodObject, kernel, lambda, ...)

## S4 method for signature '.earl'
.valueFunc(methodObject, optTx, ...)

Class .owl

Description

Class .owl stores parameters required for OWL optimization step

Slots

x

Matrix of covariates for kernel

txSignR

Vector of tx multiplied by the sign of the response

txVec

Vector of tx coded as -1/1

absRinvPi

Vector of the absolute value of the response weighted by the propensity for the tx received

response

Vector of the response

invPi

Vector of the inverse of the propensity for the tx received

surrogate

The Surrogate for the loss-function

pars

Vector of regime parameters

kernel

The Kernel defining the decision function

Methods Available for Objects of Class .owl

Description

Methods Available for Objects of Class .owl

.objFn is not allowed for OWL with multiple radial kernels

.dobjFn is not allowed for OWL with multiple radial kernels

Usage

## S4 method for signature '.owl'
.subsetObject(methodObject, subset)

## S4 method for signature 'numeric,.owl,Kernel'
.objFn(par, methodObject, kernel, ..., lambda)

## S4 method for signature 'numeric,.owl,Kernel'
.dobjFn(par, methodObject, kernel, ..., lambda)

## S4 method for signature 'numeric,.owl,LinearKernel'
.objFn(par, methodObject, kernel, ..., lambda)

## S4 method for signature 'numeric,.owl,LinearKernel'
.dobjFn(par, methodObject, kernel, ..., lambda)

## S4 method for signature 'numeric,.owl,MultiRadialKernel'
.objFn(par, methodObject, kernel, ..., lambda)

## S4 method for signature 'numeric,.owl,MultiRadialKernel'
.dobjFn(par, methodObject, kernel, ..., lambda)

## S4 method for signature '.owl'
.valueFunc(methodObject, ..., optTx)

Class .rwl

Description

Class .rwl stores parameters required for an RWL optimization step

Slots

x

Matrix of covariates for kernel

txVec

Vector of treatment coded as -1/1

absRinvPi

Vector of the absolute value of the residual weighted by the propensity for the treatment received

residual

Vector of the residuals

response

Vector of the response

beta

Vector of beta parameters

surrogate

The Surrogate for the loss-function

pars

Vector of regime parameters

kernel

The Kernel defining the decision function

Methods Available for Objects of Class .rwl

Description

Methods Available for Objects of Class .rwl

.objFn not allowed for RWL With multiple radial kernels

.dobjFn not allowed for RWL With multiple radial kernels

Usage

## S4 method for signature '.rwl'
.subsetObject(methodObject, subset)

## S4 method for signature 'numeric,.rwl,LinearKernel'
.objFn(par, methodObject, kernel, lambda, ...)

## S4 method for signature 'numeric,.rwl,LinearKernel'
.dobjFn(par, methodObject, kernel, lambda, ...)

## S4 method for signature 'numeric,.rwl,Kernel'
.objFn(par, methodObject, kernel, lambda, ...)

## S4 method for signature 'numeric,.rwl,Kernel'
.dobjFn(par, methodObject, kernel, lambda, ...)

## S4 method for signature 'numeric,.rwl,MultiRadialKernel'
.objFn(par, methodObject, kernel, lambda, ...)

## S4 method for signature 'numeric,.rwl,MultiRadialKernel'
.dobjFn(par, methodObject, kernel, lambda, ...)

## S4 method for signature '.rwl'
.valueFunc(methodObject, optTx, ...)

Interactive Q-Learning

Description

The complete interactive Q-Learning algorithm.

Usage

## Second-Stage Analysis
iqLearnSS(..., moMain, moCont, data, response, txName, iter = 0L, 
          verbose = TRUE)

## First-Stage Analysis for Fitted Main Effects
iqLearnFSM(..., moMain, moCont, data, response, txName, iter = 0L, 
           verbose = TRUE)

## First-Stage Analysis for Fitted Contrasts
iqLearnFSC(..., moMain, moCont, data, response, txName, iter = 0L, 
           verbose = TRUE)

## First-Stage Analysis of Contrast Variance Log-Linear Model
iqLearnFSV(..., object, moMain, moCont, data, iter = 0L, verbose = TRUE)

Arguments

References

Laber, EB, Linn, KA, and Stefanski, LA (2014). Interactive model building for Q-Learning. Biometrika, 101, 831–847. PMCID: PMC4274394.

See Also

Other statistical methods: bowl(), earl(), optimalClass(), optimalSeq(), owl(), qLearn(), rwl()

Other multiple decision point methods: bowl(), optimalClass(), optimalSeq(), qLearn()

Examples

# Load and process data set
data(bmiData)

# define the negative 12 month change in BMI from baseline
y12 <- -100*(bmiData[,6L] - bmiData[,4L])/bmiData[,4L]

#### Full Interactive Q-Learning Algorithm

### Second-Stage Analysis

# outcome model
moMain <- buildModelObj(model = ~parentBMI+month4BMI,
                        solver.method = 'lm')

moCont <- buildModelObj(model = ~race + parentBMI+month4BMI,
                        solver.method = 'lm')

fitSS <- iqLearnSS(moMain = moMain, moCont = moCont,
                   data = bmiData, response = y12,  txName = 'A2')

### First-Stage Analysis Main Effects Term

# main effects model
moMain <- buildModelObj(model = ~parentBMI+baselineBMI,
                        solver.method = 'lm')

moCont <- buildModelObj(model = ~race + parentBMI+baselineBMI,
                        solver.method = 'lm')

fitFSM <- iqLearnFSM(moMain = moMain, moCont = moCont,
                     data = bmiData, response = fitSS,  txName = 'A1')

### First-Stage Analysis Contrasts Term

# contrasts model
moMain <- buildModelObj(model = ~parentBMI+baselineBMI,
                        solver.method = 'lm')

moCont <- buildModelObj(model = ~race + parentBMI+baselineBMI,
                        solver.method = 'lm')

fitFSC <- iqLearnFSC(moMain = moMain, moCont = moCont,
                     data = bmiData, response = fitSS,  txName = 'A1')

### First-Stage Analysis Contrasts Variance - Log-linear

# contrasts variance model
moMain <- buildModelObj(model = ~baselineBMI,
                        solver.method = 'lm')

moCont <- buildModelObj(model = ~baselineBMI,
                        solver.method = 'lm')

fitFSV <- iqLearnFSV(object = fitFSC, moMain = moMain, moCont = moCont,
                     data = bmiData)

####Available methods

  ### Estimated value
  estimator(x = fitFSC, y = fitFSM, z = fitFSV, w = fitSS, dens = 'nonpar')

  ## Estimated optimal treatment and decision functions for training data
  ## Second stage optimal treatments
  optTx(x = fitSS)

  ## First stage optimal treatments when contrast variance is modeled.
  optTx(x = fitFSM, y = fitFSC, z = fitFSV, dens = 'nonpar')

  ## First stage optimal treatments when contrast variance is constant.
  optTx(x = fitFSM, y = fitFSC, dens = 'nonpar')

  ## Estimated optimal treatment and decision functions for new data
  ## Second stage optimal treatments
  optTx(x = fitSS, bmiData)

  ## First stage optimal treatments when contrast variance is modeled.
  optTx(x = fitFSM, y = fitFSC, z = fitFSV, dens = 'nonpar', bmiData)

  ## First stage optimal treatments when contrast variance is constant.
  optTx(x = fitFSM, y = fitFSC, dens = 'nonpar', bmiData)

### The following methods are available for all objects: fitSS, fitFSM,
### fitFSC and fitFSV. We include only one here for illustration.

  # Coefficients of the outcome regression objects
  coef(object = fitSS)

  # Description of method used to obtain object
  DTRstep(object = fitFSM)

  # Value object returned by outcome regression method
  fitObject(object = fitFSC)

  # Value object returned by outcome regression method
  outcome(object = fitFSV)

  # Plots if defined by outcome regression method
  dev.new()
  par(mfrow = c(2,4))

  plot(x = fitSS)
  plot(x = fitSS, suppress = TRUE)

  # Show main results of method
  show(object = fitFSM)

  # Show summary results of method
  summary(object = fitFSV)

Defining the iter Input Variable

Description

Several of the statistical methods implemented in package DynTxRegime allow for an iterative algorithm when completing an outcome regression. This section details how this input is to be defined.

Details

Outcome regression models are specified by the main effects components (moMain) and the contrasts component (moCont). Assuming that the treatment is denoted as binary A, the full regression model is: moMain + A*moCont. There are two ways to fit this model: (i) in the full model formulation (moMain + A*moCont) or (ii) each component, moMain and moCont, is fit separately. iter specifies if (i) or (ii) should be used.

iter >= 1 indicates that moMain and moCont are to be fit separately using an iterative algorithm. iter is the maximum number of iterations. Assume Y = Ymain + Ycont; the iterative algorithm is as follows:

(1) hat(Ycont) = 0;

(2) Ymain = Y - hat(Ycont);

(3) fit Ymain ~ moMain;

(4) set Ycont = Y - hat(Ymain)

(5) fit Ycont ~ A*moCont;

(6) Repeat steps (2) - (5) until convergence or a maximum of iter iterations.

This choice allows the user to specify, for example, a linear main effects component and a non-linear contrasts component.

iter <= 0 indicates that the full model formulation is to be used. The components moMain and moCont will be combined in the package and fit as a single object. Note that if iter <= 0, all non-model components of moMain and moCont must be identical. Specifically, the regression method and any non-default arguments should be identical. By default, the specifications in moMain are used.

Defining the moPropen Input Variable

Description

Several of the statistical methods implemented in package DynTxRegime use propensity score modeling. This section details how this input is to be defined.

Details

For input moPropen, the method specified to obtain predictions MUST return the prediction on the scale of the probability, i.e., predictions must be in the range (0,1). In addition, moPropen differs from standard "modelObj" objects in that an additional element may be required in predict.args. Recall, predict.args is the list of control parameters passed to the prediction method. An additional control parameter, propen.missing can be included. propen.missing takes value "smallest" or "largest". It will be required if the prediction method returns predictions for only a subset of the treatment data; e.g., predict.glm(). propen.missing indicates if it is the smallest or the largest treatment value that is missing from the returned predictions.

For example, fitting a binary treatment (A in {0,1}) using

  moPropen <- buildModelObj(model = ~1,
                            solver.method = 'glm',
                            solver.args = list('family'='binomial'),
                            predict.method = 'predict.glm',
                            predict.args = list(type='response'))

returns only P(A=1). P(A=0) is "missing," and thus

  moPropen <- buildModelObj(model = ~1,
                            solver.method = 'glm',
                            solver.args = list('family'='binomial'),
                            predict.method = 'predict.glm',
                            predict.args = list(type='response',
                                                propen.missing = 'smallest'))

If the dimension of the value returned by the prediction method is less than the number of treatment options and no value is provided in propen.missing, it is assumed that the smallest valued treatment option is missing. Here, 'smallest' indicates the lowest value integer if treatment is an integer, or the 'base' level if treatment is a factor.

Create a BOWL Object

Description

Create a BOWL Object

Usage

.newBOWLStep(
  moPropen,
  fSet,
  data,
  response,
  txName,
  lambdas,
  cvFolds,
  kernel,
  surrogate,
  suppress,
  guess,
  prodPi,
  index,
  ...
)

Arguments

Value

BOWLBasic object

An n-Fold Cross Validation Step

Description

An n-Fold Cross Validation Step

Usage

.newCVStep(cvObject, methodObject, lambda, suppress, ...)

Arguments

Value

The average value across all successfully trained folds

Create Internal Model Objects for Subsets of Data

Description

Create Internal Model Objects for Subsets of Data

Usage

.newModelObjSubset(object)

Arguments

Value

An object of class ModelObj_SubsetList if a single decision point or an object of class ModelObj_DecisionPointList if multiple decision points.

Extract or Estimate the Optimal Tx and Decision Functions

Description

If newdata is provided, the results of the statistical method are used to estimate the decision functions and/or optimal tx. If newdata is missing, the estimated decision functions and/or optimal tx obtained for the original training data are returned.

Usage

optTx(x, newdata, ...)

## S4 method for signature 'IQLearnFS,data.frame'
optTx(x, newdata, ..., y = NULL, z = NULL, dens = NULL)

## S4 method for signature 'IQLearnFS,missing'
optTx(x, newdata, ..., y = NULL, z = NULL, dens = NULL)

Arguments

Details

Methods are defined for all statistical methods implemented in DynTxRegime.

Extract Optimization Results

Description

Retrieves the value object returned by the optimization method for weighted learning methods.

Usage

optimObj(object, ...)

## S4 method for signature 'OWL'
optimObj(object, ...)

## S4 method for signature 'RWL'
optimObj(object, ...)

## S4 method for signature 'BOWL'
optimObj(object, ...)

## S4 method for signature 'EARL'
optimObj(object, ...)

Arguments

Classification Perspective

Description

Classification Perspective

Usage

optimalClass(
  ...,
  moPropen,
  moMain,
  moCont,
  moClass,
  data,
  response,
  txName,
  iter = 0L,
  fSet = NULL,
  verbose = TRUE
)

Arguments

Value

an object of class OptimalClass

References

Baqun Zhang, Anastasios A. Tsiatis, Marie Davidian, Min Zhang and Eric B. Laber. "Estimating optimal tx regimes from a classification perspective." Stat 2012; 1: 103-114.

Note that this method is a single decision point, binary treatment method. For multiple decision points, can be called repeatedly.

See Also

Other statistical methods: bowl(), earl(), iqLearn, optimalSeq(), owl(), qLearn(), rwl()

Other single decision point methods: earl(), optimalSeq(), owl(), qLearn(), rwl()

Other multiple decision point methods: bowl(), iqLearn, optimalSeq(), qLearn()

Examples

# Load and process data set
data(bmiData)

# define the negative 12 month change in BMI from baseline
y12 <- -100*(bmiData[,6L] - bmiData[,4L])/bmiData[,4L]

# Define the propensity for treatment model and methods.
moPropen <- buildModelObj(model =  ~ 1, 
                          solver.method = 'glm', 
                          solver.args = list('family'='binomial'),
                          predict.method = 'predict.glm',
                          predict.args = list(type='response'))

# classification model
library(rpart)
moClass <- buildModelObj(model = ~parentBMI+month4BMI+race+gender,
                         solver.method = 'rpart',
                         solver.args = list(method="class"),
                         predict.args = list(type='class'))

#### Second-Stage Analysis using IPW
fitSS_IPW <- optimalClass(moPropen = moPropen, 
                          moClass = moClass,
                          data = bmiData, response = y12,  txName = 'A2')

# outcome model
moMain <- buildModelObj(model = ~parentBMI+month4BMI,
                        solver.method = 'lm')

moCont <- buildModelObj(model = ~race + parentBMI+month4BMI,
                        solver.method = 'lm')

#### Second-Stage Analysis using AIPW
fitSS_AIPW <- optimalClass(moPropen = moPropen, 
                           moMain = moMain, moCont = moCont,
                           moClass = moClass,
                           data = bmiData, response = y12,  txName = 'A2')

##Available methods

  # Retrieve the classification regression object
  classif(object = fitSS_AIPW)

  # Coefficients of the outcome regression objects
  coef(object = fitSS_AIPW)

  # Description of method used to obtain object
  DTRstep(object = fitSS_AIPW)

  # Estimated value of the optimal treatment regime for training set
  estimator(x = fitSS_AIPW)

  # Value object returned by outcome regression method
  fitObject(object = fitSS_AIPW)

  # Estimated optimal treatment and decision functions for training data
  optTx(x = fitSS_AIPW)

  # Estimated optimal treatment and decision functions for new data
  optTx(x = fitSS_AIPW, newdata = bmiData)

  # Value object returned by outcome regression method
  outcome(object = fitSS_AIPW)
  outcome(object = fitSS_IPW)

  # Plots if defined by outcome regression method
  dev.new()
  par(mfrow = c(2,4))

  plot(x = fitSS_AIPW)
  plot(x = fitSS_AIPW, suppress = TRUE)

  # Retrieve the value object returned by propensity regression method
  propen(object = fitSS_AIPW)

  # Show main results of method
  show(object = fitSS_AIPW)

  # Show summary results of method
  summary(object = fitSS_AIPW)
 
#### First-stage Analysis using AIPW

 # Define the propensity for treatment model and methods.
 moPropen <- buildModelObj(model =  ~ 1, 
                           solver.method = 'glm', 
                           solver.args = list('family'='binomial'),
                           predict.method = 'predict.glm',
                           predict.args = list(type='response'))

# classification model
moClass <- buildModelObj(model = ~parentBMI+baselineBMI+race+gender,
                         solver.method = 'rpart',
                         solver.args = list(method="class"),
                         predict.args = list(type='class'))

# outcome model
moMain <- buildModelObj(model = ~parentBMI+baselineBMI,
                        solver.method = 'lm')

moCont <- buildModelObj(model = ~race + parentBMI+baselineBMI,
                        solver.method = 'lm')

fitFS_AIPW <- optimalClass(moPropen = moPropen, 
                           moMain = moMain, moCont = moCont,
                           moClass = moClass,
                           data = bmiData, response = fitSS_AIPW,  
                           txName = 'A1')

##Available methods for fitFS_AIPW are as shown above for fitSS_AIPW

Missing or Coarsened Data Perspective - Genetic Algorithm

Description

Missing or Coarsened Data Perspective - Genetic Algorithm

Usage

optimalSeq(
  ...,
  moPropen,
  moMain,
  moCont,
  data,
  response,
  txName,
  regimes,
  fSet = NULL,
  refit = FALSE,
  iter = 0L,
  verbose = TRUE
)

Arguments

Value

An object inheriting from class OptimalSeq

References

Baqun Zhang, Anastasios A. Tsiatis, Eric B. Laber & Marie Davidian, "A Robust Method for Estimating Optimal Treatment Regimes", Biometrics, 68, 1010-1018.

Baqun Zhang, Anastasios A. Tsiatis, Eric B. Laber & Marie Davidian, "Robust estimation of optimal treatment regimes for sequential treatment decisions", Biometrika (2013) pp.1-14.

See Also

Other statistical methods: bowl(), earl(), iqLearn, optimalClass(), owl(), qLearn(), rwl()

Other single decision point methods: earl(), optimalClass(), owl(), qLearn(), rwl()

Other multiple decision point methods: bowl(), iqLearn, optimalClass(), qLearn()

Examples

# Load and process data set
data(bmiData)

# define the negative 12 month change in BMI from baseline
y12 <- -100*(bmiData[,6L] - bmiData[,4L])/bmiData[,4L]

# Define the propensity for treatment model and methods.
# Will use constant model for both decision points
moPropen <- buildModelObj(model =  ~ 1, 
                          solver.method = 'glm', 
                          solver.args = list('family'='binomial'),
                          predict.method = 'predict.glm',
                          predict.args = list(type='response'))
moPropen <- list(moPropen, moPropen)

# outcome model second stage
moMain2 <- buildModelObj(model = ~parentBMI+month4BMI,
                         solver.method = 'lm')

moCont2 <- buildModelObj(model = ~race + parentBMI+month4BMI,
                         solver.method = 'lm')

# outcome model first stage
moMain1 <- buildModelObj(model = ~parentBMI+baselineBMI,
                         solver.method = 'lm')

moCont1 <- buildModelObj(model = ~race + parentBMI+baselineBMI,
                         solver.method = 'lm')

moMain <- list(moMain1, moMain2)
moCont <- list(moCont1, moCont2)

# regime function second stage
regime2 <- function(eta1, eta2, data) {
             tst <- {data$parentBMI > eta1} & {data$month4BMI > eta2}
             rec <- rep('MR', nrow(x = data))
             rec[!tst] <- 'CD'
             return( rec )
           }

# regime function first stage
regime1 <- function(eta1, eta2, data) {
             tst <- {data$parentBMI > eta1} & {data$baselineBMI > eta2}
             rec <- rep('MR', nrow(x = data))
             rec[!tst] <- 'CD'
             return( rec )
           }

regimes <- list(regime1, regime2)

#### Analysis using AIPW
## Not run: 
fit_AIPW <- optimalSeq(moPropen = moPropen, 
                       moMain = moMain, moCont = moCont,
                       regimes = regimes,
                       data = bmiData, response = y12,  txName = c('A1','A2'),
                       Domains = cbind(rep(0,4),rep(100,4)),
                       pop.size = 100, starting.values = rep(25,4))

##Available methods

  # Coefficients of the regression objects
  coef(object = fit_AIPW)

  # Description of method used to obtain object
  DTRstep(object = fit_AIPW)

  # Estimated value of the optimal treatment regime for training set
  estimator(x = fit_AIPW)

  # Value object returned by regression methods
  fitObject(object = fit_AIPW)

  # Retrieve the results of genetic algorithm
  genetic(object = fit_AIPW)

  # Estimated optimal treatment and decision functions for training data
  optTx(x = fit_AIPW)

  # Estimated optimal treatment and decision functions for new data
  optTx(x = fit_AIPW, newdata = bmiData)

  # Value object returned by outcome regression method
  outcome(object = fit_AIPW)

  # Plots if defined by regression methods
  dev.new()
  par(mfrow = c(2,4))

  plot(x = fit_AIPW)
  plot(x = fit_AIPW, suppress = TRUE)

  # Retrieve the value object returned by propensity regression method
  propen(object = fit_AIPW)

  # Show main results of method
  show(object = fit_AIPW)

  # Show summary results of method
  summary(object = fit_AIPW)

## End(Not run)
#### Single Decision Point Analysis using IPW

# Define the propensity for treatment model and methods.
moPropen <- buildModelObj(model =  ~ 1, 
                          solver.method = 'glm', 
                          solver.args = list('family'='binomial'),
                          predict.method = 'predict.glm',
                          predict.args = list(type='response'))

# regime function second stage
regime <- function(eta1, eta2, data) {
            tst <- {data$parentBMI > eta1} & {data$month4BMI > eta2}
            rec <- rep('MR', nrow(x = data))
            rec[!tst] <- 'CD'
            return( rec )
          }
## Not run: 
fit_IPW <- optimalSeq(moPropen = moPropen, 
                      regimes = regime,
                      data = bmiData, response = y12,  txName = 'A2',
                      Domains = cbind(rep(0,2),rep(100,2)),
                      pop.size = 100, starting.values = rep(25,2))

##Available methods

  # Coefficients of the regression objects
  coef(object = fit_IPW)

  # Description of method used to obtain object
  DTRstep(object = fit_IPW)

  # Estimated value of the optimal treatment regime for training set
  estimator(x = fit_IPW)

  # Value object returned by regression method
  fitObject(object = fit_IPW)

  # Retrieve the results of genetic algorithm
  genetic(object = fit_IPW)

  # Estimated optimal treatment and decision functions for training data
  optTx(x = fit_IPW)

  # Estimated optimal treatment and decision functions for new data
  optTx(x = fit_IPW, newdata = bmiData)

  # Value object returned by outcome regression method
  outcome(object = fit_IPW)

  # Plots if defined by outcome regression method
  dev.new()
  par(mfrow = c(2,4))

  plot(x = fit_IPW)
  plot(x = fit_IPW, suppress = TRUE)

  # Retrieve the value object returned by propensity regression method
  propen(object = fit_IPW)

  # Show main results of method
  show(object = fit_IPW)

  # Show summary results of method
  summary(object = fit_IPW)

## End(Not run)

Retrieve Outcome Regression Analysis

Description

For statistical methods that require an outcome regression analysis, the value object returned by the modeling function(s) is retrieved.

Usage

outcome(object, ...)

Arguments

Details

Methods are defined for all statistical methods implemented in DynTxRegime that use outcome regression.

Outcome Weighted Learning

Description

Outcome Weighted Learning

Usage

owl(
  ...,
  moPropen,
  data,
  reward,
  txName,
  regime,
  response,
  lambdas = 2,
  cvFolds = 0L,
  kernel = "linear",
  kparam = NULL,
  surrogate = "hinge",
  verbose = 2L
)

Arguments

Value

an OWL object

References

Yingqi Zhao, Donglin Zeng, A. John Rush, Michael R. Kosorok (2012) Estimated individualized treatment rules using outcome weighted learning. Journal of the American Statistical Association, 107(409): 1106-1118. PMCID: 3636816

See Also

Other statistical methods: bowl(), earl(), iqLearn, optimalClass(), optimalSeq(), qLearn(), rwl()

Other weighted learning methods: bowl(), earl(), rwl()

Other single decision point methods: earl(), optimalClass(), optimalSeq(), qLearn(), rwl()

Examples

 
# Load and process data set
data(bmiData)

# define the negative 12 month change in BMI from baseline
y12 <- -100*(bmiData[,6L] - bmiData[,4L])/bmiData[,4L]

# propensity model
moPropen <- buildModelObj(model = ~parentBMI+month4BMI,
                          solver.method = 'glm',
                          solver.args = list('family'='binomial'),
                          predict.method = 'predict.glm',
                          predict.args = list(type='response'))

fitOWL <- owl(moPropen = moPropen,
              data = bmiData, reward = y12,  txName = 'A2', 
              regime = ~ parentBMI + month4BMI,
              surrogate = 'hinge', kernel = 'linear', kparam = NULL)

##Available methods

  # Coefficients of the propensity score regression
  coef(fitOWL)

  # Description of method used to obtain object
  DTRstep(fitOWL)

  # Estimated value of the optimal treatment regime for training set
  estimator(fitOWL)

  # Value object returned by propensity score regression method
  fitObject(fitOWL)

  # Summary of optimization routine
  optimObj(fitOWL)

  # Estimated optimal treatment for training data
  optTx(fitOWL)

  # Estimated optimal treatment for new data
  optTx(fitOWL, bmiData)

  # Plots if defined by propensity regression method
  dev.new()
  par(mfrow = c(2,4))

  plot(fitOWL)
  plot(fitOWL, suppress = TRUE)

  # Value object returned by propensity score regression method
  propen(fitOWL)

  # Parameter estimates for decision function
  regimeCoef(fitOWL)

  # Show main results of method
  show(fitOWL)

  # Show summary results of method
  summary(fitOWL)
 

Generates Plots as Defined by Modeling Functions

Description

Calls plot() method for all regression steps of a statistical method

Arguments

Details

Methods are defined for all statistical methods implemented in DynTxRegime.

Retrieve Propensity Regression Analysis

Description

For statistical methods that require a propensity regression analysis, the value object returned by the modeling function(s) is retrieved.

Usage

propen(object, ...)

Arguments

Details

Methods are defined for all statistical methods implemented in DynTxRegime that use propensity regression.

A Step of the Q-Learning Algorithm

Description

Performs a single step of the Q-Learning algorithm. If an object of class QLearn is passed through input response, it is assumed that the QLearn object is the value object returned from the preceding step of the Q-Learning algorithm, and the value fit by the regression is taken from the QLearn object. If a vector is passed through input response, it is assumed that the call if for the first step in the Q-Learning algorithm, and models are fit using the provided response.

Usage

qLearn(
  ...,
  moMain,
  moCont,
  data,
  response,
  txName,
  fSet = NULL,
  iter = 0L,
  verbose = TRUE
)

Arguments

Value

An object of class QLearn-class

See Also

Other statistical methods: bowl(), earl(), iqLearn, optimalClass(), optimalSeq(), owl(), rwl()

Other multiple decision point methods: bowl(), iqLearn, optimalClass(), optimalSeq()

Other single decision point methods: earl(), optimalClass(), optimalSeq(), owl(), rwl()

Examples

# Load and process data set
data(bmiData)

# define the negative 12 month change in BMI from baseline
y12 <- -100*(bmiData[,6L] - bmiData[,4L])/bmiData[,4L]

# outcome model
moMain <- buildModelObj(model = ~parentBMI+month4BMI,
                        solver.method = 'lm')

moCont <- buildModelObj(model = ~race + parentBMI+month4BMI,
                        solver.method = 'lm')

#### Second-Stage Analysis
fitSS <- qLearn(moMain = moMain, moCont = moCont,
                data = bmiData, response = y12,  txName = 'A2')

##Available methods

  # Coefficients of the outcome regression objects
  coef(fitSS)

  # Description of method used to obtain object
  DTRstep(fitSS)

  # Estimated value of the optimal treatment regime for training set
  estimator(fitSS)

  # Value object returned by outcome regression method
  fitObject(fitSS)

  # Estimated optimal treatment and decision functions for training data
  optTx(fitSS)

  # Estimated optimal treatment and decision functions for new data
  optTx(fitSS, bmiData)

  # Value object returned by outcome regression method
  outcome(fitSS)

  # Plots if defined by outcome regression method
  dev.new()
  par(mfrow = c(2,4))

  plot(fitSS)
  plot(fitSS, suppress = TRUE)

  # Show main results of method
  show(fitSS)

  # Show summary results of method
  summary(fitSS)
 
#### First-stage Analysis

# outcome model
moMain <- buildModelObj(model = ~parentBMI+baselineBMI,
                        solver.method = 'lm')

moCont <- buildModelObj(model = ~race + parentBMI+baselineBMI,
                        solver.method = 'lm')

fitFS <- qLearn(moMain = moMain, moCont = moCont,
                data = bmiData, response = fitSS,  txName = 'A1')

##Available methods for fitFS are as shown above for fitSS

Extract Regime Parameters

Description

Extract the estimated regime parameters.

Usage

regimeCoef(object, ...)

Arguments

Details

Methods are defined for all statistical methods implemented in DynTxRegime that use a non-regression based regime. Specifically, OptimalSeq, OWL, BOWL, RWL, and EARL.

Extract Model Residuals

Description

Retrieve residuals from an interactive Q-Learning step.

Usage

residuals(object, ...)

## S4 method for signature 'IQLearnFS_C'
residuals(object, ...)

## S4 method for signature 'IQLearnFS_VHet'
residuals(object, ...)

Arguments

Residual Weighted Learning

Description

Residual Weighted Learning

Usage

rwl(
  ...,
  moPropen,
  moMain,
  data,
  reward,
  txName,
  regime,
  response,
  fSet = NULL,
  lambdas = 2,
  cvFolds = 0L,
  kernel = "linear",
  kparam = NULL,
  responseType = "continuous",
  verbose = 2L
)

Arguments

Value

an RWL object

References

Xin Zhou, Nicole Mayer-Hamblett, Umer Khan, and Michael R Kosorok (2017) Residual weighted learning for estimating individualized treatment rules. Journal of the American Statistical Association, 112, 169–187.

See Also

Other statistical methods: bowl(), earl(), iqLearn, optimalClass(), optimalSeq(), owl(), qLearn()

Other weighted learning methods: bowl(), earl(), owl()

Other single decision point methods: earl(), optimalClass(), optimalSeq(), owl(), qLearn()

Examples

## Not run:  
# Load and process data set
data(bmiData)

# define the negative 12 month change in BMI from baseline
y12 <- -100*(bmiData[,6L] - bmiData[,4L])/bmiData[,4L]

# propensity model
moPropen <- buildModelObj(model = ~parentBMI+month4BMI,
                          solver.method = 'glm',
                          solver.args = list('family'='binomial'),
                          predict.method = 'predict.glm',
                          predict.args = list(type='response'))

# outcome model
moMain <- buildModelObj(model = ~parentBMI+month4BMI,
                        solver.method = 'lm')

fitRWL <- rwl(moPropen = moPropen, moMain = moMain,
              data = bmiData, reward = y12,  txName = 'A2', 
              regime = ~ parentBMI + month4BMI,
              kernel = 'radial', kparam = 1.5)

##Available methods

  # Coefficients of the regression objects
  coef(fitRWL)

  # Description of method used to obtain object
  DTRstep(fitRWL)

  # Estimated value of the optimal treatment regime for training set
  estimator(fitRWL)

  # Value object returned by regression methods
  fitObject(fitRWL)

  # Summary of optimization routine
  optimObj(fitRWL)

  # Estimated optimal treatment for training data
  optTx(fitRWL)

  # Estimated optimal treatment for new data
  optTx(fitRWL, bmiData)

  # Value object returned by outcome regression method
  outcome(fitRWL)

  # Plots if defined by regression methods
  dev.new()
  par(mfrow = c(2,4))

  plot(fitRWL)
  plot(fitRWL, suppress = TRUE)

  # Value object returned by propensity score regression method
  propen(fitRWL)

  # Parameter estimates for decision function
  regimeCoef(fitRWL)

  # Show main results of method
  show(fitRWL)

  # Show summary results of method
  summary(fitRWL)
 
## End(Not run)

Standard Deviation

Description

Retrieve the standard deviation of the residuals for the first-stage contrasts regression in the interactive Q-Learning algorithm.

Usage

sd(x, na.rm=FALSE)

  ## S4 method for IQLearnFS_C
  sd(x, na.rm=FALSE)

Arguments

Result Summaries

Description

Returns a list of the primary results, including regression results, optimization results, estimated tx and value, etc.

Usage

summary(object, ...)

Arguments

Details

Methods are defined for all statistical methods implemented in DynTxRegime.

The exact structure of the returned list will vary depending on the statistical method.