DynTxRegime (version 4.15)

.newLearning: Complete a Learning Analysis

Description

Performs a weighted learning analysis.

Usage

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

Value

A Learning object

Arguments

fSet

NULL or function defining subset rules

kernel

Kernel object or SubsetList

...

Additional inputs for optimization

moPropen

modelObj for propensity model

moMain

modelObj for main effects of outcome model

moCont

modelObj for contrasts of outcome model

data

data.frame of covariates

response

Vector of responses

txName

Tx variable column header in data

lambdas

Tuning parameter(s)

cvFolds

Number of cross-validation folds

iter

Maximum number of iterations for outcome regression

surrogate

Surrogate object

suppress

T/F indicating if prints to screen are executed

guess

optional numeric vector providing starting values for optimization methods

createObj

A function name defining the method object for a specific learning algorithm

prodPi

A vector of propensity weights

index

The subset of individuals to be included in learning