newLearning function

Complete a Learning Analysis

Performs a weighted learning analysis.

.newLearning(fSet, kernel, ...)

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

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

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

Arguments

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

Returns

A Learning object