newRWL-methods function

Complete a Residual Weighted Learning Analysis

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

Arguments

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

Returns

An RWL object