control_earl: Control arguments for Efficient Augmentation and Relaxation...

In polle: Policy Learning

Control arguments for Efficient Augmentation and Relaxation Learning

Description

control_earl sets the default control arguments for efficient augmentation and relaxation learning , type = "earl". The arguments are passed directly to DynTxRegime::earl() if not specified otherwise.

Usage

control_earl(
  moPropen,
  moMain,
  moCont,
  regime,
  iter = 0L,
  fSet = NULL,
  lambdas = 0.5,
  cvFolds = 0L,
  surrogate = "hinge",
  kernel = "linear",
  kparam = NULL,
  verbose = 0L
)

Arguments

moPropen	Propensity model of class "ModelObj", see modelObj::modelObj.
moMain	Main effects outcome model of class "ModelObj".
moCont	Contrast outcome model of class "ModelObj".
regime	An object of class formula specifying the design of the policy/regime.
iter	Maximum number of iterations for outcome regression.
fSet	A function or NULL defining subset structure.
lambdas	Numeric or numeric vector. Penalty parameter.
cvFolds	Integer. Number of folds for cross-validation of the parameters.
surrogate	The surrogate 0-1 loss function. The options are "logit", "exp", "hinge", "sqhinge", "huber".
kernel	The options are "linear", "poly", "radial".
kparam	Numeric. Kernel parameter
verbose	Integer.

Value

list of (default) control arguments.

polle documentation built on May 29, 2024, 1:15 a.m.