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# December 3, 2018
#' Class \code{LearningObject}
#'
#' Class \code{LearningObject} contains stores parameters required for
#' weighted learning optimization step
#'
#' @name LearningObject-class
#' @slot mu Matrix of outcome regression
#' @slot txVec Vector of treatment coded as -1/1
#' @slot invPi Vector of inverse propensity for treatment received
#' @slot response Vector of the response
#'
#' @keywords internal
setClass(Class = 'LearningObject',
slots = c(mu = "matrix",
txVec = "vector",
invPi = "vector",
response = "vector"),
contains = c("MethodObject"))
##########
## METHODS
##########
#' Methods Available for Objects of Class \code{LearningObject}
#'
#' @name LearningObject-methods
#'
#' @keywords internal
NULL
#' @rdname LearningObject-methods
setMethod(f = ".subsetObject",
signature = c(methodObject = "LearningObject"),
definition = function(methodObject, subset) {
newMO <- .subsetObject(methodObject = as(object = methodObject,
Class = "MethodObject"),
subset = subset)
return( new("LearningObject",
"mu" = methodObject@mu[subset,,drop=FALSE],
"txVec" = methodObject@txVec[subset],
"invPi" = methodObject@invPi[subset],
"response" = methodObject@response[subset],
newMO) )
})
#' @rdname LearningObject-methods
setMethod(f = ".objFn",
signature = c("par" = "numeric",
"methodObject" = "LearningObject",
"kernel" = "Kernel"),
definition = function(par,
methodObject,
kernel,
lambda, ...) { stop("not allowed") })
#' @rdname LearningObject-methods
setMethod(f = ".dobjFn",
signature = c("par" = "numeric",
"methodObj" = "LearningObject",
"kernel" = "Kernel"),
definition = function(par,
methodObject,
kernel,
lambda, ...) { stop("not allowed") })
#' @rdname LearningObject-methods
setMethod(f = ".valueFunc",
signature = c("methodObject" = "LearningObject"),
definition = function(methodObject, optTx, ...) { stop("not allowed") })
#' Create LearningObject
#'
#' @param kernel Kernel object
#' @param surrogate Surrogate object indicating surrogate loss-function
#' @param mu Matrix of predicted outcome on binary tx coding
#' @param txVec Tx vector coded as -1/1
#' @param prWgt propensity wgt for tx received
#' @param response vector of response
#' @param guess Vector of estimated regime parameters
#'
#' @return A LearningObject object
#'
#' @rdname LearningObject-methods
#'
#' @keywords internal
.createLearningObject <- function(kernel,
surrogate,
mu,
txVec,
response,
prWgt,
guess = NULL, ...) {
newMO <- .createMethodObject(kernel = kernel,
surrogate = surrogate,
guess = guess)
return( new("LearningObject",
"mu" = mu,
"txVec" = txVec,
"invPi" = 1.0/prWgt,
"response" = response,
newMO) )
}
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