63 lines
2.0 KiB
R
63 lines
2.0 KiB
R
# VT.FOREST.ONE -----------------------------------------------------------
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# IF RUNNING ONE FOREST COMPUTATION
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#' Difft by one random forest
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#'
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#' A reference class to compute twins via one random forest
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#'
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#' \code{VT.forest.one} extends \code{VT.forest}.
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#'
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#' OOB predictions are used to estimate \eqn{E(Y|T = real treatment)}. Then,
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#' treatement is switched, it means that 1 becomes 0 and 0 becomes 1. We use
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#' again \code{model} to estimate \eqn{E(Y|T = the other treatment)}. This is
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#' what \code{computeTwin1()} and \code{computeTwin2()} functions do.
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#'
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#' @include forest.R
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#'
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#' @field model is a caret/RandomForest/randomForest class object
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#' @field interactions logical set TRUE if model has been computed with interactions
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#' @field ... field from parent class : \code{\link{VT.forest}}
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#'
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#' @seealso \code{\link{VT.difft}}, \code{\link{VT.forest}}, \code{\link{VT.forest.double}}
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#'
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#' @name VT.forest.one
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#'
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#' @export VT.forest.one
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#'
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#' @import methods
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VT.forest.one <- setRefClass(
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Class = "VT.forest.one",
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contains = "VT.forest",
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fields = list(
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model = "ANY",
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interactions = "logical"
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),
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methods = list(
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initialize = function(vt.object, model, interactions = T, ...){
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.self$checkModel(model)
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.self$model <- model
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.self$interactions <- interactions
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callSuper(vt.object, ...)
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},
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computeTwin1 = function(){
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"Compute twin1 with OOB predictions"
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.self$twin1 <- as.vector(VT.predict(rfor = .self$model, type = .self$vt.object$type))
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return(invisible(.self$twin1))
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},
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computeTwin2 = function(){
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"Compute twin2 by switching treatment and applying random forest model"
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.self$twin2 <- as.vector(VT.predict(.self$model,
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newdata = .self$vt.object$getX(interactions = .self$interactions),
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.self$vt.object$type))
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return(invisible(.self$twin2))
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}
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)
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) |