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Documentation of VT.object
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R/object.R
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R/object.R
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# VT.OBJECT ---------------------------------------------------------------
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#' VirtualTwins.object
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#'
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#' A Reference Class to deal with RCT dataset
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#'
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#' @field data A data.frame de la forme \eqn{Y,T,X_{1}, \ldots, X_{p}}. Y must
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#' be two levels factor if type is binary. T must be numeric or integer.
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#' @field alpha no usefull now, set to 1
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#' @field screening logical, set to FALSE. Se TRUE to use varimp in trees
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#' computation
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#' @field varimp character vector of important variables to use in trees
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#' computation
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#' @field delta numeric representing the difference of incidence between
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#' treatments
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#' @field type character : binary or continous. Only binary is possible.
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#' Currently working with binary response only. Continous will come, one day.
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#' Two-levels treatment only as well.
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#'
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#' \code{data} field should be as described, however if virtual twins won't used
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#' interactions, there is no need to transform factors. A tool function to
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#' transform factor will come soon.
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#'
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#'
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#' @field data Data.frame with format: \eqn{Y,T,X_{1}, \ldots, X_{p}}. Y must be
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#' two levels factor if type is binary. T must be numeric or integer.
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#' @field alpha Numeric, no need in this current version. Set to \code{1}.
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#' @field screening Logical, set to \code{FALSE} Set to \code{TRUE} to use \code{varimp} in trees
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#' computation.
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#' @field varimp Character vector of important variables to use in trees
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#' computation.
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#' @field delta Numeric representing the difference of incidence between
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#' treatments.
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#' @field type Character : binary or continous. Only binary is currently
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#' available.
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#'
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#' @import methods
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#'
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#' @name VT.object
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#'
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#' @examples
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#' # Default use :
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#' vt.o <- VT.object$new(data = my.rct.dataset)
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#'
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#' # Getting data
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#' vt.o$data
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#'
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#' # or getting predictor with interactions
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#' vt.o$getX(interactions = T)
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#'
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#' # or getting X|T = 1
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#' vt.o$getX(trt = 1)
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#'
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#' # or getting Y|T = 0
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#' vt.o$getY(0)
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#'
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#' # Print incidences
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#' vt.o$getIncidences()
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#' # ...
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#'
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#' @seealso \code{\linkS4class{VT.difft}}
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#'
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VT.object <- setRefClass(
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Class = "VT.object",
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@ -44,8 +80,8 @@ VT.object <- setRefClass(
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},
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getX = function(interactions = T, trt = NULL){
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"Return predictors {T,X,X*T,X*(1-T)}. Or {T,X} if interactions is FALSE.
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If trt is not NULL, return predictors for T=trt"
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"Return predictors (T,X,X*T,X*(1-T)). Or (T,X) if interactions is FALSE.
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If trt is not NULL, return predictors for T = trt"
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# retour les prédicteurs si trt n'est pas null
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if(!is.null(trt)) return(.self$data[.self$data[,2] == trt, -c(1,2)])
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# retourne les predicteurs*traitement peut importe le traitement si interactions est à TRUE
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@ -55,7 +91,7 @@ VT.object <- setRefClass(
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},
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getY = function(trt = NULL){
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"Return outcome. If trt is not NULL, return outcome for T=trt."
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"Return outcome. If trt is not NULL, return outcome for T = trt."
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if(is.null(trt)) return(.self$data[, 1])
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return(.self$data[.self$data[,2] == trt, 1])
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},
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@ -1,48 +0,0 @@
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% Generated by roxygen2 (4.1.1): do not edit by hand
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% Please edit documentation in R/object.R
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\docType{class}
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\name{VT.object-class}
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\alias{VT.object}
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\alias{VT.object-class}
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\title{A Reference Class to deal with RCT dataset}
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\description{
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A Reference Class to deal with RCT dataset
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}
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\section{Fields}{
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\describe{
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\item{\code{data}}{A data.frame de la forme \eqn{Y,T,X_{1}, \ldots, X_{p}}. Y must
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be two levels factor if type is binary. T must be numeric or integer.}
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\item{\code{alpha}}{no usefull now, set to 1}
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\item{\code{screening}}{logical, set to FALSE. Se TRUE to use varimp in trees
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computation}
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\item{\code{varimp}}{character vector of important variables to use in trees
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computation}
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\item{\code{delta}}{numeric representing the difference of incidence between
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treatments}
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\item{\code{type}}{character : binary or continous. Only binary is possible.}
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}}
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\section{Methods}{
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\describe{
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\item{\code{computeDelta()}}{Compute delta value.}
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\item{\code{getFormula()}}{Return formula : Y~T+X1+...+Xp. Usefull for cforest function.}
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\item{\code{getIncidences()}}{Return incidence table of data.}
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\item{\code{getX(interactions = T, trt = NULL)}}{Return predictors {T,X,X*T,X*(1-T)}. Or {T,X} if interactions is FALSE.
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If trt is not NULL, return predictors for T=trt}
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\item{\code{getXwithInt()}}{Return predictors with interactions. Use VT.object::getX(interactions = T) instead.}
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\item{\code{getY(trt = NULL)}}{Return outcome. If trt is not NULL, return outcome for T=trt.}
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\item{\code{switchTreatment()}}{Switch treatment value.}
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}}
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79
man/VT.object.Rd
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79
man/VT.object.Rd
Normal file
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@ -0,0 +1,79 @@
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% Generated by roxygen2 (4.1.1): do not edit by hand
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% Please edit documentation in R/object.R
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\docType{class}
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\name{VT.object}
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\alias{VT.object}
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\title{VirtualTwins.object}
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\description{
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A Reference Class to deal with RCT dataset
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}
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\details{
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Currently working with binary response only. Continous will come, one day.
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Two-levels treatment only as well.
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\code{data} field should be as described, however if virtual twins won't used
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interactions, there is no need to transform factors. A tool function to
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transform factor will come soon.
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}
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\section{Fields}{
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\describe{
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\item{\code{data}}{Data.frame with format: \eqn{Y,T,X_{1}, \ldots, X_{p}}. Y must be
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two levels factor if type is binary. T must be numeric or integer.}
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\item{\code{alpha}}{Numeric, no need in this current version. Set to \code{1}.}
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\item{\code{screening}}{Logical, set to \code{FALSE} Set to \code{TRUE} to use \code{varimp} in trees
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computation.}
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\item{\code{varimp}}{Character vector of important variables to use in trees
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computation.}
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\item{\code{delta}}{Numeric representing the difference of incidence between
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treatments.}
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\item{\code{type}}{Character : binary or continous. Only binary is currently
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available.}
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}}
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\section{Methods}{
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\describe{
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\item{\code{computeDelta()}}{Compute delta value.}
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\item{\code{getFormula()}}{Return formula : Y~T+X1+...+Xp. Usefull for cforest function.}
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\item{\code{getIncidences()}}{Return incidence table of data.}
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\item{\code{getX(interactions = T, trt = NULL)}}{Return predictors (T,X,X*T,X*(1-T)). Or (T,X) if interactions is FALSE.
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If trt is not NULL, return predictors for T = trt}
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\item{\code{getXwithInt()}}{Return predictors with interactions. Use VT.object::getX(interactions = T) instead.}
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\item{\code{getY(trt = NULL)}}{Return outcome. If trt is not NULL, return outcome for T = trt.}
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\item{\code{switchTreatment()}}{Switch treatment value.}
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}}
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\examples{
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# Default use :
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vt.o <- VT.object$new(data = my.rct.dataset)
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# Getting data
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vt.o$data
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# or getting predictor with interactions
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vt.o$getX(interactions = T)
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# or getting X|T = 1
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vt.o$getX(trt = 1)
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# or getting Y|T = 0
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vt.o$getY(0)
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# Print incidences
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vt.o$getIncidences()
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# ...
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}
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\seealso{
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\code{\linkS4class{VT.difft}}
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}
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