83 lines
2.3 KiB
R
83 lines
2.3 KiB
R
% Generated by roxygen2: 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{VT.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. See
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\link{formatRCTDataset} for more details.
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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{screening}}{Logical, set to \code{FALSE} Set to \code{TRUE} to use
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\code{varimp} in trees 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{getData(interactions = F)}}{Return dataset. If interactions is set to T, return data with treatement interactions}
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\item{\code{getFormula()}}{Return formula : Y~T+X1+...+Xp. Usefull for cforest function.}
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\item{\code{getIncidences(rule = NULL)}}{Return incidence table of data if rule set to NULL. Otherwise return incidence for the rule.}
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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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\dontrun{
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# Default use :
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vt.o <- VT.object$new(data = my.rct.dataset)
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# Getting data
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head(vt.o$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{\link{VT.difft}}
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}
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