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73 lines
2.4 KiB
R
73 lines
2.4 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/forest.wrapper.R
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\name{vt.forest}
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\alias{vt.forest}
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\title{Create forest to compute difft}
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\usage{
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vt.forest(forest.type = "one", vt.data, interactions = T,
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method = "absolute", model = NULL, model_trt1 = NULL,
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model_trt0 = NULL, ratio = 1, fold = 10, ...)
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}
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\arguments{
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\item{forest.type}{must be a character. "one" to use VT.forest.one class.
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"double" to use VT.forest.double. "fold" to use VT.forest.fold.}
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\item{vt.data}{\code{\link{VT.object}}. Can be return of \code{vt.data()}
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function}
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\item{interactions}{logical. If running VirtualTwins with treatment's
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interactions, set to TRUE (default value)}
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\item{method}{character c("absolute", "relative", "logit"). See
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\code{\link{VT.difft}}.}
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\item{model}{allows to give a model you build outside this function. Can be
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randomForest, train or cforest. Is only used with forest.type = "one". If
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NULL, a randomForest model is grown inside the function. NULL is default.}
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\item{model_trt1}{see model_trt0 explanation and
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\code{\link{VT.forest.double}} details.}
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\item{model_trt0}{works the same as model parameter. Is only used with
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forest.type = "double". If NULL, a randomForest model is grown inside the
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function. NULL is default. See \code{\link{VT.forest.double}} for details.}
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\item{ratio}{numeric value that allow sampsize to be a bit controlled.
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Default to 1. See \code{\link{VT.forest.fold}}.}
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\item{fold}{number of fold you want to construct forest with k-fold method.
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Is only used with forest.type = "fold". Default to 5. See
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\code{\link{VT.forest.fold}}}
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\item{...}{randomForest() function parameters. Can be used for any forest.type.}
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}
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\value{
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\code{VT.difft}
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}
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\description{
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\code{vt.forest} is a wrapper of \code{\link{VT.forest.one}},
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\code{\link{VT.forest.double}} and \code{\link{VT.forest.fold}}. With
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parameter forest.type, any of these class can be used with its own parameter.
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}
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\examples{
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data(sepsis)
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vt.o <- vt.data(sepsis, "survival", "THERAPY", T)
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# inside model :
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vt.f <- vt.forest("one", vt.o)
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# ...
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# your model :
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# library(randomForest)
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# rf <- randomForest(y = vt.o$getY(),
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# x = vt.o$getX(int = T),
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# mtry = 3,
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# nodesize = 15)
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# vt.f <- vt.forest("one", vt.o, model = rf)
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# ...
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# Can also use ... parameters
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vt.f <- vt.forest("one", vt.o, mtry = 3, nodesize = 15)
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# ...
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}
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