## ---- eval = F-----------------------------------------------------------
#  vt.data <- function(dataset, outcome.field, treatment.field, interactions = TRUE, ...){
#    data <- formatRCTDataset(dataset, outcome.field, treatment.field, interactions = TRUE)
#    VT.object(data = data, ...)
#  }

## ------------------------------------------------------------------------
# load library VT
library(aVirtualTwins)
# load data sepsis
data(sepsis)
# initialize VT.object
vt.o <- vt.data(sepsis, "survival", "THERAPY", TRUE)

## ------------------------------------------------------------------------
# Creation of categorical variable
cat.x <- rep(1:5, (nrow(sepsis))/5)
cat.x <- as.factor(cat.x)
sepsis.tmp <- cbind(sepsis, cat.x)
vt.o.tmp <- vt.data(sepsis.tmp, "survival", "THERAPY", TRUE)

## ---- echo = FALSE-------------------------------------------------------
rm(vt.o.tmp, cat.x, sepsis.tmp)

## ------------------------------------------------------------------------
# use randomForest::randomForest()
library(randomForest, verbose = F)
# Reproducibility
set.seed(123)
# Fit rf model 
# default params
# set interactions to TRUE if using interaction between T and X
model.rf <- randomForest(x = vt.o$getX(interactions = T),
                         y = vt.o$getY())
# initialize VT.forest.one
vt.f.rf <- VT.forest.one(vt.o, model.rf)
# Then, use run() to compute probabilities
vt.f.rf$run()

## ------------------------------------------------------------------------
# # use randomForest::randomForest()
# library(party, verbose = F)
# # Reproducibility
# set.seed(123)
# # Fit cforest model 
# # default params
# # set interactions to TRUE if using interaction between T and X
# model.cf <- cforest(formula = vt.o$getFormula(), data = vt.o$getData(interactions = T))
# # initialize VT.forest.one
# vt.f.cf <- VT.forest.one(vt.o, model.cf)
# # Then, use run() to compute probabilities
# vt.f.cf$run()

## ------------------------------------------------------------------------
# Copy new object
vt.o.tr <- vt.o$copy()
# Change levels
tmp <- ifelse(vt.o.tr$data$survival == 1, "y", "n")
vt.o.tr$data$survival <- as.factor(tmp)
rm(tmp)
# Check new data to be sure
formatRCTDataset(vt.o.tr$data, "survival", "THERAPY")
# use caret::train()
library(caret, verbose = F)
# Reproducibility
set.seed(123)
# fit train model
fitControl <- trainControl(classProbs = T, method = "none")
model.tr <- train(x = vt.o.tr$getX(interactions = T),
                  y = vt.o.tr$getY(),
                  method = "rf",
                  tuneGrid = data.frame(mtry = 5),
                  trControl = fitControl)
# initialize VT.forest.one
vt.f.tr <- VT.forest.one(vt.o.tr, model.tr)
# Then, use run() to compute probabilities
vt.f.tr$run()

## ------------------------------------------------------------------------
# grow RF for T = 1
model.rf.trt1 <- randomForest(x = vt.o$getX(trt = 1),
                              y = vt.o$getY(trt = 1))
# grow RF for T = 0
model.rf.trt0 <- randomForest(x = vt.o$getX(trt = 0),
                              y = vt.o$getY(trt = 0))
# initialize VT.forest.double()
vt.doublef.rf <- VT.forest.double(vt.o, model.rf.trt1, model.rf.trt0)
# Then, use run() to compute probabilities
vt.doublef.rf$run()

## ---- cache=F------------------------------------------------------------

# initialize k-fold RF
model.fold <- aVirtualTwins:::VT.forest.fold(vt.o, fold = 5, ratio = 1, interactions = T)
# grow RF with randomForest package options
# set do.trace option to see the 5 folds
model.fold$run(ntree = 500, do.trace = 500)

## ------------------------------------------------------------------------
# you get twin1 and twin2 by your own method
# here, i'll use random number between 0 and 1 :
twin1_random <- runif(470)
twin2_random <- runif(470)

# then you can initialize VT.difft class : 
model.difft <- VT.difft(vt.o, twin1 = twin1_random, twin2 = twin2_random, "absolute")
# compute difference of twins : 
model.difft$computeDifft()
# See results
head(model.difft$difft)
# Graph :
# hist(model.difft$difft)

## ------------------------------------------------------------------------
# initialize classification tree
tr.class <- VT.tree.class(vt.f.rf, sens = ">", threshold = quantile(vt.f.rf$difft, 0.7))
# compute tree with rpart option
tr.class$run(cp = 0, maxdepth = 3, maxcompete = 1)

## ------------------------------------------------------------------------
# initialize regression tree
tr.reg <- VT.tree.reg(vt.f.rf, sens = ">", threshold = quantile(vt.f.rf$difft, 0.7))
# compute tree with rpart option
tr.reg$run(cp = 0, maxdepth = 3, maxcompete = 1)