mirror of
https://github.com/prise6/aVirtualTwins.git
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125 lines
4.5 KiB
R
125 lines
4.5 KiB
R
## ---- eval = F-----------------------------------------------------------
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# vt.data <- function(dataset, outcome.field, treatment.field, interactions = TRUE, ...){
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# data <- formatRCTDataset(dataset, outcome.field, treatment.field, interactions = TRUE)
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# VT.object(data = data, ...)
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# }
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## ------------------------------------------------------------------------
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# load library VT
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library(VirtualTwins)
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# load data sepsis
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data(sepsis)
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# initialize VT.object
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vt.o <- vt.data(sepsis, "survival", "THERAPY", TRUE)
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## ------------------------------------------------------------------------
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# Creation of categorical variable
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cat.x <- rep(1:5, (nrow(sepsis))/5)
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cat.x <- as.factor(cat.x)
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sepsis.tmp <- cbind(sepsis, cat.x)
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vt.o.tmp <- vt.data(sepsis.tmp, "survival", "THERAPY", TRUE)
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## ---- echo = FALSE-------------------------------------------------------
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rm(vt.o.tmp, cat.x, sepsis.tmp)
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## ------------------------------------------------------------------------
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# use randomForest::randomForest()
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library(randomForest, verbose = F)
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# Reproducibility
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set.seed(123)
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# Fit rf model
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# default params
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# set interactions to TRUE if using interaction between T and X
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model.rf <- randomForest(x = vt.o$getX(interactions = T),
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y = vt.o$getY())
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# initialize VT.forest.one
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vt.f.rf <- VT.forest.one(vt.o, model.rf)
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# Then, use run() to compute probabilities
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vt.f.rf$run()
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## ------------------------------------------------------------------------
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# # use randomForest::randomForest()
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# library(party, verbose = F)
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# # Reproducibility
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# set.seed(123)
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# # Fit cforest model
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# # default params
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# # set interactions to TRUE if using interaction between T and X
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# model.cf <- cforest(formula = vt.o$getFormula(), data = vt.o$getData(interactions = T))
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# # initialize VT.forest.one
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# vt.f.cf <- VT.forest.one(vt.o, model.cf)
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# # Then, use run() to compute probabilities
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# vt.f.cf$run()
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## ------------------------------------------------------------------------
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# Copy new object
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vt.o.tr <- vt.o$copy()
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# Change levels
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tmp <- ifelse(vt.o.tr$data$survival == 1, "y", "n")
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vt.o.tr$data$survival <- as.factor(tmp)
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rm(tmp)
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# Check new data to be sure
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formatRCTDataset(vt.o.tr$data, "survival", "THERAPY")
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# use caret::train()
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library(caret, verbose = F)
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# Reproducibility
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set.seed(123)
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# fit train model
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fitControl <- trainControl(classProbs = T, method = "none")
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model.tr <- train(x = vt.o.tr$getX(interactions = T),
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y = vt.o.tr$getY(),
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method = "rf",
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tuneGrid = data.frame(mtry = 5),
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trControl = fitControl)
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# initialize VT.forest.one
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vt.f.tr <- VT.forest.one(vt.o.tr, model.tr)
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# Then, use run() to compute probabilities
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vt.f.tr$run()
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## ------------------------------------------------------------------------
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# grow RF for T = 1
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model.rf.trt1 <- randomForest(x = vt.o$getX(trt = 1),
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y = vt.o$getY(trt = 1))
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# grow RF for T = 0
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model.rf.trt0 <- randomForest(x = vt.o$getX(trt = 0),
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y = vt.o$getY(trt = 0))
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# initialize VT.forest.double()
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vt.doublef.rf <- VT.forest.double(vt.o, model.rf.trt1, model.rf.trt0)
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# Then, use run() to compute probabilities
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vt.doublef.rf$run()
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## ---- cache=TRUE---------------------------------------------------------
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# initialize k-fold RF
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model.fold <- VirtualTwins:::VT.forest.fold(vt.o, fold = 5, ratio = 1, interactions = T)
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# grow RF with randomForest package options
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# set do.trace option to see the 5 folds
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model.fold$run(ntree = 500, do.trace = 500)
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## ------------------------------------------------------------------------
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# you get twin1 and twin2 by your own method
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# here, i'll use random number between 0 and 1 :
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twin1_random <- runif(470)
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twin2_random <- runif(470)
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# then you can initialize VT.difft class :
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model.difft <- VT.difft(vt.o, twin1 = twin1_random, twin2 = twin2_random, "absolute")
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# compute difference of twins :
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model.difft$computeDifft()
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# See results
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head(model.difft$difft)
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# Graph :
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# hist(model.difft$difft)
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## ------------------------------------------------------------------------
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# initialize classification tree
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tr.class <- VT.tree.class(vt.f.rf, sens = ">", threshold = quantile(vt.f.rf$difft, 0.7))
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# compute tree with rpart option
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tr.class$run(cp = 0, maxdepth = 3, maxcompete = 1)
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## ------------------------------------------------------------------------
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# initialize regression tree
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tr.reg <- VT.tree.reg(vt.f.rf, sens = ">", threshold = quantile(vt.f.rf$difft, 0.7))
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# compute tree with rpart option
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tr.reg$run(cp = 0, maxdepth = 3, maxcompete = 1)
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