Nothing
rfTargetY <- function(X,
Y,
E,
trainInd,
testInd,
verbose,
mtry,
ntree,
nodesize,
maxnodes,
permute,
returnModel){
if(!permute){
# train model with X only
matX <- as.matrix(X)[trainInd, , drop = FALSE]
colnames(matX) <- paste("V", 1:ncol(matX), sep = "")
rfResultOnlyX <- randomForest(x = matX,
y = Y[trainInd],
mtry = mtry,
ntree = ntree,
nodesize = nodesize,
maxnode = maxnodes)
# predict out-of-sample with X-model
matXPred <- as.matrix(X)[testInd, , drop = FALSE]
colnames(matXPred) <- paste("V", 1:ncol(matXPred), sep = "")
predictedOnlyX <- predict(rfResultOnlyX, newdata = matXPred)
}else{
# train model with X and E
matX <- cbind(X = as.matrix(X)[trainInd,], E = E[trainInd][sample(1:length(trainInd))])
colnames(matX) <- paste("V", 1:ncol(matX), sep = "")
rfResultOnlyX <- randomForest(x = matX,
y = Y[trainInd],
mtry = mtry,
ntree = ntree,
nodesize = nodesize,
maxnode = maxnodes)
# predict out-of-sample with XE-model
matXPred <- cbind(X = as.matrix(X)[testInd,], E = E[testInd][sample(1:length(testInd))])
colnames(matXPred) <- paste("V", 1:ncol(matXPred), sep = "")
predictedOnlyX <- predict(rfResultOnlyX, newdata = matXPred)
}
# train model with X and E
matXE <- cbind(X = as.matrix(X)[trainInd,], E = E[trainInd])
colnames(matXE) <- paste("V", 1:ncol(matXE), sep = "")
rfResultXE <- randomForest(x = matXE,
y = Y[trainInd],
mtry = mtry,
ntree = ntree,
nodesize = nodesize,
maxnode = maxnodes)
# predict out-of-sample with XE-model
matXEPred <- cbind(X = as.matrix(X)[testInd,], E = E[testInd])
colnames(matXEPred) <- paste("V", 1:ncol(matXEPred), sep = "")
predictedXE <- predict(rfResultXE, newdata = matXEPred)
if(!returnModel){
list(predictedOnlyX = predictedOnlyX,
predictedXE = predictedXE)
}else{
list(predictedOnlyX = predictedOnlyX,
predictedXE = predictedXE,
model = list(rfResultOnlyX = rfResultOnlyX, rfResultXE = rfResultXE))
}
}
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