Nothing
gamTargetY <- function(X,
Y,
E,
trainInd,
testInd,
verbose,
colNameNoSmooth,
returnModel){
if(!is.null(colNameNoSmooth)){
idxNoSmooth <- which(is.element(colnames(X), colNameNoSmooth))
if(length(idxNoSmooth) == 0){
idxNoSmooth <- NULL
}
}else{
idxNoSmooth <- NULL
}
# train model with X only and predict out-of-sample with X-model
gamOnlyX <- getgamPredictions(as.data.frame(as.matrix(X)[trainInd,]),
Y[trainInd],
as.data.frame(as.matrix(X)[testInd,]),
idxNoSmooth = idxNoSmooth,
returnModel = returnModel)
predictedOnlyX <- gamOnlyX$predictions
# train model with X and E and predict out-of-sample with XE-model
dimE <- NCOL(E)
if(is.factor(E)){
idxNoSmoothE <- c(idxNoSmooth, ncol(as.matrix(X))+1)
}else if(is.data.frame(E)){
smE <- ncol(as.matrix(X))+which(sapply(E, is.factor))
idxNoSmoothE <- c(idxNoSmooth, smE)
}else{
idxNoSmoothE <- idxNoSmooth
}
if(is.factor(E)){
E <- as.matrix(as.numeric(as.character(E)))
}else if(is.vector(E)){
E <- as.matrix(E)
}
gamXE <- getgamPredictions(as.data.frame(cbind(as.matrix(X)[trainInd,], E[trainInd,])),
Y[trainInd],
as.data.frame(cbind(as.matrix(X)[testInd,], E[testInd,])),
idxNoSmooth = idxNoSmoothE,
returnModel = returnModel)
predictedXE <- gamXE$predictions
if(!returnModel){
list(predictedOnlyX = predictedOnlyX,
predictedXE = predictedXE)
}else{
list(predictedOnlyX = predictedOnlyX,
predictedXE = predictedXE,
model = list(gamOnlyX = gamOnlyX$model, gamXE = gamXE$model))
}
}
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