Neural Network Prediction Intervals in R - r

Question

I am trying to compute prediction intervals for my neural network created with the neuralnet package.
I use R in Tableau Software, by creating .RData files containing my functions and loaded in Tableau.
It's a simple NN, with one hidden layer containing 5 nodes. I searched and found this package : nnetpredint
So I tried to use it, using their examples.
I tried also to change the way I use it (train/test in same data frame, separated data frames with the same columns names etc.)
And the best result I had was the prediction, but without the lowerBound and upperBound columns.
In fact, I got exactly the same result as when I use compute(myNN, etc.), but I don't have the second and third columns.
Thanks for your help,
EDIT :
My data is coming from tableau, my function take five parameters which are :
ValuesToExplain,train1,train2,test1,test2.
Then, i create and train my NN with the 3first and try to compute the two last.
(test1 = k*train1 and test2 = k2*train2 for now but it will probably move in the future).
Here is my whole code :
NNetwork <- function(objectiveValues, knownValues1, knownValues2, newData, newData2){
numberOfColumn = 3
##Create the training dataframe
training <- data.frame(objectiveValues, knownValues1,knownValues2)
training[which(is.na(training[,"objectiveValues"])),"objectiveValues"]<- mean(training[,"objectiveValues"], na.rm = TRUE)
training[which(is.na(training[,"knownValues1"])),"knownValues1"]<- mean(training[,"knownValues1"], na.rm = TRUE)
training[which(is.na(training[,"knownValues2"])),"knownValues2"]<- mean(training[,"knownValues2"], na.rm = TRUE)
## Create the testing dataframe
testing <- data.frame(objectiveValues,newData,newData2)
names(testing) <- c("objectiveValues", "knownValues1", "knownValues2")
testing[which(is.na(testing[,"objectiveValues"])),"objectiveValues"]<- mean(testing[,"objectiveValues"], na.rm = TRUE)
testing[which(is.na(testing[,"knownValues1"])),"knownValues1"]<- mean(testing[,"knownValues1"], na.rm = TRUE)
testing[which(is.na(testing[,"knownValues2"])),"knownValues2"]<- mean(testing[,"knownValues2"], na.rm = TRUE)
## Scaling
maxs <- apply(training, 2, max)
mins <- apply(training, 2, min)
trainingScaled <- as.data.frame(scale(training, center = mins, scale = maxs - mins))
testingScaled <- as.data.frame(scale(testing, center = mins, scale = maxs - mins))
### NeuralNetwork Part
library(neuralnet)
n <- names(trainingScaled)
f <- as.formula(paste("objectiveValues ~", paste(n[!n %in% "objectiveValues"], collapse = " + ")))
# Training NN
nn <- neuralnet(f, data=trainingScaled,hidden=5,linear.output=TRUE)
# Using NN
computedTrainingScaled <- compute(nn,trainingScaled[,2:numberOfColumn])
computedFromNNScaled <- compute(nn,testingScaled[,2:numberOfColumn])
# UnScaling
computedTraining <- computedTrainingScaled$net.result*(max(training$objectiveValues)-min(training$objectiveValues))+min(training$objectiveValues)
computedFromNN <- computedFromNNScaled$net.result*(max(training$objectiveValues)-min(training$objectiveValues))+min(training$objectiveValues)
RSquare = (1-( (sum((training$objectiveValues - computedTraining)^2))/(sum((training$objectiveValues - mean(training$objectiveValues))^2)) ))*100
RSE = sum((training$objectiveValues - computedTraining)^2)/nrow(training)
res <- (1:nrow(training))
library(nnetpredint) # Getting prediction confidence interval
x <- trainingScaled[,2:numberOfColumn]
y <- trainingScaled[1]
newData <- testingScaled[,2:numberOfColumn]
# S3 generic method: Object of nn
yPredInt <- nnetPredInt(nn, x, y, newData)
for(i in 1:nrow(training)){
res[i] <- paste(computedFromNN[i],RSquare,RSE, sep="#")
}
return(res)
}
save(NNetwork, file = "NNetwork.RData")
Here, i removed the part using the nnetpredint pckage because it was not working, but it was like this :
library(nnetpredint)
y <- trainingScaled
x <- trainingScaled[,2:3]
newData <- testingScaled[,2:3]
yPredInt <- nnetPredInt(nn, x, y, newData)
My problem is that when I try to access yPredInt$lowerBound or yPredInt$upperBound , they don't exist.