print.summary.HessMLP function

Print method of the summary HessMLP Class

Print the sensitivity metrics of a HessMLP object. This metrics are the mean sensitivity, the standard deviation of sensitivities and the mean of sensitivities square

## S3 method for class 'summary.HessMLP'
print(x, round_digits = NULL, ...)

Arguments

• x: summary.HessMLP object created by summary method of HessMLP object
• round_digits: integer number of decimal places, default NULL
• ...: additional parameters

Examples

## Load data -------------------------------------------------------------------
data("DAILY_DEMAND_TR")
fdata <- DAILY_DEMAND_TR

## Parameters of the NNET ------------------------------------------------------
hidden_neurons <- 5
iters <- 250
decay <- 0.1

################################################################################
#########################  REGRESSION NNET #####################################
################################################################################
## Regression dataframe --------------------------------------------------------
# Scale the data
fdata.Reg.tr <- fdata[,2:ncol(fdata)]
fdata.Reg.tr[,3] <- fdata.Reg.tr[,3]/10
fdata.Reg.tr[,1] <- fdata.Reg.tr[,1]/1000

# Normalize the data for some models
preProc <- caret::preProcess(fdata.Reg.tr, method = c("center","scale"))
nntrData <- predict(preProc, fdata.Reg.tr)

#' ## TRAIN nnet NNET --------------------------------------------------------
# Create a formula to train NNET
form <- paste(names(fdata.Reg.tr)[2:ncol(fdata.Reg.tr)], collapse = " + ")
form <- formula(paste(names(fdata.Reg.tr)[1], form, sep = " ~ "))

set.seed(150)
nnetmod <- nnet::nnet(form,
                           data = nntrData,
                           linear.output = TRUE,
                           size = hidden_neurons,
                           decay = decay,
                           maxit = iters)
# Try HessianMLP
sens <- NeuralSens::HessianMLP(nnetmod, trData = nntrData, plot = FALSE)
print(summary(sens))