plot.HessMLP function

Plot method for the HessMLP Class

Plot the sensitivities and sensitivity metrics of a HessMLP object.

## S3 method for class 'HessMLP'
plot(
  x,
  plotType = c("sensitivities", "time", "features", "matrix", "interactions"),
  ...
)

Arguments

• x: HessMLP object created by HessianMLP

• plotType: character specifying which type of plot should be created. It can be:

  • "sensitivities" (default): use HessianMLP function
  • "time": use SensTimePlot function
  • "features": use HessFeaturePlot function
  • "matrix": use SensMatPlot function to show the values of second partial derivatives
  • "interactions": use SensMatPlot function to show the values of second partial derivatives and the first partial derivatives in the diagonal

• ...: additional parameters passed to plot function of the NeuralSens package

Returns

list of graphic objects created by ggplot

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)

plot(sens)
plot(sens,"time")