Skip to contents

Plot method for the HessMLP Class

Source: R/HessMLP.R
plot.HessMLP.Rd

Plot the sensitivities and sensitivity metrics of a HessMLP object.

Usage

# S3 method for 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

Value

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)
#> # weights:  21
#> initial  value 2487.870002 
#> iter  10 value 1587.516208
#> iter  20 value 1349.706741
#> iter  30 value 1333.940734
#> iter  40 value 1329.097060
#> iter  50 value 1326.518168
#> iter  60 value 1323.148574
#> iter  70 value 1322.378769
#> iter  80 value 1322.018091
#> final  value 1321.996301 
#> converged
# Try HessianMLP
sens <- NeuralSens::HessianMLP(nnetmod, trData = nntrData, plot = FALSE)
# \donttest{
plot(sens)
#> Warning: All aesthetics have length 1, but the data has 3 rows.
#> ℹ Did you mean to use `annotate()`?
#> Warning: minimum occurred at one end of the range

plot(sens,"time")
#> Warning: Use of `plotdata$value` is discouraged.
#> ℹ Use `value` instead.
#> Warning: Use of `plotdata$variable` is discouraged.
#> ℹ Use `variable` instead.
#> Warning: Use of `plotdata$variable` is discouraged.
#> ℹ Use `variable` instead.

# }