Plot method for the SensMLP Class

Plot the sensitivities and sensitivity metrics of a SensMLP object.

Usage

# S3 method for SensMLP
plot(x, plotType = c("sensitivities", "time", "features"), ...)

Arguments

x

SensMLP object created by SensAnalysisMLP

plotType

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

• "sensitivities" (default): use SensAnalysisMLP function

• "time": use SensTimePlot function

• "features": use SensFeaturePlot function

...

additional parameters passed to plot function of the NeuralSens package

Value

list of graphic objects created by ggplot

References

Pizarroso J, Portela J, Muñoz A (2022). NeuralSens: Sensitivity Analysis of Neural Networks. Journal of Statistical Software, 102(7), 1-36.

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 SensAnalysisMLP
sens <- NeuralSens::SensAnalysisMLP(nnetmod, trData = nntrData, plot = FALSE)
# \donttest{
plot(sens)
#> Warning: All aesthetics have length 1, but the data has 2 rows.
#> ℹ Did you mean to use `annotate()`?

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.

plot(sens,"features")

# }