plot.HessMLP | R Documentation |
Plot the sensitivities and sensitivity metrics of a HessMLP
object.
## S3 method for class 'HessMLP'
plot(
x,
plotType = c("sensitivities", "time", "features", "matrix", "interactions"),
...
)
x |
|
plotType |
|
... |
additional parameters passed to plot function of the |
list of graphic objects created by ggplot
#' ## 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")
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.