SensDotPlot function

Sensitivity scatter plot against input values

Plot of sensitivities of the neural network output respect to the inputs

SensDotPlot(
  object,
  fdata = NULL,
  input_vars = "all",
  output_vars = "all",
  smooth = FALSE,
  nspline = NULL,
  color = NULL,
  grid = FALSE,
  ...
)

Arguments

• object: fitted neural network model or array containing the raw sensitivities from the function SensAnalysisMLP
• fdata: data.frame containing the data to evaluate the sensitivity of the model.
• input_vars: character vector with the variables to create the scatter plot. If "all", then scatter plots are created for all the input variables in fdata.
• output_vars: character vector with the variables to create the scatter plot. If "all", then scatter plots are created for all the output variables in fdata.
• smooth: logical if TRUE, geom_smooth plots are added to each variable plot
• nspline: integer if smooth is TRUE, this determine the degree of the spline used to perform geom_smooth. If nspline is NULL, the square root of the length of the data is used as degrees of the spline.
• color: character specifying the name of a numeric variable of fdata to color the scatter plot.
• grid: logical. If TRUE, plots created are show together using arrangeGrob
• ...: further arguments that should be passed to SensAnalysisMLP function

Returns

list of geom_point plots for the inputs variables representing the sensitivity of each output respect to the inputs

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 SensDotPlot
NeuralSens::SensDotPlot(nnetmod, fdata = nntrData)