SensDotPlot: Sensitivity scatter plot against input values

In NeuralSens: Sensitivity Analysis of Neural Networks

Sensitivity scatter plot against input values

Description

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

Usage

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

Value

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)

NeuralSens documentation built on July 9, 2023, 6:18 p.m.