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Sensitivity scatter plot against input values

Source: R/SensDotPlot.R
SensDotPlot.Rd

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