Change significance of boot SensMLP Class

For a SensMLP Class object, change the significance level of the statistical tests

Usage

ChangeBootAlpha(x, boot.alpha)

Arguments

x

SensMLP object created by SensAnalysisMLP

boot.alpha

float significance level

Value

SensMLP object with changed significance level. All boot related metrics are changed

Examples

# \donttest{
## 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


## TRAIN nnet NNET --------------------------------------------------------

set.seed(150)
nnetmod <- caret::train(DEM ~ .,
                 data = fdata.Reg.tr,
                 method = "nnet",
                 tuneGrid = expand.grid(size = c(1), decay = c(0.01)),
                 trControl = caret::trainControl(method="none"),
                 preProcess = c('center', 'scale'),
                 linout = FALSE,
                 trace = FALSE,
                 maxit = 300)
# Try SensAnalysisMLP
sens <- NeuralSens::SensAnalysisMLP(nnetmod, trData = fdata.Reg.tr,
                                    plot = FALSE, boot.R=2, output_name='DEM')
#> # Calculating bootstrap sensitivity measures
#> 
  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |===================================                                   |  50%
  |                                                                            
  |======================================================================| 100%
NeuralSens::ChangeBootAlpha(sens, boot.alpha=0.1)
#> Sensitivity analysis of 2-1-1 MLP network.
#> 
#>   1980 samples
#> 
#> Sensitivities of each output (only 5 first samples):
#> $DEM
#>             WD        TEMP
#> [1,] 0.1657809 -0.06765913
#> [2,] 0.3102340 -0.12661386
#> [3,] 0.3185984 -0.13002759
#> [4,] 0.3180175 -0.12979052
#> [5,] 0.3204461 -0.13078166
# }