Plot of sensitivity of the neural network output respect to the inputs over the time variable from the data provided
CombineSens(object, comb_type ="mean")
Arguments
object: SensMLP object generated by SensAnalysisMLP
with several outputs (classification MLP)
comb_type: Function to combine the matrixes of the raw_sens component of object. It can be "mean", "median" or "sqmean". It can also be a function to combine the rows of the matrixes
Returns
SensMLP object with the sensitivities combined
Examples
fdata <- iris
## Parameters of the NNET ------------------------------------------------------hidden_neurons <-5iters <-250decay <-0.1#' ## TRAIN nnet NNET --------------------------------------------------------# Create a formula to train NNETform <- paste(names(fdata)[1:ncol(fdata)-1], collapse =" + ")form <- formula(paste(names(fdata)[5], form, sep =" ~ "))set.seed(150)mod <- nnet::nnet(form, data = fdata, linear.output =TRUE, size = hidden_neurons, decay = decay, maxit = iters)# mod should be a neural network classification modelsens <- SensAnalysisMLP(mod, trData = fdata, output_name ='Species')combinesens <- CombineSens(sens,"sqmean")