This function performs a Welfare Decision Analysis via a Monte Carlo simulation from input files and analyses the value of different information about the input variables. This value of information analysis can be done via combined PLSR - VIP analysis or via IndividualEVPI calculation. Results are saved as plots and tables.
decisionSupport(inputFilePath, outputPath, welfareFunction,
numberOfModelRuns, randomMethod = "calculate",
functionSyntax = "data.frameNames", relativeTolerance = 0.05,
write_table = TRUE, plsrVipAnalysis = TRUE,
individualEvpiNames = NULL, sortEvpiAlong = if (individualEvpiNames)
individualEvpiNames[[1]] else NULL, oldInputStandard = FALSE,
verbosity = 1)Path to input csv file, which gives the input estimate.
Path where the result plots and tables are saved.
The welfare function.
The number of running the welfare model for the underlying Monte Carlo simulation.
character: The method to be used to sample the distribution
representing the input estimate. For details see option method in
random.estimate.
character: function syntax used in the welfare function(s). For
details see mcSimulation.
numeric: the relative tolerance level of deviation of the
generated confidence interval from the specified interval. If this deviation is greater than
relativeTolerance a warning is given.
logical: If the full Monte Carlo simulation results and PLSR results should be
written to file.
logical: If PLSR-VIP analysis shall be performed.
character vector: names of variables, which for the
IndividualEVPI shall be obtained via Monte Carlo simulation. If =NULL (the default), no
IndividualEVPI is calculated; if ="all", the IndividualEVPI is calculated for all
variables. Note: depending on numberOfModelRuns and the complexity of
welfare this might take a long time.
character: result name along which the summary of the IndividualEVPI
shall be sorted. Only relevant if sortEvpiAlong!=NULL.
logical: If the old input standard should be used
(estimate_read_csv_old).
integer: if 0 the function is silent; the larger the value the
more verbose is output information.
This function integrates the most important features of
this package into a single function. It is wrapped arround the functions
welfareDecisionAnalysis, plsr.mcSimulation,
VIP and individualEvpiSimulation.
The combined Partial Least Squares Regression (PLSR) and Variables Importance in Projection
(VIP) analysis is implemented via: plsr.mcSimulation and
VIP.
Implementation: individualEvpiSimulation
mcSimulation, estimate, estimate_read_csv,
plsr.mcSimulation, VIP,
welfareDecisionAnalysis, individualEvpiSimulation,
decisionSupport-package