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