individualEvpiSimulation {decisionSupport}

Individual Expected Value of Perfect Information Simulation

Description

The Individual Expected Value of Perfect Information (Individual EVPI) is calculated based on a Monte Carlo simulation of the values of two different decision alternatives.

Usage

individualEvpiSimulation(
  welfare,
  currentEstimate,
  perfectProspectiveNames = row.names(currentEstimate),
  perfectProspectiveValues = colMeans(as.data.frame(random(rho = currentEstimate, n =
    numberOfModelRuns, method = randomMethod, relativeTolerance =
    relativeTolerance))[perfectProspectiveNames]),
  numberOfModelRuns,
  randomMethod = "calculate",
  functionSyntax = "data.frameNames",
  relativeTolerance = 0.05,
  verbosity = 0
)

Arguments

welfare

either a function or a list with two functions, i.e. list(p1,p2). In the first case the function is the net benefit (or welfare) of project approval (PA) vs. the status quo (SQ). In the second case the element p1 is the function valuing the first project and the element p2 valuing the second project, viz. the welfare function of p1 and p2 respectively.

currentEstimate

estimate: describing the distribution of the input variables as currently being estimated.

perfectProspectiveNames

character vector: input variable names that are assumed to be known perfectly with prospective information.

perfectProspectiveValues

numeric vector: of the same length as perfectProspectiveNames with the corresponding values assumed to be known perfectly.

numberOfModelRuns

integer: The number of running the welfare model for the underlying Monte Carlo simulation.

randomMethod

character: The method to be used to sample the distribution representing the input estimate. For details see option method in random.estimate.

functionSyntax

character: function syntax used in the welfare function(s). For details see mcSimulation.

relativeTolerance

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.

verbosity

integer: if 0 the function is silent; the larger the value the more verbose is output information.

Details

The Individual EVPI is defined as the EVI with respect to a prospective information that assumes perfect knowledge on one particular variable.

Value

An object of class eviSimulation with the following elements:

$current

welfareDecisionAnalysis object for currentEstimate

$prospective

welfareDecisionAnalysis object for single perfectProspectiveNames or a list of welfareDecisionAnalysis objects for several perfectProspectiveNames.

$evi

Expected Value of Information(s) (EVI)(s) gained by the perfect knowledge of individual variable(s) w.r.t. the current estimate.

See Also

eviSimulation, welfareDecisionAnalysis, mcSimulation, estimate

Examples

# Number of running the underlying welfare model:
n=10000
# Create the current estimate from text:
estimateText<-"variable,  distribution, lower, upper
               revenue1,  posnorm,      100,   1000
               revenue2,  posnorm,      50,    2000
               costs1,    posnorm,      50,    2000
               costs2,    posnorm,      100,   1000"
currentEstimate<-as.estimate(read.csv(header=TRUE, text=estimateText, 
                          strip.white=TRUE, stringsAsFactors=FALSE))
# The welfare function:
profitModel <- function(x){
 list(Profit=x$revenue1 + x$revenue2 - x$costs1 - x$costs2)
}
# Calculate the Individual EVPI:
individualEvpiResult<-individualEvpiSimulation(welfare=profitModel,
                                               currentEstimate=currentEstimate,
                                               numberOfModelRuns=n,
                                               functionSyntax="data.frameNames")
# Show the simulation results:
print(sort(summary(individualEvpiResult)),decreasing=TRUE,along="Profit")
hist(individualEvpiResult, breaks=100)