individualEvpiSimulation: 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
)

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.

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.

Examples

Run this code

# 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)