Sim.Const.Weights: Simulation of constrained weights

In mau: Decision Models with Multi Attribute Utility Theory

Description

Simulation of weights employing the Dirichlet distribution. The concentration parameters for the Dirichlet distribution are tentative weights, additionally constraints over partial sums of weights are introduced by a list ordered structure.

Usage

Sim.Const.Weights(n, utilities, alpha, constraints)

Arguments

n	number of simulations
utilities	utility dataframe, first column is the identifier
alpha	concentration parameter for the Dirichlet distribution
constraints	list of sum constraints

Details

Employing the properties of the Dirichlet distribution, weights could be simulated with a given concentration, additionally this simulation can be carry out by subsets of weights only to meet specific constraints.

Value

List with data.frames {simulation, weights} with total utilities and simulated weights

Author(s)

Pedro Guarderas [e-mail]

See Also

Eval.Utilities

Examples

library( data.table )
N<-10
utilities<-data.table( id = 1:N, 
                       u1 = runif( N, 0, 1 ), 
                       u2 = runif( N, 0, 1 ), 
                       u3 = runif( N, 0, 1 ),
                       u4 = runif( N, 0, 1 ) )
n<-100
alpha<-c( 0.2, 0.5, 0.1, 0.2 )
constraints<-list( list( c(1,2), 0.7 ), 
                   list( c(3,4), 0.3 ) )
S<-Sim.Const.Weights( n, utilities, alpha, constraints )
plot.S<-Plot.Simulation.Weight( S$simulation, title = 'Simulations', 
                                xlab = 'ID', ylab = 'Utility' ) 
plot( plot.S )

mau documentation built on May 1, 2019, 8:23 p.m.