kappalab (version 0.4-0)

lin.prog.capa.ident: Capacity identification based on linear programming

Description

Creates an object of class Mobius.capacity using the linear programming approach proposed by Marichal and Roubens (see reference hereafter). Roughly speaking, this function determines, if it exists, the capacity compatible with a set of linear constraints that "separates" the most the provided alternatives. The problem is solved using the lpSolve package.

Usage

lin.prog.capa.ident(n, k, A.Choquet.preorder = NULL,
A.Shapley.preorder = NULL, A.Shapley.interval = NULL,
A.interaction.preorder = NULL, A.interaction.interval = NULL,
A.inter.additive.partition = NULL, epsilon = 1e-6)

Arguments

n

Object of class numeric containing the number of elements of the set on which the object of class Mobius.capacity is to be defined.

k

Object of class numeric imposing that the solution is at most a k-additive capacity (the M�bius{Mobius} transform of subsets whose cardinal is superior to k vanishes).

A.Choquet.preorder

Object of class matrix containing the constraints relative to the preorder of the alternatives. Each line of the matrix corresponds to one constraint of the type "alternative a is preferred to alternative b

A.Shapley.preorder

Object of class matrix containing the constraints relative to the preorder of the criteria. Each line of this 3-column matrix corresponds to one constraint of the type "the Shapley importance index of criterion i is g

A.Shapley.interval

Object of class matrix containing the constraints relative to the quantitative importance of the criteria. Each line of this 3-column matrix corresponds to one constraint of the type "the Shapley importance index of criterion

A.interaction.preorder

Object of class matrix containing the constraints relative to the preorder of the pairs of criteria in terms of the Shapley interaction index. Each line of this 5-column matrix corresponds to one constraint of the type "the Shaple

A.interaction.interval

Object of class matrix containing the constraints relative to the type and the magnitude of the Shapley interaction index for pairs of criteria. Each line of this 4-column matrix corresponds to one constraint of the type "the

A.inter.additive.partition

Object of class numeric encoding a partition of the set of criteria imposing that there be no interactions among criteria belonging to different classes of the partition. The partition is to be given under the form of a vecto

epsilon

Object of class numeric containing the thresold value for the monotonicity constraints, i.e. the difference between the "weights" of two subsets whose cardinals differ exactly by 1 must be greater than epsilon.

Value

• The function returns a list structured as follows:
• solution
  Object of class Mobius.capacity containing the M�bius{Mobius} transform of the k-additive solution, if any.
• value
  Value of the objective function.
• lp.object
  Object of class lp.object returned by lpSolve.

Details

The linear program is solved using the lp function of the lpSolve.

References

K. Fujimoto and T. Murofushi (2000) Hierarchical decomposition of the Choquet integral, in: Fuzzy Measures and Integrals: Theory and Applications, M. Grabisch, T. Murofushi, and M. Sugeno Eds, Physica Verlag, pages 95-103. J-L. Marichal and M. Roubens (2000), Determination of weights of interacting criteria from a reference set, European Journal of Operational Research 124, pages 641-650.

See Also

Mobius.capacity-class, mini.var.capa.ident, mini.dist.capa.ident, least.squares.capa.ident, heuristic.ls.capa.ident, ls.sorting.capa.ident, entropy.capa.ident.

Examples

## some alternatives
a <- c(18,11,18,11,11)
b <- c(18,18,11,11,11)
c <- c(11,11,18,18,11)
d <- c(18,11,11,11,18)
e <- c(11,11,18,11,18)
    
## preference threshold relative
## to the preorder of the alternatives
delta.C <- 1

## corresponding Choquet preorder constraint matrix 
Acp <- rbind(c(d,a,delta.C),
             c(a,e,delta.C),
             c(e,b,delta.C),
             c(b,c,delta.C)
            )

## a Shapley preorder constraint matrix
## Sh(1) - Sh(2) >= -delta.S
## Sh(2) - Sh(1) >= -delta.S
## Sh(3) - Sh(4) >= -delta.S
## Sh(4) - Sh(3) >= -delta.S
## i.e. criteria 1,2 and criteria 3,4
## should have the same global importances
delta.S <- 0.01    
Asp <- rbind(c(1,2,-delta.S),
             c(2,1,-delta.S),
             c(3,4,-delta.S),
             c(4,3,-delta.S)
            )

## a Shapley interval constraint matrix
## 0.3 <= Sh(1) <= 0.9 
Asi <- rbind(c(1,0.3,0.9))


## an interaction preorder constraint matrix
## such that I(12) = I(34)
delta.I <- 0.01
Aip <- rbind(c(1,2,3,4,-delta.I),
             c(3,4,1,2,-delta.I))

## an interaction interval constraint matrix
## i.e. -0.20 <= I(12) <= -0.15 
Aii <- rbind(c(1,2,-0.2,-0.15))


## a LP 2-additive solution
lin.prog <- lin.prog.capa.ident(5,2,A.Choquet.preorder = Acp)              
m <- lin.prog$solution
m

## the resulting global evaluations
rbind(c(a,mean(a),Choquet.integral(m,a)),
      c(b,mean(b),Choquet.integral(m,b)),
      c(c,mean(c),Choquet.integral(m,c)),
      c(d,mean(d),Choquet.integral(m,d)),
      c(e,mean(e),Choquet.integral(m,e)))

## the Shapley value
Shapley.value(m)

## a LP 3-additive more constrained solution
lin.prog2 <- lin.prog.capa.ident(5,3,
                                   A.Choquet.preorder = Acp,
                                   A.Shapley.preorder = Asp)
m <- lin.prog2$solution
m
rbind(c(a,mean(a),Choquet.integral(m,a)),
      c(b,mean(b),Choquet.integral(m,b)),
      c(c,mean(c),Choquet.integral(m,c)),
      c(d,mean(d),Choquet.integral(m,d)),
      c(e,mean(e),Choquet.integral(m,e)))
Shapley.value(m)

## a LP 5-additive more constrained solution
lin.prog3 <- lin.prog.capa.ident(5,5,
                                   A.Choquet.preorder = Acp,
                                   A.Shapley.preorder = Asp,
                                   A.Shapley.interval = Asi,
                                   A.interaction.preorder = Aip,
                                   A.interaction.interval = Aii)

m <- lin.prog3$solution
m
rbind(c(a,mean(a),Choquet.integral(m,a)),
      c(b,mean(b),Choquet.integral(m,b)),
      c(c,mean(c),Choquet.integral(m,c)),
      c(d,mean(d),Choquet.integral(m,d)),
      c(e,mean(e),Choquet.integral(m,e)))
summary(m)