FASTcons {ConsRank}

FAST algorithm to find consensus (median) ranking. FAST algorithm to find consensus (median) ranking defined by Amodio, D'Ambrosio and Siciliano (2016). It returns at least one of the solutions. If there are multiple solutions, sometimes it returns all the solutions, sometimes it returns some solutions, always it returns at least one solution.

Description

FAST algorithm to find consensus (median) ranking.

FAST algorithm to find consensus (median) ranking defined by Amodio, D'Ambrosio and Siciliano (2016). It returns at least one of the solutions. If there are multiple solutions, sometimes it returns all the solutions, sometimes it returns some solutions, always it returns at least one solution.

Usage

FASTcons(X, Wk = NULL, maxiter = 50, FULL = FALSE, PS = FALSE)

Arguments

X

is a ranking data matrix

Wk

is a vector of weights

maxiter

maximum number of iterations: default = 50.

FULL

Default FULL=FALSE. If FULL=TRUE, the searching is limited to the space of full rankings.

PS

Default PS=FALSE. If PS=TRUE the number of current iteration is diplayed

Details

This function is deprecated and it will be removed in the next release of the package. Use function 'consrank' instead.

Value

a "list" containing the following components:

Consensus		the Consensus Ranking
Tau		averaged TauX rank correlation coefficient
Eltime		Elapsed time in seconds

Author(s)

Antonio D'Ambrosio [e-mail] and Sonia Amodio [e-mail]

References

Amodio, S., D'Ambrosio, A. and Siciliano, R. (2016). Accurate algorithms for identifying the median ranking when dealing with weak and partial rankings under the Kemeny axiomatic approach. European Journal of Operational Research, 249(2), 667-676.

See Also

EMCons Emond and Mason branch-and-bound algorithm.

QuickCons Quick algorithm.

Examples

##data(EMD)
##X=EMD[,1:15]
##Wk=matrix(EMD[,16],nrow=nrow(X))
##CR=FASTcons(X,Wk,maxiter=100)
##These lines produce all the three solutions in less than a minute.

data(sports)
CR=FASTcons(sports,maxiter=5)