ConsRank (version 2.1.5)

EMCons: Branch-and-bound algorithm to find consensus (median) ranking according to the Kemeny's axiomatic approach

Description

Branch-and-bound algorithm to find consensus ranking as definned by Emond and Mason (2002). If the number of objects to be ranked is large (greater than 15 or 20, specially if there are missing rankings), it can work for very long time.

Usage

EMCons(X, Wk = NULL, PS = TRUE)

Value

a "list" containing the following components:

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

Arguments

X

A N by M data matrix, in which there are N judges and M objects to be judged. Each row is a ranking of the objects which are represented by the columns. Alternatively X can contain the rankings observed only once. In this case the argument Wk must be used

Wk

Optional: the frequency of each ranking in the data

PS

If PS=TRUE, on the screen some information about how many branches are processed are displayed

Author

Antonio D'Ambrosio

Details

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

References

Emond, E. J., and Mason, D. W. (2002). A new rank correlation coefficient with application to the consensus ranking problem. Journal of Multi-Criteria Decision Analysis, 11(1), 17-28.

See Also

consrank

Examples

data(Idea)
RevIdea=6-Idea 
# as 5 means "most associated", it is necessary compute the reverse ranking of 
# each rankings to have rank 1 = "most associated" and rank 5 = "least associated"
CR=EMCons(RevIdea)