ConsRank (version 1.0.1)

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), it can work for very long time.

Usage

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

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 u

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

Value

• Consensus
  Consensus ranking
• Tau
  Tau extended rank correlation coefficient
• Eltime
  Elapsed time in seconds

Details

If the objects to be ranked is large (>15-20), it can take long time to finde the solutions. If the searching space is limited to the space of full rankings (also incomplete rankings, but without ties), use the function BBFULL or the functions FASTcons and QuickCons with the option FULL=TRUE.

References

Emond, E. J., & 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

BBFULL FASTcons QuickCons

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)