ConsRank (version 2.1.5)

kemenyd: Kemeny distance

Description

Compute the Kemeny distance of a data matrix containing preference rankings, or compute the kemeny distance between two (matrices containing) rankings.

Usage

kemenyd(X, Y = NULL)

Value

If there is only X as input, d = square distance matrix. If there is also Y as input, d = matrix with N rows and n columns.

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. If there is only X as input, the output is a square distance matrix

Y

A row vector, or a n by M data matrix in which there are n judges and the same M objects as X to be judged.

Author

Antonio D'Ambrosio

References

Kemeny, J. G., & Snell, L. J. (1962). Preference ranking: an axiomatic approach. Mathematical models in the social sciences, 9-23.

See Also

tau_x TauX rank correlation coefficient

iw_kemenyd item-weighted Kemeny distance

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"
KD<-kemenyd(RevIdea)
KD2<-kemenyd(RevIdea[1:10,],RevIdea[55,])