HEMM {ExtMallows}

A hierarchical extended Mallows model for aggregating multiple ranking lists

Description

It uses the hierarchical extended Mallows model to aggregate multiple full/partial ranking lists.

Usage

HEMM(rankings, num.kappa, is.kappa.ranker, initial.method, it.max)

Arguments

rankings

A n by m matrix, with each column representing a ranking list, which ranks the items from the most preferred to the least preferred. For missing items, use 0 to denote them.

num.kappa

the number of over-correlated ranking groups

is.kappa.ranker

a list of over-correlated ranking groups, with the k-th element denoting the column numbers of the rankings that belong to the k-th group

initial.method

the method for initializing the value of pi0, with four options: mean, median, geometric and random (the mean of three randomly sampled ranking lists). By default, initial.method="mean".

it.max

the maximum number of iterations. By default, it.max=20.

Value

op.phi

optimal value of phi

op.phi1

optimal value of phi1, the phi value in over-correlated ranking groups

op.omega

optimal value of omega

op.alpha

optimal value of alpha

op.pi0

optimal value of pi0, ranking the items from the most preferred to the least preferred

op.kappa

optimal value of kappa, denoting the items from the most preferred to the least preferred

max.logL

maximum value of log-likelihood

Author(s)

Han Li, Minxuan Xu, Jun S. Liu and Xiaodan Fan

References

An extended Mallows model for ranked data aggregation

Examples

data(simu3)
res=HEMM(rankings = simu3, num.kappa = 2, is.kappa.ranker = list(1:5, 6:10),
    initial.method = "mean", it.max = 20)
res$op.phi
res$op.phi1
res$op.omega
res$op.pi0

data(NBArankings)
res=HEMM(rankings = NBArankings, num.kappa = 1, is.kappa.ranker = list(1:6),
    initial.method = "mean", it.max = 20)
res$op.omega
res$op.pi0
res$op.kappa