Description Usage Arguments Value Author(s) References Examples

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

1 | ```
HEMM(rankings, num.kappa, is.kappa.ranker, initial.method, it.max)
``` |

`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. |

`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 |

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

An extended Mallows model for ranked data aggregation

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
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
``` |

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