Description Usage Arguments Details Value Author(s) References See Also Examples

Aggregating ranked lists using three Markov chain algorithms.

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`input` |
A list containing individual ranked lists. |

`space` |
A list containing the underlying spaces. If not explicitly specified, all lists are treated as coming from a common space defined by the union of all input lists. |

`k` |
An integer specifying the number of items in the output top-k list. |

`a` |
Tuning parameter to make sure Markov Chain with the transition matrix is ergodic; default set to 0.15. |

`delta` |
Convergence criterion for stationary distribution; default set to 10^-15. |

Constructs ergodic Markov Chain based on ranking data from individual lists. A larger probability in the stationary distribution corresponds to a higher rank of the corresponding element. The algorithm are considered: MC1 (spam sensitive), MC2 (majority rule), and MC3 (proportional).

A list of elements, two for each of the MC algorithms:

`MC1.TopK` |
A vector of aggregate ranked elements based on |

`MC1.Prob` |
Stationary probability distribution: a vector of probabilities corresponding to the ranked elements in |

`MC2.TopK` |
A vector of aggregate ranked elements based on MC2 algorithm. |

`MC2.Prob` |
Stationary probability distribution: a vector of probabilities corresponding to the ranked elements in |

`MC3.TopK` |
A vector of aggregate ranked elements based on MC3 algorithm. |

`MC3.Prob` |
Stationary probability distribution: a vector of probabilities corresponding to the ranked elements in |

Shili Lin <[email protected]>

Lin, S. (2010). Space oriented rank-based data integration. Statistical Applications in Genetics and Molecular Biology 9, Article 20.

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TopKLists documentation built on May 31, 2017, 1:46 a.m.

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