Description Usage Arguments Details Value Author(s) References See Also Examples
Aggregating ranked lists using three Markov chain algorithms.
1  | 
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 <shili@stat.osu.edu>
Lin, S. (2010). Space oriented rank-based data integration. Statistical Applications in Genetics and Molecular Biology 9, Article 20.
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