| MC {TopKLists} | R Documentation |
Markov chain based rank aggregation
Description
Aggregating ranked lists using three Markov chain algorithms.
Usage
MC(input, space = NULL, k = NULL, a = 0.15, delta = 10^-15)
Arguments
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. |
Details
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).
Value
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 |
Author(s)
Shili Lin <shili@stat.osu.edu>
References
Lin, S. (2010). Space oriented rank-based data integration. Statistical Applications in Genetics and Molecular Biology 9, Article 20.
See Also
Examples
#get sample data
data(TopKSpaceSampleInput)
outMC=MC(input,space) #underlying space-dependent
outMCa=MC(input,space=input) #top-k spaces
MC.plot(outMC)