MC.ranks

Compute aggregate ranks based on the transition matrix from the three Markov Chain algorithms.

For multiple ranked input lists (full or partial) representing the same set of N objects, the package 'TopKLists' <doi:10.1515/sagmb-2014-0093> offers (1) statistical inference on the lengths of informative top-k lists, (2) stochastic aggregation of full or partial lists, and (3) graphical tools for the statistical exploration of input lists, and for the visualization of aggregation results. Note that RGtk2 and gWidgets2RGtk2 have been archived on CRAN. See <https://github.com/pievos101/TopKLists> for installation instructions.

Bastian Pfeifer

TopKLists

Inference, Aggregation and Visualization for Top-K Ranked Lists

MC.ranks function

<dl> <dt>elements</dt>
<dd>Unique elements of the union of all input lists - second element of the output list from function <code>trans.matrix</code></dd> <dt>trans</dt>
<dd>One of the three transition matrices build by function <code>trans.matrix</code> - 4 (5 or 6)
elements of the output list from function <code>trans.matrix</code></dd> <dt>a</dt>
<dd>Tuning parameter to make sure Markov Chain with the transition matrix is ergodic; parameter value passed from <code>MC</code>.</dd> <dt>delta</dt>
<dd>Convergence criterion for stationary distribution; parameter value passed from <code>MC</code>.</dd></dl>

Arguments

Shili Lin &lt;shili@stat.osu.edu&gt;

Author

MC based rank aggregation — MC.ranks

<dl>

 <dt>elements</dt>
<dd>Unique elements of the union of all input lists - second element of the output list from function <code>trans.matrix</code></dd>

 <dt>trans</dt>
<dd>One of the three transition matrices build by function <code>trans.matrix</code> - 4 (5 or 6)
elements of the output list from function <code>trans.matrix</code></dd>

 <dt>a</dt>
<dd>Tuning parameter to make sure Markov Chain with the transition matrix is ergodic; parameter value passed from <code>MC</code>.</dd>

 <dt>delta</dt>
<dd>Convergence criterion for stationary distribution; parameter value passed from <code>MC</code>.</dd>

</dl>

MC.ranks: MC based rank aggregation

Description

Usage

Value

Arguments

Author

Details

References

See Also