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optCluster (version 1.0.1)

repRankAggreg: Repeat Rank Aggregation

Description

repRankAggreg repeats rank aggregation of ordered validation measure lists obtained from an object of class "optCluster". The function returns an object of class "optCluster".

Usage

repRankAggreg(optObj, rankMethod = "same", distance = "same", 
  rankVerbose = FALSE, ... )

Arguments

optObj
An object of class "optCluster".
rankMethod
A character string providing the method to be used for rank aggregation. As default, the "same" method as the input "optCluster" object is used. The cross-entropy Monte Carlo algorithm ("CE") or Genetic a
distance
A character string providing the type of distance to be used for measuring the similarity between ordered lists in rank aggregation. As default, the "same" distance as the input "optCluster" object is used.
rankVerbose
If TRUE, current rank aggregation results are displayed at each iteration.
...
Additional arguments that can be passed to the internal function RankAggreg.

Value

  • repRankAggreg returns an object of class "optCluster". The class description is provided in the help file.

Details

This function tests the consistency of the rank aggregation results by repeating rank aggregation with the same rank aggregation method, distance measure, clustering algorithm lists, and validation score lists used to create the input object of class "optCluster". A different rank aggregation algorithm or type of distance measure can also be evaluated using this function, but doing so may affect the final results.

References

Pihur, V., Datta, S. and Datta, S. (2007). Weighted rank aggregation of cluster validation measures: A Mounte Carlo cross-entropy approach. Bioinformatics 23(13): 1607-1615. Pihur, V., Datta, S. and Datta, S. (2009). RankAggreg, an R package for weighted rank aggregation. BMC Bioinformatics, 10:62, http://www.biomedcentral.com/1471-2105/10/62. Sekula, M. (2015). optCluster : An R package for Determining the Optimal Clustering Algorithm and Optimal Number of Clusters. Electronic Theses and Dissertations. Paper 2147. http://ir.library.louisville.edu/etd/2147

See Also

For a description of the RankAggreg function, including all available arguments that can be passed to it, see RankAggreg in the RankAggreg package.

Examples

Run this code
## These examples may take a few minutes to compute
	## Obtain Dataset	
	data(arabid)			
	
	## Normalize Data with Respect to Library Size	
	obj <- t(t(arabid)/colSums(arabid))
	
	## Analysis of Normalized Data using Internal and Stability Validation Measures
	norm1 <- optCluster(obj, 2:4, clMethods = "all")
	print(norm1)
	repCE <- repRankAggreg(norm1)
	print(repCE)
	repGA <- repRankAggreg(norm1, rankMethod = "GA")
	print(repGA)

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