repRankAggreg repeats rank aggregation of ordered validation measure lists
obtained from an
object of class
function returns an object of class
repRankAggreg(optObj, rankMethod = "same", distance = "same", importance = NULL, rankVerbose = FALSE, ... )
An object of class
A character string
providing the method to be used for rank aggregation. As default, the "same"
method as the input
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
Vector of weights indicating the importance of each validation measure list. Default of NULL represents equal weights to each validation measure. See Weighted Rank Aggregation in the ‘Details’ section for more information.
If TRUE, current rank aggregation results are displayed at each iteration.
Additional arguments that can be passed to the internal function
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.
Weighted Rank Aggregation: A list of weights for each validation measure list
can be included using the
importance argument. The default value of equal weights (NULL) is
represented by rep(1, length(x)), where x is the character vector of validation measure names. This
means each validation measure list has a weight of 1/length(x).
To manually change the weights, the order of the validation measures selected needs to be known.
The order of validation measures used in
optCluster is provided below:
When selected, stability measures will ALWAYS be listed first and in the following order: "APN", "AD", "ADM", "FOM".
When selected, internal measures will only precede biological measures. The order of these measures is: "Connectivity", "Dunn", "Silhouette".
When selected, biological measures will always be listed last and in the following order: "BHI", "BSI".
repRankAggreg returns an object of class
"optCluster". The class description
is provided in the help file.
Sekula, M., Datta, S., and Datta, S. (2017). optCluster: An R package for determining the optimal clustering algorithm. Bioinformation, 13(3), 101. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5450252
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, https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-10-62.
For a description of the
RankAggreg function, including all available arguments that can be
passed to it, see
RankAggreg in the RankAggreg package.
## 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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