getRanksWeights {clValid} | R Documentation |
Extract ranks and weights from clValid object
Description
Creates matrix of ranks and weights from clValid
object, to use as input for rank aggregation using
RankAggreg
in package RankAggreg
Usage
getRanksWeights(clVObj, measures = measNames(clVObj), nClust =
nClusters(clVObj), clAlgs = clusterMethods(clVObj))
Arguments
clVObj |
a clValid object |
measures |
the cluster validation measures to use for rank aggregation |
nClust |
the number of clusters to evaluate |
clAlgs |
the clustering algorithms to evaluate |
Details
This function extracts cluster validation measures from a
clValid
object, and creates a matrix of rankings
where each row contains a list of clustering algorithms which are
ranked according to the validation measure for that row. The function
also returns the cluster validation measures as a matrix of weights,
for use with weighted rank aggregation in the function
RankAggreg
. Any combination of validation
measures, numbers of clusters, and clustering algorithms can be
selected by the user. Number of clusters and clustering algorithms
are appended into a single name.
Value
A list with components
ranks |
Matrix with rankings for each validation measure in each row |
weights |
Matrix of weights, corresponding to the cluster validation measures, which are used for weighted rank aggregation |
Author(s)
Guy Brock
References
Brock, G., Pihur, V., Datta, S. and Datta, S. (2008) clValid: An R Package for Cluster Validation Journal of Statistical Software 25(4) https://www.jstatsoft.org/v25/i04/
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/
See Also
Examples
data(mouse)
express <- mouse[1:25,c("M1","M2","M3","NC1","NC2","NC3")]
rownames(express) <- mouse$ID[1:25]
clv <- clValid(express, 4:6, clMethods=c("hierarchical","kmeans","pam"),
validation=c("internal","stability"))
res <- getRanksWeights(clv)
if(require("RankAggreg")) {
CEWS <- RankAggreg(x=res$ranks, k=5, weights=res$weights, seed=123, verbose=FALSE)
CEWS
}