repRankAggreg
repeats rank aggregation of ordered validation measure lists
obtained from an object of class "optCluster"
. The
function returns an object of class "optCluster"
.
repRankAggreg(optObj, rankMethod = "same", distance = "same", importance = NULL, rankVerbose = FALSE, ... )
"optCluster"
."optCluster"
object is used. The
cross-entropy Monte Carlo algorithm ("CE") or Genetic algorithm ("GA") can also be
directly specified. Selection of only one method is allowed."optCluster"
object is used.
The weighted Spearman footrule distance ("Spearman") or the weighted Kendall's tau distance ("Kendall")
can also be directly specified. Selection of only one distance is allowed.RankAggreg
:
k
- Size of top-k list in aggregation.
convIN
- Stopping criteria for CE and GA algorithms. The algorithm converges once the "best" solution does not
change after convIN iterations. Default: 7 for CE and 30 for GA.
N
- Number of samples generated by MCMC in the CE algorithm. Default = 10*k^2
rho
- For CE algorithm, (rho*N) is the qunatile of candidate list sorted by function values.
weight
- For CE algorithm, the learning factor used in the probability update feature. Default = 0.25
popSize
- For GA algorithm population size in each generation. Default = 100
CP
- For GA algorithm, the cross-over probability. Default = 0.4
MP
- For GA algorithm, the mutation probability. Default = 0.01
repRankAggreg
returns an object of class "optCluster"
. The class description
is provided in the help file."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.
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:
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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