# ConsensusPartition: Consensus of Partitions In FreeSortR: Free Sorting Data Analysis

## Description

Returns the consensus partition among a set of partitions

## Usage

 ```1 2``` ```ConsensusPartition(Part, ngroups = 0, type = "cutree", optim = FALSE, maxiter = 100, plotDendrogram = FALSE, verbose = FALSE) ```

## Arguments

 `Part` Object of class `SortingPartition` `ngroups` Number of groups of the consensus (or `ngroups=0` for optimal choice) `type` Method (`type="cutree"` or `type="fusion"` or `type="medoid"`) `optim` Optimisation of the consensus (default is `optim=FALSE`) `maxiter` Maximum number of iterations for fusion algorithm `plotDendrogram` Plot of the dendrogram (if `type="cutree"` initialisation) `verbose` Print the initialisation results

## Details

The criterion for optimal consensus is the mean adjusted Rand Index between the consensus and the partitions given by the subjects.

If `ngroups=0`, consensus is computed between 2 and nstimuli-1 and the best consensus is returned.

For `type="cutree"`, the initialisation step is based on cutting the tree generated by clustering the stimuli. For `type="fusion"`, the initialisation step is based on the fusion algorithm. In this case, results are more accurate but the algorithm might be time consuming. For `type="medoid"`, the consensus is the closest partition to all the partitions given by subjects.

For ` optim=TRUE`, a transfer step is performed after the initialisation step.

## Value

List of following components:

 `Consensus ` Consensus `Crit ` Criterion for consensus

## References

Krieger & Green (1999) J. of Classification, 16:63-89

## Examples

 ```1 2 3 4 5 6 7 8 9``` ``` data(AromaSort) Aroma<-SortingPartition(AromaSort) res<-ConsensusPartition(Aroma,ngroups=0,type="cutree") res ##res<-ConsensusPartition(Aroma,ngroups=0,type="fusion",optim=TRUE) ##res ##res<-ConsensusPartition(Aroma,type="medoid") ##res ```

FreeSortR documentation built on May 2, 2019, 2:47 p.m.