CompareInteractive: Interactive comparison of clustering results for a specific...
In IntClust: Integration of Multiple Data Sets with Clustering Techniques

Description Usage Arguments Value Examples

A visual comparison of all methods is handy to see which objects will always cluster together independent of the applied methods. The function CompareInteractive plots the comparison over the specified methods. A cluster or method can than be identified by clicking and is plotted separately against the single source or other specified methods.

CompareInteractive(ListM, ListS, nrclusters = NULL, cols = NULL,
  fusionsLogM = FALSE, fusionsLogS = FALSE, weightclustM = FALSE,
  weightclustS = FALSE, namesM = NULL, namesS = NULL, marginsM = c(2,
  2.5, 2, 2.5), marginsS = c(8, 2.5, 2, 2.5), Interactive = TRUE, n = 1)

`ListM`	A list of the multiple source clustering or other methods to be compared and from which a cluster or method will be identified. The first element of the list will be used as the reference in `ReorderToReference`.
`ListS`	A list of the single source clustering or other methods the identified result will be compared to. The first element of the list will be used as the reference in `ReorderToReference`.
`nrclusters`	The number of clusters to cut the dendrogram in. Default is NULL.
`cols`	A character vector with the names of the colours. Default is NULL.
`fusionsLogM`	The fusionsLog parameter for the elements in ListM. To be handed to `ReorderToReference`. Default is FALSE.
`fusionsLogS`	The fusionslog parameter for the elements in ListS. To be handed to `ReorderToReference`. Default is FALSE.
`weightclustM`	The weightclust parameter for the elements in ListM. To be handed to `ReorderToReference`. Default is FALSE.
`weightclustS`	The weightclust parameter for the elements in ListS. To be handed to `ReorderToReference`. Default is FALSE.
`namesM`	Optional. Names of the multiple source clusterings to be used as labels for the columns. Default is NULL.
`namesS`	Optional. Names of the single source clusterings to be used as labels for the columns. Default is NULL.
`marginsM`	Optional. Margins to be used for the plot for the elements is ListM after the identification. Default is c(2,2.5,2,2.5).
`marginsS`	Optional. Margins to be used for the plot for the elements is ListS after the identification. Default is c(8,2.5,2,2.5).
`Interactive`	Optional. Do you want an interactive plot? Defaults to TRUE, if not the function provides the same as `ComparePlot` for the elements in ListM. Default is TRUE.
`n`	The number of methods/clusters you want to identify. Default is 1.

The returned value is a plot of the comparison of the elements of ListM. On this plot multiple clusters and/or methods can be identified. Click on a cluster of a specific method to see how that cluster of that method compares to the elements in ListS. Click left next to a row to identify a all cluster of a specific method. A new plotting window will appear for every identification.

## Not run: 
data(fingerprintMat)
data(targetMat)
data(Colors1)

MCF7_F = Cluster(fingerprintMat,type="data",distmeasure="tanimoto",normalize=FALSE,
method=NULL,clust="agnes",linkage="flexible",gap=FALSE,maxK=55,StopRange=FALSE)
MCF7_T = Cluster(targetMat,type="data",distmeasure="tanimoto",normalize=FALSE,
method=NULL,clust="agnes",linkage="flexible",gap=FALSE,maxK=55,StopRange=FALSE)

L=list(fingerprintMat,targetMat)

MCF7_W=WeightedClust(List=L,type="data",distmeasure=c("tanimoto","tanimoto"),
normalize=c(FALSE,FALSE),method=c(NULL,NULL),weight=seq(1,0,-0.1),weightclust=0.5,
clust="agnes",linkage="ward",StopRange=FALSE)

ListM=list(MCF7_W)
namesM=c(seq(1.0,0.0,-0.1))

ListS=list(MCF7_F,MCF7_T)
namesS=c("FP","TP")

CompareInteractive(ListM,ListS,nrclusters=7,cols=Colors1,fusionsLogM=FALSE,
fusionsLogS=FALSE,weightclustM=FALSE,weightclustS=TRUE,namesM,namesS,
marginsM=c(2,2.5,2,2.5),marginsS=c(8,2.5,2,2.5),Interactive=TRUE,n=1)

## End(Not run)