CompareSvsM: Comparison of clustering results for the single and multiple...
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 CompareSvsM plots the ComparePlot of the single source clustering results on the left and that of the multiple source clustering results on the right such that a visual comparison is possible.

CompareSvsM(ListS, ListM, nrclusters = NULL, cols = NULL,
  fusionsLogS = FALSE, fusionsLogM = FALSE, weightclustS = FALSE,
  weightclustM = FALSE, namesS = NULL, namesM = NULL, margins = c(8.1,
  3.1, 3.1, 4.1), plottype = "new", location = NULL)

`ListS`	A list of the outputs from the single source clusterings to be compared. The first element of the list will be used as the reference in `ReorderToReference`.
`ListM`	A list of the outputs from the multiple source clusterings to be compared. The first element of the list will be used as the reference.
`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.
`fusionsLogS`	The fusionslog parameter for the elements in ListS. To be handed to `ReorderToReference`. Default is FALSE.
`fusionsLogM`	The fusionsLog 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.
`weightclustM`	The weightclust parameter for the elements in ListM. To be handed to `ReorderToReference`. Default is FALSE.
`namesS`	Optional. Names of the single source clusterings to be used as labels for the columns. Default is NULL.
`namesM`	Optional. Names of the multiple source clusterings to be used as labels for the columns. Default is NULL.
`margins`	Optional. Margins to be used for the plot. Default is c(8.1,3.1,3.1,4.1).
`plottype`	Should be one of "pdf","new" or "sweave". If "pdf", a location should be provided in "location" and the figure is saved there. If "new" a new graphic device is opened and if "sweave", the figure is made compatible to appear in a sweave or knitr document, i.e. no new device is opened and the plot appears in the current device or document. Default is "new".
`location`	If plottype is "pdf", a location should be provided in "location" and the figure is saved there. Default is NULL.

The returned value is a plot with on the left the comparison over the objects in ListS and on the right a comparison over the objects in ListM.

## 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=seq(1.0,0.0,-0.1)

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

CompareSvsM(ListS,ListM,nrclusters=7,cols=Colors1,fusionsLogS=FALSE,
fusionsLogM=FALSE,weightclustS=FALSE,weightclustM=FALSE,namesS,
namesM,plottype="new",location=NULL)

## End(Not run)