EvidenceAccumulation: Evidence accumulation
In IntClust: Integration of Multiple Data Sets with Clustering Techniques

Description Usage Arguments Value References Examples

The Evidence Accumulation (EA, \insertCiteFred2005IntClust) sets up the same similarity matrix and applies a minimum spanning tree (MST,\insertCitePrim1957IntClust) algorithm to the corresponding graph. The algorithm will connect all objects with the shortest past. In order to recover the clusters, weak edges are cut at a threshold value $t$.

EvidenceAccumulation(List, type = c("data", "dist", "clust"),
  distmeasure = c("tanimoto", "tanimoto"), normalize = c(FALSE, FALSE),
  method = c(NULL, NULL), clust = "agnes", linkage = c("flexible",
  "flexible"), alpha = 0.625, nrclusters = c(7, 7), gap = FALSE,
  maxK = 15, graphPartitioning = c("MTS", "SL", "SL_agnes"), t = NULL)

`List`	A list of data matrices. It is assumed the rows are corresponding with the objects.
`type`	indicates whether the provided matrices in "List" are either data matrices, distance matrices or clustering results obtained from the data. If type="dist" the calculation of the distance matrices is skipped and if type="clusters" the single source clustering is skipped. Type should be one of "data", "dist" or"clusters".
`distmeasure`	A vector of the distance measures to be used on each data matrix. Should be one of "tanimoto", "euclidean", "jaccard", "hamming". Defaults to c("tanimoto","tanimoto").
`normalize`	Logical. Indicates whether to normalize the distance matrices or not, defaults to c(FALSE, FALSE) for two data sets. This is recommended if different distance types are used. More details on normalization in `Normalization`.
`method`	A method of normalization. Should be one of "Quantile","Fisher-Yates", "standardize","Range" or any of the first letters of these names. Default is c(NULL,NULL) for two data sets.
`clust`	Choice of clustering function (character). Defaults to "agnes".
`linkage`	Choice of inter group dissimilarity (character) for each data set. Defaults to c("flexible", "flexible") for two data sets.
`alpha`	The parameter alpha to be used in the "flexible" linkage of the agnes function. Defaults to 0.625 and is only used if the linkage is set to "flexible"
`nrclusters`	The number of clusters to divide each individual dendrogram in. Default is c(7,7) for two data sets.
`gap`	Logical. Whether the optimal number of clusters should be determined with the gap statistic. Defaults to FALSE.
`maxK`	The maximal number of clusters to investigate in the gap statistic. Default is 15.
`graphPartitioning`	The method to be performed: "MTS", "SL", "SL_agnes".
`t`	A threshold to cut weak edges. Default is NULL.

The returned value is a list of two elements:

`DistM`	A NULL object
`Clust`	The resulting clustering

The value has class 'Ensemble'.

\insertRef

Fred2005IntClust \insertRefPrim1957IntClust

data(fingerprintMat)
data(targetMat)
L=list(fingerprintMat,targetMat)

MCF7_EA=EvidenceAccumulation(List=L,type="data",distmeasure=c("tanimoto", "tanimoto"),
normalize=c(FALSE,FALSE),method=c(NULL,NULL),clust="agnes",linkage = c("flexible",
"flexible"),alpha=0.625,nrclusters=c(7,7),gap = FALSE, maxK = 15,graphPartitioning="MTS")