ensemble: Ensemble Methods for Bicluster Algorithms

In biclust: BiCluster Algorithms

Ensemble Methods for Bicluster Algorithms

Description

Calculates an ensemble of biclusters from different parameter setting of possible different bicluster algorithms.

Usage

ensemble(x, confs, rep = 1, maxNum = 5, similar = jaccard2, thr = 0.8, simthr =0.7,
  subs = c(1, 1), bootstrap = FALSE, support = 0, combine=firstcome, ...)

Arguments

x	Data Matrix
confs	Matrix containing parameter sets
rep	Number of repetitions for each parameter set
maxNum	Maximum number of biclusters taken from each run
similar	Function to produce a similarity matrix of bicluster
thr	Threshold for similarity
simthr	Proportion of row column combinations in bicluster
subs	Vector of proportion of rows and columns for subsampling. Default c(1,1) means no subsampling.
bootstrap	Should bootstrap sampling be used (logical: replace=bootstrap).
support	Which proportion of the runs must contain the bicluster to have enough support to report it (between 0 and 1).
combine	Function to combine the single bicluster only firstcome and hcl for hierarchical clustering are possible at the moment.
...	Arguments past to the combine function.

Details

Two different kinds (or both combined) of ensembling is possible. Ensemble of repeated runs or ensemble of runs on subsamples.

Value

Return an object of class Biclust

Author(s)

Sebastian Kaiser [e-mail]

See Also

Biclust-class, plaid.grid, bimax.grid

Examples

## Not run: 
data(BicatYeast)
ensemble.plaid <- ensemble(BicatYeast,plaid.grid()[1:5],rep=1,maxNum=2, thr=0.5, subs = c(1,1))
ensemble.plaid
x <- binarize(BicatYeast)
ensemble.bimax <- ensemble(x,bimax.grid(),rep=10,maxNum=2,thr=0.5, subs = c(0.8,0.8))
ensemble.bimax

## End(Not run)

biclust documentation built on May 31, 2023, 6:18 p.m.