ClusterRowCoverage {BiBitR}  R Documentation 
Plotting function to be used with the BiBitWorkflow
output. It plots the number of clusters (of the hierarchical tree) versus the number/percentage of row coverage and number of final biclusters (see Details for more information).
ClusterRowCoverage(result, matrix, maxCluster = 20, noise = 0.1, noise_select = 0, plots = c(1:3), verbose = TRUE, plot.type = "device", filename = "RowCoverage")
result 
A BiBitWorkflow Object. 
matrix 
Accompanying binary data matrix which was used to obtain 
maxCluster 
Maximum number of clusters to cut the tree at (default=20). 
noise 
The allowed noise level when growing the rows on the merged patterns after cutting the tree. (default=

noise_select 
Should the allowed noise level be automatically selected for each pattern? (Using ad hoc method to find the elbow/kink in the Noise Scree plots)

plots 
Vector for which plots to draw:

verbose 
Logical value if the progress bar of merging/growing the biclusters should be shown. (default= 
plot.type 
Output Type

filename 
Base filename (with/without directory) for the plots if 
The graph of number of chosen tree clusters versus the final row coverage can help you to make a decision on how many clusters to choose in the hierarchical tree. The more clusters you choose, the smaller (albeit more similar) the patterns are and the more rows will fit your patterns (i.e. more row coverage).
A data frame containing the number of clusters and the corresponding number of row coverage, percentage of row coverage and the number of final biclusters.
Ewoud De Troyer
## Not run: ## Prepare some data ## set.seed(254) mat < matrix(sample(c(0,1),5000*50,replace=TRUE,prob=c(10.15,0.15)), nrow=5000,ncol=50) mat[1:200,1:10] < matrix(sample(c(0,1),200*10,replace=TRUE,prob=c(10.9,0.9)), nrow=200,ncol=10) mat[300:399,6:15] < matrix(sample(c(0,1),100*10,replace=TRUE,prob=c(10.9,0.9)), nrow=100,ncol=10) mat[400:599,21:30] < matrix(sample(c(0,1),200*10,replace=TRUE,prob=c(10.9,0.9)), nrow=200,ncol=10) mat[700:799,29:38] < matrix(sample(c(0,1),100*10,replace=TRUE,prob=c(10.9,0.9)), nrow=100,ncol=10) mat < mat[sample(1:5000,5000,replace=FALSE),sample(1:50,50,replace=FALSE)] ## Apply BiBitWorkflow ## out < BiBitWorkflow(matrix=mat,minr=50,minc=5,noise=0.2,cut_type="number",cut_pm=10) # Make ClusterRowCoverage Plots ClusterRowCoverage(result=out,matrix=mat,maxCluster=20,noise=0.2) ## End(Not run)