Description Usage Arguments Details Value Author(s) Examples
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).
1 2 3 | ClusterRowCoverage(result, matrix, maxCluster = 20, noise = 0.1,
noise_select = 0, plots = c(1:3), verbose = TRUE,
plot.type = "device", filename = "RowCoverage")
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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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ## Not run:
## Prepare some data ##
set.seed(254)
mat <- matrix(sample(c(0,1),5000*50,replace=TRUE,prob=c(1-0.15,0.15)),
nrow=5000,ncol=50)
mat[1:200,1:10] <- matrix(sample(c(0,1),200*10,replace=TRUE,prob=c(1-0.9,0.9)),
nrow=200,ncol=10)
mat[300:399,6:15] <- matrix(sample(c(0,1),100*10,replace=TRUE,prob=c(1-0.9,0.9)),
nrow=100,ncol=10)
mat[400:599,21:30] <- matrix(sample(c(0,1),200*10,replace=TRUE,prob=c(1-0.9,0.9)),
nrow=200,ncol=10)
mat[700:799,29:38] <- matrix(sample(c(0,1),100*10,replace=TRUE,prob=c(1-0.9,0.9)),
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)
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