NoiseScree {BiBitR}  R Documentation 
Extract patterns from either a Biclust or BiBitWorkflow object (see Details) and plot the Noise Scree plot (same as plot 4 in BiBitWorkflow
). Additionally, if FisherResult
is available (from RowTest_Fisher
), this info will be added to the plot.
NoiseScree(result, matrix, type = c("Added", "Total"), pattern = NULL, noise_select = 0, alpha = 0.05)
result 
A Biclust or BiBitWorkflow Object. 
matrix 
Accompanying binary data matrix which was used to obtain 
type 
Either 
pattern 
Numeric vector for which patterns the noise scree plot should be drawn (default = all patterns). 
noise_select 
Should an automatic noise selection be applied and drawn (blue vertical line) on the plot? (Using ad hoc method to find the elbow/kink in the Noise Scree plots)

alpha 
If info from the Fisher Exact test is available, which significance level should be used to in the plot (Noise versus Significant Fisher Exact Test rows). (default=0.05) 
Using the column patterns of the Biclust result, the noise level is plotted versus the number of "Total"
or "Added"
rows.
The merged column patterns (after cutting the hierarchical tree) are extracted from the BiBitWorkflow object, namely the $info$MergedColPatterns
slot.
These patterns are used to plot the noise level versus the number of "Total"
or "Added"
rows.
If information on the Fisher Exact Test is available, then this info will added to the plot (noise level versus significant rows).
NULL
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=4) # Make Noise Scree Plot  Default NoiseScree(result=out,matrix=mat,type="Added") NoiseScree(result=out,matrix=mat,type="Total") # Make Noise Scree Plot  Use Automatic Noies Selection NoiseScree(result=out,matrix=mat,type="Added",noise_select=2) NoiseScree(result=out,matrix=mat,type="Total",noise_select=2) ## Apply RowTest_Fisher on BiBitWorkflow Object ## out2 < RowTest_Fisher(result=out,matrix=mat) # Fisher output is added to "NoiseScree" plot NoiseScree(result=out2,matrix=mat,type="Added") NoiseScree(result=out2,matrix=mat,type="Total") ## End(Not run)