network.stability {bootcluster}  R Documentation 
Estimate of detect module stability
network.stability( data.input, threshold, B = 20, cor.method, large.size, PermuNo, scheme_2 = FALSE )
data.input 
a 
threshold 
a 
B 
number of bootstrap resamplings 
cor.method 
the correlation method applied to the data set,three method are available: 
large.size 
the smallest set of modules, the 
PermuNo 
number of random graphs for null 
scheme_2 

This function estimates the modules' stability through bootstrapping approach for the given threshold. The approach to stability estimation is to compare the module composition of the reference correlation graph to the various bootstrapped correlation graphs, and to assess the stability at the (1) nodelevel, (2) modulelevel, and (3) overall.
stabilityresult
a list
of result for nodeswise stability
modularityresult
list
of modularity information with the given threshold
jaccardresult
list
estimated unconditional observed stability and
the estimates of expected stability under the null
originalinformation
list
information for original data,
igraph object and adjacency matrix constructed with the given threshold
Mingmei Tian
A framework for stabilitybased module detection in correlation graphs. Mingmei Tian,Rachael Hageman Blair,Lina Mu, Matthew Bonner, Richard Browne and Han Yu.
set.seed(1) data(wine) x0 < wine[1:50,] mytest<network.stability(data.input=x0,threshold=0.7, B=20, cor.method='pearson',large.size=0, PermuNo = 10, scheme_2 = FALSE)