dCommSignif is supposed to test the significance of communities
within a graph. For a community of the graph, it first calculates two
types of degrees for each node: degrees based on parters only within
the community itself, and the degrees based on its parters NOT in the
community but in the graph. Then, it performs two-sample Wilcoxon tests
on these two types of degrees to produce the signficance level
an object of class "igraph" or "graphNEL"
an object of class "communities". Details on this class can be found at http://igraph.org/r/doc/communities.html
significance: a vector of p-values (significance)
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# 1) generate an vector consisting of random values from beta distribution x <- rbeta(1000, shape1=0.5, shape2=1) # 2) fit a p-value distribution under beta-uniform mixture model fit <- dBUMfit(x, ntry=1, hist.bum=FALSE, contour.bum=FALSE) # 3) calculate the scores according to the fitted BUM and fdr=0.01 # using "pdf" method scores <- dBUMscore(fit, method="pdf", fdr=0.05, scatter.bum=FALSE) names(scores) <- as.character(1:length(scores)) # 4) generate a random graph according to the ER model g <- erdos.renyi.game(1000, 1/100) # 5) produce the induced subgraph only based on the nodes in query subg <- dNetInduce(g, V(g), knn=0) # 6) find the module with the maximum score module <- dNetFind(subg, scores) # 7) find the module and test its signficance comm <- walktrap.community(module, modularity=TRUE) significance <- dCommSignif(module, comm)
Loading required package: igraph Attaching package: 'igraph' The following objects are masked from 'package:stats': decompose, spectrum The following object is masked from 'package:base': union Loading required package: supraHex Loading required package: hexbin A total of p-values: 1000 Maximum Log-Likelihood: 328.8 Mixture parameter (lambda): 0.000 Shape parameter (a): 0.489
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