comp.rand.subnet: Comparing a sub network to the randomly simulated ones
In ProNet: Biological Network Construction, Visualization and Analyses

Description Usage Arguments Value References See Also Examples

Comparing a sub network with randomly simulated ones from the whole network.

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comp.rand.subnet(subgraph, graph, nsim = 1000, degree = FALSE,
  betweenness = FALSE, ave.path.len = FALSE, eccentricity = FALSE,
  cc = FALSE, method = "utest", FDR = 0.05)

`subgraph`	An igraph object.
`graph`	An igraph object. The whole one for random simulation.
`nsim`	Times for simulation. Default value is `1000`.
`degree`	Logical value, indicating whether to do vertex degree comparing (if `TRUE`) or not (if `FALSE`).
`betweenness`	Logical value, indicating whether to do betweenness comparing (if `TRUE`) or not (if `FALSE`).
`ave.path.len`	Logical value, indicating whether to do average path comparing (if `TRUE`) or not (if `FALSE`).
`eccentricity`	Logical value, indicating whether to do eccentricity comparing (if `TRUE`) or not (if `FALSE`).
`cc`	Logical value, indicating whether to do clustering coefficient comparing (if `TRUE`) or not (if `FALSE`).
`method`	Test method, currently only `utest` is supported.
`FDR`	False discovery rate. Default value is `0.05`.

A matrix of compared parameters and plots.

Y Benjamini, Y Hochberg. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological), Vol. 57, No. 1. (1995), pp. 289-300.

net.comparing, comp.subnet

g<-barabasi.game(100,power=0.8,directed=FALSE)
	subg<-induced.subgraph(g,sample(1:100,30))
	comp.rand.subnet(subg,g)
comp.rand.subnet(subg,g,degree=TRUE)