Test regular (gnp) vs configuration model

Likelihood ratio test for gnp vs configuration model.

conf.test(
  graph,
  directed,
  selfloops,
  nempirical = NULL,
  parallel = NULL,
  seed = NULL
)

Arguments

graph

adjacency matrix or igraph graph

directed

a boolean argument specifying whether object is directed or not.

selfloops

a boolean argument specifying whether the model should incorporate selfloops.

nempirical

optional, number of graphs to sample from null distribution for empirical distribution.

parallel

optional, number of cores to use or boolean for parallel computation. If passed TRUE uses all cores-1, else uses the number of cores passed. If none passed performed not in parallel.

seed

optional integer

Value

p-value of test.

Examples

data("adj_karate")
conf.test(graph = adj_karate, directed = FALSE, selfloops = FALSE, seed=123)
#> 
#> 	LR test -- gnp vs CM
#> 
#> data:  
#> lr = 299.79, df = 33, p-value < 2.2e-16
#> alternative hypothesis: one.sided
#> 95 percent confidence interval:
#>  19.63033 51.83806
#>