Perform a goodness-of-fit test

gof.test(
  model,
  Beta = TRUE,
  nempirical = NULL,
  parallel = NULL,
  returnBeta = FALSE,
  seed = NULL
)

Arguments

model: ghype model to test
Beta: boolean, whether to use empirical Beta distribution approximation. Default TRUE
nempirical: optional scalar, number of replicates for empirical beta 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.
returnBeta: boolean, return estimated parameters of Beta distribution? Default FALSE.
seed: scalar, seed for the empirical distribution.

Value

p-value of test. If returnBeta=TRUE returns the p-value together with the parameters of the beta distribution.

Examples

data("adj_karate")
confmodel <- scm(graph = adj_karate, directed = FALSE, selfloops = FALSE)
gof.test(model = confmodel, seed = 123)
#> 
#> 	LR test -- GOF
#> 
#> data:  
#> lr = 649.84, df = 560, p-value < 2.2e-16
#> alternative hypothesis: one.sided
#> 95 percent confidence interval:
#>  339.1651 413.9994
#>