Conditional uniform graph and permutation tests

Source: R/model_tests.R

These functions conduct conditional uniform graph (CUG) or permutation (QAP) tests of any graph-level statistic.

test_random(
  object,
  FUN,
  ...,
  times = 1000,
  strategy = "sequential",
  verbose = FALSE
)

test_permutation(
  object,
  FUN,
  ...,
  times = 1000,
  strategy = "sequential",
  verbose = FALSE
)

Arguments

object

An object of a migraph-consistent class:

  • matrix (adjacency or incidence) from {base} R

  • edgelist, a data frame from {base} R or tibble from {tibble}

  • igraph, from the {igraph} package

  • network, from the {network} package

  • tbl_graph, from the {tidygraph} package

FUN

A graph-level statistic function to test.

...

Additional arguments to be passed on to FUN, e.g. the name of the attribute.

times

Integer indicating number of simulations used for quantile estimation. (Relevant to the null hypothesis test only - the analysis itself is unaffected by this parameter.) Note that, as for all Monte Carlo procedures, convergence is slower for more extreme quantiles. By default, times=1000. 1,000 - 10,000 repetitions recommended for publication-ready results.

strategy

If {furrr} is installed, then multiple cores can be used to accelerate the function. By default "sequential", but if multiple cores available, then "multisession" or "multicore" may be useful. Generally this is useful only when times > 1000. See {furrr} for more.

verbose

Whether the function should report on its progress. By default FALSE. See {progressr} for more.

Functions

  • test_random(): Returns test results for some measure on an object against a distribution of measures on random networks of the same dimensions

  • test_permutation(): Returns test results for some measure on an object against a distribution of measures on permutations of the original network

See also

Other models: play, regression

Examples

marvel_friends <- to_unsigned(ison_marvel_relationships)
marvel_friends <- to_giant(marvel_friends) %>% 
  to_subgraph(PowerOrigin == "Human")
(cugtest <- test_random(marvel_friends, network_homophily, attribute = "Attractive",
  times = 200))
#> 
#>  CUG Test Results
#> 
#> Observed Value: -0.8571429 
#> Pr(X>=Obs): 1 
#> Pr(X<=Obs): 0 
#> 
plot(cugtest)

(qaptest <- test_permutation(marvel_friends, 
                network_homophily, attribute = "Attractive",
                times = 200))
#> 
#>  QAP Test Results
#> 
#> Observed Value: -0.8571429 
#> Pr(X>=Obs): 0.965 
#> Pr(X<=Obs): 0.07 
#> 
plot(qaptest)