These functions conduct tests of any network-level statistic:
test_random() performs a conditional uniform graph (CUG) test
of a measure against a distribution of measures on random networks
of the same dimensions.
test_permutation() performs a quadratic assignment procedure (QAP) test
of a measure against a distribution of measures on permutations
of the original network.
test_random(
.data,
FUN,
...,
times = 1000,
strategy = "sequential",
verbose = FALSE
)
test_configuration(
.data,
FUN,
...,
times = 1000,
strategy = "sequential",
verbose = FALSE
)
test_permutation(
.data,
FUN,
...,
times = 1000,
strategy = "sequential",
verbose = FALSE
)A manynet-consistent network.
See e.g. manynet::as_tidygraph() for more details.
A graph-level statistic function to test.
Additional arguments to be passed on to FUN, e.g. the name of the attribute.
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.
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.
Whether the function should report on its progress.
By default FALSE.
See {progressr} for more.
Other models:
regression,
test_distributions
marvel_friends <- to_unsigned(ison_marvel_relationships)
marvel_friends <- to_giant(marvel_friends) %>%
to_subgraph(PowerOrigin == "Human")
# (cugtest <- test_random(marvel_friends, manynet::net_heterophily, attribute = "Attractive",
# times = 200))
# plot(cugtest)
# (qaptest <- test_permutation(marvel_friends,
# manynet::net_heterophily, attribute = "Attractive",
# times = 200))
# plot(qaptest)