tests | R Documentation |
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_permutation(
.data,
FUN,
...,
times = 1000,
strategy = "sequential",
verbose = FALSE
)
.data |
A manynet-consistent network.
See e.g. |
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, |
strategy |
If |
verbose |
Whether the function should report on its progress.
By default FALSE.
See |
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)
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