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global_test() uses permutation inference
and does not assume standard-normal scaling when its normalization
constant is one.global_test() runs the permutation test
for a difference between populations on its own: it returns the observed
statistic T, the proportion of label permutations with a smaller
statistic (the p-value), and the permutation null distribution, as an
object of class "global_test" with a print()
method. Until now this test was only available as the first step of
identify_critical_links(). It takes the populations,
n_permutations and an optional seed; the
normalisation constant a is fixed at 1, since the p-value
does not depend on it. The statistic is on the scale of
compute_test_statistic(), and with the same populations,
n_permutations and seed the p-value is exactly
the one identify_critical_links() records. The input must
be binary, as for identify_critical_links().global_test() and
identify_critical_links() (edge-wise decomposition of T and
the matrix-product null) now lives in one set of internal helpers.
Results are unchanged.identify_critical_links() now returns an object of
class "critical_links" with print(),
summary() and plot() methods, so the result
reports itself instead of having to be picked apart by hand.
print() gives the populations compared, the global-test
result and the most significant critical edges; summary()
adds the call, the settings the analysis ran with, and the node-level
ranking from get_critical_nodes(); plot() is a
method interface to plot_critical_edges() and takes the
original populations as its second argument.p_value (the permutation p-value of the global test
on the unmodified data, which was previously computed and discarded),
n_edges and settings, plus the matched
call. critical_edges,
edges_removed and modified_populations keep
their names, positions and contents, so existing code that extracts them
is unaffected.10.1038/s41598-018-21688-0, resolves to an unrelated
article; the correct one is
10.1038/s41598-018-23152-5.generate_category_graphs(0.7) used to fail with
attempt to select less than one element in integerOneIndex,
generate_random_graph(0.7) and
generate_community_graph(n_nodes = 0.7) returned a
degenerate 0 x 0 matrix, and a fractional n_graphs was
silently truncated. These now raise an error that names the offending
argument. The same applies to batch_size,
n_permutations, alpha and a in
identify_critical_links().identify_critical_links() now checks that
populations is a list of non-empty lists of equally sized
square binary matrices, as its documentation has always required.
Weighted matrices were previously passed to fisher.test(),
which rounded the resulting counts and returned a meaningless
ranking.compute_edge_pvalues() no longer subscripts out of
bounds for single-node networks.compute_test_statistic() returns an unnamed scalar; it
used to carry the name of the first population.
rank_edges() resets the row names of its output, so the
ranking reads 1, 2, 3, … rather than the positions the edges occupied
before sorting.lengths() in place of sapply(x, length), and
Reduce("+", x) / length(x) in place of an accumulation loop
in compute_central_graph()..Rbuildignore now excludes the manuscript sources, so
they are no longer included in the source tarball.compute_edge_pvalues() now clamps every returned
p-value to the valid probability range [0, 1]. On platforms
built without extended (long double) precision, exact tests such as
fisher.test() can return a value fractionally greater than
1 due to floating-point rounding, which caused a test failure under
CRAN’s noLD check. (Reported by the CRAN team.)plot_graph_with_communities() and
plot_graphs_grid() to streamline the API. The recommended
plotting function is plot_critical_edges(), which produces
a multi-panel visualisation of the analysis results.ggplotify,
gridExtra.grDevices::rainbow,
graphics::legend.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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