ego.extract
(Package: sna) :
Extract Egocentric Networks from Complete Network Data
ego.extract takes one or more input graphs (dat) and returns a list containing the egocentric networks centered on vertices named in ego, using adjacency rule neighborhood to define inclusion.
● Data Source:
CranContrib
● Keywords: graphs, math
● Alias: ego.extract
●
0 images
cugtest
(Package: sna) :
Perform Conditional Uniform Graph (CUG) Hypothesis Tests for Graph-Level Indices
cugtest tests an arbitrary GLI (computed on dat by FUN) against a conditional uniform graph null hypothesis, via Monte Carlo simulation. Some variation in the nature of the conditioning is available; currently, conditioning only on size, conditioning jointly on size and estimated tie probability (via expected density), and conditioning jointly on size and (bootstrapped) edge value distributions are implemented. Note that fair amount of flexibility is possible regarding CUG tests on functions of GLIs (Anderson et al., 1999). See below for more details.
● Data Source:
CranContrib
● Keywords: graphs, htest, math
● Alias: cugtest
●
0 images
flowbet takes one or more graphs (dat) and returns the flow betweenness scores of positions (selected by nodes) within the graphs indicated by g. Depending on the specified mode, flow betweenness on directed or undirected geodesics will be returned; this function is compatible with centralization, and will return the theoretical maximum absolute deviation (from maximum) conditional on size (which is used by centralization to normalize the observed centralization score).
cug.test takes an input network and conducts a conditional uniform graph (CUG) test of the statistic in FUN, using the conditioning statistics in cmode. The resulting test object has custom print and plot methods.
● Data Source:
CranContrib
● Keywords: graphs, htest, math
● Alias: cug.test, plot.cug.test, print.cug.test
●
0 images
bbnam.bf
(Package: sna) :
Estimate Bayes Factors for the bbnam
This function uses monte carlo integration to estimate the BFs, and tests the fixed probability, pooled, and pooled by actor models. (See bbnam for details.)