Description Usage Arguments Details Value Author(s) References See Also Examples
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.
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dat 
one or more input graphs. 
FUN 
the function generating the test statistic; note that this must take a graph as its first argument, and return a single numerical value. 
mode 

cmode 
string indicating the type of conditioning to be applied. 
diag 
logical; should selfties be treated as valid data? 
reps 
number of Monte Carlo replications to use. 
ignore.eval 
logical; should edge values be ignored? (Note: 
FUN.args 
a list containing any additional arguments to 
cug.test
is an improved version of cugtest
, for use only with univariate CUG hypotheses. Depending on cmode
, conditioning on the realized size, edge count (or exact edge value distribution), or dyad census (or dyad value distribution) can be selected. Edges are treated as unvalued unless ignore.eval=FALSE
; since the latter setting is less efficient for sparse graphs, it should be used only when necessary.
A brief summary of the theory and goals of conditional uniform graph testing can be found in the reference below. See also cugtest
for a somewhat informal description.
An object of class cug.test
.
Carter T. Butts [email protected]
Butts, Carter T. (2008). “Social Networks: A Methodological Introduction.” Asian Journal of Social Psychology, 11(1), 13–41.
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