g_test_scalar: Hypothesis test for graph statistics apply a graph statistic...
In mabafaba/frequentgraphs: Frequentist Hypothesis Tests For Graph Statistics
Description
Hypothesis test for graph statistics
apply a graph statistic to a graph, as well as randomised versions of it, and test the null-hypothesis that the statistic of the input graph was produced by a uniform random edge distribution.
Assumes normal distribution of the test statistic (returns normality test results along with p-values and results)
Usage
1
g_test_scalar(g, simulation.n = 100, scalar.graph.statistic, alpha = 0.05)
Arguments
g
igraph object
simulation.n
how many simulations to run
scalar.graph.statistic
a function that takes an igraph graph object as input and returns a scalar
alpha
the confidence level
mabafaba/frequentgraphs documentation built on Jan. 1, 2021, 8:28 a.m.
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Description
Hypothesis test for graph statistics apply a graph statistic to a graph, as well as randomised versions of it, and test the null-hypothesis that the statistic of the input graph was produced by a uniform random edge distribution. Assumes normal distribution of the test statistic (returns normality test results along with p-values and results)
Usage
1 | g_test_scalar(g, simulation.n = 100, scalar.graph.statistic, alpha = 0.05)
|
Arguments
g |
igraph object |
simulation.n |
how many simulations to run |
scalar.graph.statistic |
a function that takes an igraph graph object as input and returns a scalar |
alpha |
the confidence level |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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