sg.bern.compute_graph_statistics: Bernoulli Subgraph Computation

In neurodata/subgraphing: Signal Subgraphs and Extensions

Description

sg.bern.compute_subgraph estimates the edges for a subgraph in a given set of samples.

Usage

sg.bern.compute_graph_statistics(samp, Y, e, coherent = FALSE,
  tstat = "fisher")

Arguments

samp	a list or array of graphs with arbitrary labelling. - if samp is a list, then it should have s elements of dimensions [n x n]. - if samp is an array, then it should be of dimensions [n x n x s].
Y	[s] the class labels.
e	the number of edges for the subgraph.
coherent=FALSE	a logical indicating whether to approximate a coherent, or incoherent, subgraph.
tstat="fisher"	the test statistic to use. options are fisher's exact ("fisher") and chi-squared ("chisq").

Value

subgraph [n x n] an array indicating whether an edge is present or not present in the subgraph.

p [n x n x c] the probability per edge of being connected per class for c classes.

See Also

sg.bern.graph_estimator sg.bern.subgraph_estimator sg.bern.subgraph_estimator

neurodata/subgraphing documentation built on May 21, 2019, 8:10 a.m.