sg.bern.subgraph_train: Bernoulli Subgraph Train

In neurodata/subgraphing: Signal Subgraphs and Extensions

Description

sg.bern.xval_classifier Trains a Model for identification of the edges in a subgraph.

Usage

sg.bern.subgraph_train(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	[1] the number of edges in the subgraph.
coherent=FALSE	if FALSE, estimate an incoherent subgraph, otherwise an integer indicating the number of vertices in the coherent subgraph.
tstat="fisher"	the test statistic to use. options are fisher's exact ("fisher") and chi-squared ("chisq").

Value

edges [n x n] an ordering of the edges in the subgraph by test statistic results.

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

pi [c] the probability of seeing a given class.

See Also

sg.bern.compute_graph_statistics

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