sg.bern.xval_classifier: Cross-Validated Bernoulli Subgraph Classifier

In neurodata/graphstats: Graph Statistics

Description

sg.bern.xval_classifier Bernoulli Subgraph Classifier with Cross Validation to determine the optimal subgraph.

Usage

sg.bern.xval_classifier(samp, Y, nedges, coherent = FALSE, tstat = "fisher",
  xval = "loo", folds = NaN)

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.
nedges	z an array where each element is the number of edges to look for, arbitrarily breaking ties as necessary.
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").
xval="loo"	the cross-validation options to use. Options are "loo" (leave-one-out) and "kfold" (K-fold).
folds=NaN	the number of folds to do if xval is set to kfold.

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.compute_graph_statistics

neurodata/graphstats documentation built on May 14, 2019, 5:19 p.m.