sg.bern.graph_estimator: A function to fit a bernoulli distribution to the edges of...
In neurodata/subgraphing: Signal Subgraphs and Extensions
Description
Usage
Arguments
Value
See Also
Description
sg.bern.graph_estimator uses the method of moments to estimate the parameters of a beta
distribution, alpha and beta, for a collection of graphs.
Usage
1
sg.bern.graph_estimator(samp, thresh = 0, smooth = TRUE)
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].
thresh=0:
is the threshold below which we set edges to disconnected, and above which we set edges to connected.
smooth=TRUE:
whether to smooth p to avoid undesirable limits.
Value
p [n x n] the p parameter per edge representing the probability of an edge existing.
See Also
neurodata/subgraphing documentation built on May 21, 2019, 8:10 a.m.
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Description Usage Arguments Value See Also
Description
sg.bern.graph_estimator uses the method of moments to estimate the parameters of a beta
distribution, alpha and beta, for a collection of graphs.
Usage
1 | sg.bern.graph_estimator(samp, thresh = 0, smooth = TRUE)
|
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]. |
thresh=0: |
is the threshold below which we set edges to disconnected, and above which we set edges to connected. |
smooth=TRUE: |
whether to smooth p to avoid undesirable limits. |
Value
p [n x n] the p parameter per edge representing the probability of an edge existing.
See Also
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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