BN_structure_learning: Compute the structure of the BN
In ComputationalIntelligenceGroup/3DSomaMS: 3DSomaMS: A univocal definition of the neuronal soma morphology using Gaussian mixture models
Description
Usage
Arguments
Value
Description
Compute the structure of a Dynamical Spatial Bayesian Network. Also apply bootstrap to choose the most significant arcs.
Usage
1
BN_structure_learning(somas, nboots, significant_arcs)
Arguments
somas
dataframe with the characterization of the somas
significant_arcs
a value between 0 and 1 that denotes a threshold over percentage of times that an arc appeared in the boostrap structures
n_boot
number of times that bootstrap learn the bayesian structure
Value
bayesian_model an object of class bn from bnlearn package that representates the structure of the DSBN
ComputationalIntelligenceGroup/3DSomaMS documentation built on May 6, 2019, 12:49 p.m.
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Description Usage Arguments Value
Description
Compute the structure of a Dynamical Spatial Bayesian Network. Also apply bootstrap to choose the most significant arcs.
Usage
1 | BN_structure_learning(somas, nboots, significant_arcs)
|
Arguments
somas |
dataframe with the characterization of the somas |
significant_arcs |
a value between 0 and 1 that denotes a threshold over percentage of times that an arc appeared in the boostrap structures |
n_boot |
number of times that bootstrap learn the bayesian structure |
Value
bayesian_model an object of class bn from bnlearn package that representates the structure of the DSBN
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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