test_markov_dependence_learning: Performance of Causal Inference
In robertness/bninfo: Queries and information theoritic operations on Bayesian networks.
Description
This is specific to the dimensionality vignette. Simulates data from
a network, applies a learning algorithm, and counts the true and false postives of network
inference.
Usage
1
test_markov_dependence_learning(net, n, algo = gs)
Arguments
net
a bn.fit.gnet object. This won't work with a graph that simulates non-numeric data.
n
number of observations
robertness/bninfo documentation built on May 27, 2019, 10:32 a.m.
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Description
This is specific to the dimensionality vignette. Simulates data from a network, applies a learning algorithm, and counts the true and false postives of network inference.
Usage
1 | test_markov_dependence_learning(net, n, algo = gs)
|
Arguments
net |
a bn.fit.gnet object. This won't work with a graph that simulates non-numeric data. |
n |
number of observations |
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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