boot_dags: Infer Multiple Structures with Different Starting Graphs

In robertness/bninfo: Queries and information theoritic operations on Bayesian networks.

Description

Infers multiple structures from different starting graphs. In each iteration the data is resampled. If a whitelist is included in the inference algorithm arguments, random graphs are generated from the whitelist using preferential attachment. Currently not implemented for blacklists.

Usage

boot_dags(x, cluster = NULL, R = 200, m = nrow(data), algorithm,
  algorithm.args = list(), random.graph.args = list())

Arguments

cluster	an optional cluster object from package parallel.
R	a positive integer, the number of bootstrap replicates.
m	a positive integer, the size of each bootstrap replicate.
algorithm	a character string, the learning algorithm to be applied to the bootstrap replicates. Possible values are gs, iamb, fast.iamb, inter.iamb, mmpc, hc, tabu, mmhc and rsmax2. See bnlearn-package and the documentation of each algorithm for details.
algorithm.args	a list of extra arguments to be passed to the learning algorithm.
random.graph.args	arguments to be passed in random graph generation. If a whitelist is provided in algorithm.args, the arguments are used in preferential attachment. Otherwise they are passed to bnlearn's random.graph generation.
data	the data set targeted for inference.

robertness/bninfo documentation built on May 27, 2019, 10:32 a.m.