bnboot: Nonparametric bootstrap of Bayesian networks

In bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference

Nonparametric bootstrap of Bayesian networks

Description

Apply a user-specified function to the Bayesian network structures learned from bootstrap samples of the original data.

Usage

bn.boot(data, statistic, R = 200, m = nrow(data), algorithm,
  algorithm.args = list(), statistic.args = list(), cluster,
  debug = FALSE)

Arguments

data	a data frame containing the variables in the model.
statistic	a function or a character string (the name of a function) to be applied to each bootstrap replicate.
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. See structure learning and the documentation of each algorithm for details.
algorithm.args	a list of extra arguments to be passed to the learning algorithm.
statistic.args	a list of extra arguments to be passed to the function specified by statistic.
cluster	an optional cluster object from package parallel.
debug	a boolean value. If TRUE a lot of debugging output is printed; otherwise the function is completely silent.

Details

The first argument of statistic is the bn object encoding the network structure learned from the bootstrap sample; the arguments specified in statistics.args are extracted from the list and passed to statistics as the 2nd, 3rd, etc. arguments.

Value

A list containing the results of the calls to statistic.

Author(s)

Marco Scutari

References

Friedman N, Goldszmidt M, Wyner A (1999). "Data Analysis with Bayesian Networks: A Bootstrap Approach". Proceedings of the 15th Annual Conference on Uncertainty in Artificial Intelligence, 196–201.

See Also

bn.cv, rbn.

Examples

## Not run: 
data(learning.test)
bn.boot(data = learning.test, R = 2, m = 500, algorithm = "gs",
  statistic = arcs)

## End(Not run)

bnlearn documentation built on Sept. 11, 2024, 8:27 p.m.