bnlearn (version 2.6)

bn.boot: Parametric and nonparametric bootstrap of Bayesian networks

Description

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

Usage

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

Value

• A list containing the results of the calls to statistic.

References

Friedman N, Goldszmidt M, Wyner A (1999). "Data Analysis with Bayesian Networks: A Bootstrap Approach". In "UAI '99: Proceedings of the 15th Annual Conference on Uncertainty in Artificial Intelligence", pp. 196-20. Morgan Kaufmann.

See Also

bn.cv, rbn.

Examples

data(learning.test)
bn.boot(data = learning.test, R = 2, m = 500, algorithm = "gs",
  statistic = arcs)
# [[1]]
#      from to
# <arcs for the first replicate>
#
# [[2]]
#      from to
# <arcs for the second replicate>