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| bn.boot {bnlearn} | R Documentation |
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
|
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 |
cluster |
an optional cluster object from package parallel. |
debug |
a boolean value. If |
Details
The first argument of statistic is the bn object encoding the network
structure learned from the bootstrap sample. The arguments specified in statistic.args are
extracted from the list and passed to statistic as subsequent 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
Examples
## Not run:
data(learning.test)
bn.boot(data = learning.test, R = 2, m = 500, algorithm = "gs",
statistic = arcs)
## End(Not run)
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