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bn.boot {bnlearn}  R Documentation 
Nonparametric bootstrap of Bayesian networks
Description
Apply a userspecified 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 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
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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