Nonparametric bootstrap of Bayesian networks
Apply a user-specified function to the Bayesian network structures learned from bootstrap samples of the original data.
bn.boot(data, statistic, R = 200, m = nrow(data), algorithm, algorithm.args = list(), statistic.args = list(), cluster = NULL, debug = FALSE)
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 |
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
statitstics as the 2nd, 3rd, etc. arguments.
A list containing the results of the calls to statistic.
Marco Scutari
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.
## 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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