R: Parametric and nonparametric bootstrap of Bayesian networks

bn.boot {bnlearn} | R Documentation

## Parametric and 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), sim = "ordinary",
      algorithm, algorithm.args = list(), statistic.args = list(),
      cluster = NULL, 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.
`sim` | a character string indicating the type of simulation required. Possible values are `"ordinary"` (the default, for nonparametric bootstrap) and `"parametric"`.
`algorithm` | a character string, the learning algorithm to be applied to the bootstrap replicates. Possible values are `gs`, `iamb`, `fast.iamb`, `inter.iamb`, `mmpc`, `hc`, `tabu`, `mmhc` and `rsmax2`. See `bnlearn-package` and 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 `statitstics` 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". In "UAI '99: Proceedings of the 15th Annual Conference on Uncertainty in Artificial Intelligence", pp. 196-201. Morgan Kaufmann.

### 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)

[Package bnlearn version 4.3 Index]