R: Build a model string from a Bayesian network and vice versa

model string utilities {bnlearn} | R Documentation

## Build a model string from a Bayesian network and vice versa

### Description

Build a model string from a Bayesian network and vice versa.

### Usage

    modelstring(x)
    modelstring(x, debug = FALSE) <- value

    model2network(string, ordering = NULL, debug = FALSE)

    ## S3 method for class 'bn'
    as.character(x, ...)
    ## S3 method for class 'character'
    as.bn(x, ...)

### Arguments

`x` | an object of class `bn`. `modelstring()` (but not its replacement form) accepts also objects of class `bn.fit`.
`string` | a character string describing the Bayesian network.
`ordering` | the labels of all the nodes in the graph; their order is the node ordering used in the construction of the `bn` object. If `NULL` the nodes are sorted alphabetically.
`value` | a character string, the same as the `string`.
`debug` | a boolean value. If `TRUE` a lot of debugging output is printed; otherwise the function is completely silent.
`...` | extra arguments from the generic method (currently ignored).

### Details

The strings returned by `modelstringi()` have the same format as the ones returned by the `modelstring()` function in package deal; network structures may be easily exported to and imported from that package (via the `model2network` function).

The format of the model strings is as follows. The local structure of each node is enclosed in square brackets ("`[]`"); the first string is the label of that node. The parents of the node (if any) are listed after a ("`|`") and separated by colons ("`:`"). All nodes (including isolated and root nodes) must be listed.

### Value

`model2network()` and `as.bn()` return an object of class `bn`; `modelstring()` and `as.character.bn()` return a character string.

### Author(s)

Marco Scutari

### Examples

    data(learning.test)
    res = set.arc(gs(learning.test), "A", "B")
    res
    modelstring(res)
    res2 = model2network(modelstring(res))
    res2
    all.equal(res, res2)

[Package bnlearn version 4.4.1 Index]