R: Try to infer the direction of an undirected arc

choose.direction {bnlearn} | R Documentation

## Try to infer the direction of an undirected arc

### Description

Check both possible directed arcs for existence, and choose the one with the lowest p-value, the highest score or the highest bootstrap probability.

### Usage

    choose.direction(x, arc, data, criterion = NULL, ..., debug = FALSE)

### Arguments

`x` | an object of class `bn`.
`arc` | a character string vector of length 2, the labels of two nodes of the graph.
`data` | a data frame containing the data the Bayesian network was learned from.
`criterion` | a character string, the label of a score function, the label of an independence test or `bootstrap`. See `network scores` and `independence tests` for details on the first two possibilities.
`...` | additional tuning parameters for the network score. See `score` for details.
`debug` | a boolean value. If `TRUE` a lot of debugging output is printed; otherwise the function is completely silent.

### Details

If `criterion` is `bootstrap`, `choose.directions` accepts the same arguments as `boot.strength()`: `R` (the number of bootstrap replicates), `m` (the bootstrap sample size), `algorithm` (the structure learning algorithm), `algorithm.args` (the arguments to pass to the structure learning algorithm) and `cpdag` (whether to transform the network structure to the CPDAG representation of the equivalence class it belongs to).

If `criterion` is a test or a score function, any node connected to one of the nodes in `arc` by an undirected arc is treated as a parent of that node (with a warning).

### Value

`choose.direction` returns invisibly an updated copy of `x`.

### Author(s)

Marco Scutari

### See Also

`score`, `arc.strength`.

### Examples

    data(learning.test)
    res = gs(learning.test)

    ## the arc A - B has no direction.
    choose.direction(res, learning.test, arc = c("A", "B"), debug = TRUE)

    ## let's see score equivalence in action.
    choose.direction(res, learning.test, criterion = "aic",
      arc = c("A", "B"), debug = TRUE)

    ## arcs which introduce cycles are handled correctly.
    res = set.arc(res, "A", "B")
    # now A -> B -> E -> A is a cycle.
    choose.direction(res, learning.test, arc = c("E", "A"), debug = TRUE)

    ## Not run:
    choose.direction(res, learning.test, arc = c("D", "E"), criterion = "bootstrap",
      R = 100, algorithm = "iamb", algorithm.args = list(test = "x2"), cpdag = TRUE,
      debug = TRUE)

    ## End(Not run)

[Package bnlearn version 4.6 Index]