Description Usage Arguments Value Note Author(s) References Examples

Identify all the nodes relevant to compute all the conditional probability distributions for a given set of nodes.

1 |

`target` |
a vector of character strings, the labels of nodes whose conditional probability distributions are of interest. |

`context` |
a vector of character strings, the labels of nodes on which to condition the independence tests. |

`data` |
a data frame containing either numeric or factor columns. |

`test` |
a character string, the label of the conditional
independence test to be used in the algorithm.
If none is specified, the default test statistic is the |

`alpha` |
a numeric value, the target nominal type I error rate. If
none is specified, the default value is |

`B` |
a positive integer, the number of permutations considered
for each permutation test. It will be ignored with a warning if
the conditional independence test specified by the |

`debug` |
a boolean value. If |

`relevant`

returns a vector of character strings, the labels of the
relevant nodes.

This algorithms selects all the nodes that are relevant at all, not only
those that are significantly so. Therefore, to be discarded a node must
be completely unrelated to any of the `target`

nodes, not just weakly
dependent. On the good side, relevant nodes are correctly identified even
for data sets whose probability structure is not faithful to any directed
acyclic graph.

Marco Scutari

Pena JM, Nilsson R, Bjorkegren J, Tegner J (2006). "Identifying the Relevant Nodes Without Learning the Model". In "Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence (UAI2006)", pp. 367-374.

1 2 3 4 | ```
data(learning.test)
X = as.factor(sample(c("x1", "x2"), nrow(learning.test), replace = TRUE))
relevant("A", data = cbind(learning.test, X))
relevant("A", context = "B", data = learning.test,)
``` |

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