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ci.test {bnlearn} | R Documentation |

## Independence and conditional independence tests

### Description

Perform an independence or a conditional independence test.

### Usage

```
ci.test(x, y, z, data, test, B, debug = FALSE)
```

### Arguments

`x` |
a character string (the name of a variable), a data frame, a numeric vector or a factor object. |

`y` |
a character string (the name of another variable), a numeric vector or a factor object. |

`z` |
a vector of character strings (the names of the conditioning variables), a numeric vector, a factor
object or a data frame. If |

`data` |
a data frame containing the variables to be tested. |

`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 |

`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 |

### Value

An object of class `htest`

containing the following components:

`statistic` |
the value the test statistic. |

`parameter` |
the degrees of freedom of the approximate chi-squared or t distribution of the test statistic; the number of permutations computed by Monte Carlo tests. Semiparametric tests have both. |

`p.value` |
the p-value for the test. |

`method` |
a character string indicating the type of test performed, and whether Monte Carlo simulation or continuity correction was used. |

`data.name` |
a character string giving the name(s) of the data. |

`null.value` |
the value of the test statistic under the null hypothesis, always 0. |

`alternative` |
a character string describing the alternative hypothesis. |

### Author(s)

Marco Scutari

### See Also

`independence tests`

, `arc.strength`

.

### Examples

```
data(gaussian.test)
data(learning.test)
# using a data frame and column labels.
ci.test(x = "F" , y = "B", z = c("C", "D"), data = gaussian.test)
# using a data frame.
ci.test(gaussian.test)
# using factor objects.
attach(learning.test)
ci.test(x = F , y = B, z = data.frame(C, D))
```

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