Description Usage Arguments Details Value Author(s) See Also

Determining the importance of interactions found by `logic.bagging`

or `logicFS`

by Pearson's ChiSquare Statistic. Only available for the classification and the logistic
regression approach of logic regression.

1 |

`object` |
either an object of class |

`data` |
a data frame or matrix consisting of 0's and 1's in which each column corresponds
to one of the explanatory variables used in the original analysis with |

`cl` |
a numeric vector of 0's and 1's specifying the class labels of the observations in |

Currently Pearson's ChiSquare statistic is computed without continuity correction.

Contrary to `vim.logicFS`

(and `vim.norm`

and `vim.signperm`

),
`vim.chisq`

does neither take the logic regression models into acount nor uses the out-of-bag
observations for computing the importances of the identified interactions. It "just" tests each
of the found interactions on the whole data set by calculating Pearson's ChiSquare statistic for
each of these interactions. It is, therefore, highly recommended to use an independent data set
for specifying the importances of these interactions with `vim.chisq`

.

An object of class `logicFS`

containing

`primes` |
the prime implicants |

`vim` |
the values of Pearson's ChiSquare statistic, |

`prop` |
NULL, |

`type` |
NULL, |

`param` |
further parameters (if |

`mat.imp` |
NULL, |

`measure` |
"ChiSquare Based", |

`threshold` |
the 1 - 0.05/m quantile of the ChiSquare distribution with one degree of freedom, |

`mu` |
NULL. |

Holger Schwender, holger.schwender@hhu.de

`logic.bagging`

, `logicFS`

,
`vim.logicFS`

, `vim.norm`

, `vim.ebam`

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