Description Usage Arguments Value

For a given dataset, performs a confidence-interval bootstrap of the mutual information or maximal information coefficient (MIC) for each pairwise association.

Computes the MI or MIC for each pairwise association.

Performs a bootstrap (of

`boots`

samples), and store each pairwise associationCalculate the 1th percentile for each pairwise association from the bootstrap distribution

If the percentile is inferior to the threshold of the corresponding pairwise variable type, then the MI or MIC is set to 0.

1 2 3 | ```
boot_cat_bin(obs_data, list_cat_var, list_bin_var, threshold_bin,
threshold_cat, threshold_bin_cat, method = c("mi", "mic"),
boots = 5000, show_progress = TRUE)
``` |

`obs_data` |
(data.frame or matrix) : a dataset which rows are observations and columns the variables. |

`list_cat_var` |
: list of the categorical variables of the dataset |

`list_bin_var` |
: list of the binary variables of the dataset |

`threshold_bin` |
: the threshold to apply to binary pairwise associations |

`threshold_cat` |
: the threshold to apply to categorical pairwise associations |

`threshold_bin_cat` |
: to apply to a pairwise association between a binary and a categorical variable |

`method` |
: the method to use to compute the adjacency matrix
("mi" or "mic").
If "mi", uses mutual information package |

`boots` |
: number of bootstraps (default 5000) |

`show_progress` |
: if TRUE, prints the percentage of completion to keep track of the algorithm's progress. Default is TRUE. Recommended to FALSE for RMarkdown files. |

The inferred adjacency matrix. All bootstrap 1th percentile values of each pairwise association inferior to their predefined thresholds will be set to 0.

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