Description Usage Arguments Value Note Author(s) References See Also Examples

The function for calculating the adjusted p-values based on original available p-values and the attaianble p-values for the discrete test statistics.

1 | ```
MixBonf.p.adjust(pc, pd, pd.set, alpha, make.decision)
``` |

`pc` |
numeric vector of the available p-values (possibly with |

`pd` |
numeric vector of the available p-values (possibly with |

`pd.set` |
a list of numeric vectors, where each vector is the vector of all attainable p-values containing the available p-value for the corresponding hypothesis for discrete data. |

`alpha` |
significant level used to compare with adjusted p-values to make decisions, the default value is 0.05. |

`make.decision` |
logical; if |

A numeric vector of the adjusted p-values (of the same length as `p`

) if `make.decision = FALSE`

, or a list including original p-values, adjusted p-values and decision rules if `make.decision = TRUE`

.

The arguments include three parts, the available p-values need to be reorganized in advance. Gather all available p-values for continuous data as `pc`

, and all available p-values for discrete data as `pd`

. The attainable p-value refers to the element of domain set of p-value for the corresponding hypothesis for discrete test statistics, the p-value can only take finite values bewtween 0 and 1, that is, the attainable p-values for discrete case are finite and countable, so we can assign them in a finite list `pd.set`

. The function returns the adjusted p-values with the first part for continuous data of the same length as `pc`

, and second part for discrete data of the same length as `pd`

Yalin Zhu

Zhu, Y., & Guo, W. (2017).
Familywise error rate controlling procedures for discrete data
*arXiv preprint* arXiv:1711.08147.

`Tarone.p.adjust`

, `MBonf.p.adjust`

, `p.adjust`

.

1 2 3 4 5 |

MHTdiscrete documentation built on Dec. 16, 2018, 1:04 a.m.

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