The function for calculating the adjusted p-values based on original available p-values and all attaianble p-values

1 | ```
MBonf.p.adjust(p, p.set, alpha, make.decision)
``` |

`p` |
numeric vector of p-values (possibly with |

`p.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. |

`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 attainable p-value refers to the element of domain set of p-value for the corresponding hypothesis. For continuous test statistics, the p-value under true null are uniform distributed in (0,1), thus the p-values are attainable everywhere between 0 and 1. But 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 `p.set`

.

Yalin Zhu

`Tarone.p.adjust`

, `MixBonf.p.adjust`

, `p.adjust`

.

1 2 3 |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.