run_fwer_sim | R Documentation |

Run a MC simulation study on family-wise error rates (FWERs) for the Holm and Romano & Wolf Methods multiple hypothesis adjustment methods given true null effects

run_fwer_sim( n_sims = 100, rho = c(0, 0.25, 0.5, 0.75), seed = 114411, B = 499, N = 1000, s = 6, G = 20 )

`n_sims` |
The number of Monte Carlo iterations. 100 by default. |

`rho` |
The correlation between the outcome variables. Vectorized c(0, 0.25, 0.5, .75) by default |

`seed` |
A random seed. |

`B` |
The number of bootstrap draws. 499 by default. |

`N` |
The number of observations. 1000 by default. |

`s` |
The number of dependent variables. 6 by default. |

`G` |
The number of clusters. If NULL, no clustering. 20 by default |

A data frame containing familiy wise rejection rates for uncorrected pvalues and corrected pvalues using Holm's and the Romano-Wolf method.

`reject_5` |
The family wise rejection rate at a 5% level |

`reject_10` |
The family wise rejection rate at a 10% level |

`rho` |
The correlation between the outcome variables. See function argument'rho' for more information. |

# N, B, n_sims, chosen so that the example runs quicker # for a higher quality simulation, increase all values res <- run_fwer_sim( seed = 123, n_sims = 10, B = 199, N = 100, s = 10, rho = 0 )

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