Description Usage Arguments Details Value Author(s) See Also Examples

This function simulates a matrix of permutation statistics, by performing a t test on normal data.

1 |

`prop` |
proportion of non-null hypotheses. |

`m` |
total number of variables. |

`B` |
number of permutations, including the identity. |

`rho` |
level of equicorrelation between pairs of variables. |

`n` |
number of observations. |

`alpha` |
significance level. |

`pw` |
power of the t test. |

`p` |
logical, |

`seed` |
seed. |

The function applies the one-sample two-sided t test to a matrix of simulated data,
for `B`

data permutations.
Data is obtained by simulating `n`

independent observations from a multivariate normal distribution,
where a proportion `prop`

of the variables has non-null mean.
This mean is such that the one-sample t test with significance level `alpha`

has power equal to `pw`

.
Each pair of distinct variables has equicorrelation `rho`

.

`simData`

returns a matrix where the `B`

rows correspond to permutations (the first is the identity),
and the `m`

columns correspond to variables.
The matrix contains p-values if `p`

is `TRUE`

, and t-scores otherwise.
The first columns (a proportion `prop`

) correspond to non-null hypotheses.

Anna Vesely.

True discovery guarantee: `sumStats`

, `sumPvals`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
# generate matrix of p-values for 5 variables and 10 permutations
G <- simData(prop = 0.6, m = 5, B = 10, alpha = 0.4, seed = 42)
# subset of interest (variables 1 and 2)
S <- c(1,2)
# create object of class sumObj
# combination: harmonic mean (Vovk and Wang with r = -1)
res <- sumPvals(G, S, alpha = 0.4, r = -1)
res
summary(res)
# lower confidence bound for the number of true discoveries in S
discoveries(res)
# lower confidence bound for the true discovery proportion in S
tdp(res)
# upper confidence bound for the false discovery proportion in S
fdp(res)
``` |

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