Description Usage Arguments Details Value References See Also Examples

Statistical power of soft-thresholding Fisher's p-value combination test under Gaussian mixture model.

1 | ```
power.soft(alpha, n, tau1, eps = 0, mu = 0)
``` |

`alpha` |
- type-I error rate. |

`n` |
- dimension parameter, i.e. the number of input p-values. |

`tau1` |
- truncation parameter=normalization parameter. tau1 > 0. |

`eps` |
- mixing parameter of the Gaussian mixture. |

`mu` |
- mean of non standard Gaussian model. |

We consider the following hypothesis test,

*H_0: X_i\sim F_0, H_a: X_i\sim (1-ε)F_0+ε F_1*

, where *ε* is the mixing parameter,
*F_0* is the standard normal CDF and *F = F_1* is the CDF of normal distribution with *μ* defined by mu and *σ = 1*.

Power of the soft-thresholding Fisher's p-value combination test.

1. Hong Zhang and Zheyang Wu. "TFisher Tests: Optimal and Adaptive Thresholding for Combining p-Values", submitted.

`stat.soft`

for the definition of the statistic.

1 2 3 | ```
alpha = 0.05
#If the alternative hypothesis Gaussian mixture with eps = 0.1 and mu = 1.2:#
power.soft(alpha, 100, 0.05, eps = 0.1, mu = 1.2)
``` |

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