Description Usage Arguments Details Value References Examples

Construct omnibus truncated product method statistic.

1 | ```
stat.tpm.omni(p, TAU1, M = NULL)
``` |

`p` |
- input p-values. |

`TAU1` |
- a vector of truncation parameters. Must be in non-descending order. |

`M` |
- correlation matrix of the input statistics. Default = NULL assumes independence. |

Let *x_{i}*, *i = 1,...,n* be a sequence of individual statistics with
correlation matrix M, *p_{i}* be the corresponding two-sided p-values, then the truncated product method statistics

*TPM_j = ∑_{i=1}^n -2\log(p_i)I(p_i≤qτ_{1j})*

, *j = 1,...,d*.
The omnibus test statistic is the minimum p-value of these truncated product method tests,

*W_o = min_j G_j(TPM_j)*

, where *G_j* is the survival function of *TPM_j*.

omni - omnibus truncated product method statistic.

pval - p-values of each truncated product method tests.

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

1 2 3 4 5 | ```
pval = runif(20)
TAU1 = c(0.01, 0.05, 0.5, 1)
stat.tpm.omni(p=pval, TAU1=TAU1)
M = matrix(0.3,20,20) + diag(1-0.3,20)
stat.tpm.omni(p=pval, TAU1=TAU1, M=M)
``` |

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