stat.soft.omni: Construct omnibus soft-thresholding Fisher's p-value...
In TFisher: Optimal Thresholding Fisher's P-Value Combination Method

Description Usage Arguments Details Value References Examples

Construct omnibus soft-thresholding Fisher's p-value combination statistic.

1	stat.soft.omni(p, TAU1, M = NULL)

`p`	- input p-values.
`TAU1`	- a vector of truncation parameters (=normalization 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 soft-thresholding statistics

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

, j = 1,...,d. The omnibus test statistic is the minimum p-value of these soft-thresholding tests,

W_o = min_j G_j(Soft_j)

, where G_j is the survival function of Soft_j.

omni - omnibus soft-thresholding statistic.

pval - p-values of each soft-thresholding tests.

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

pval = runif(20)
TAU1 = c(0.01, 0.05, 0.5, 1)
stat.soft.omni(p=pval, TAU1=TAU1)
M = matrix(0.3,20,20) + diag(1-0.3,20)
stat.soft.omni(p=pval, TAU1=TAU1, M=M)