TFisher (version 0.2.0)

stat.tpm.omni: Construct omnibus truncated product method statistic.

Description

Construct omnibus truncated product method statistic.

Usage

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

Arguments

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.

Value

omni - omnibus truncated product method statistic.

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

Details

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 $$TPM_j = \sum_{i=1}^n -2\log(p_i)I(p_i\leq\tau_{1j})$$, \(j = 1,...,d\). 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 \(TPM_j\).

References

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

Examples

# NOT RUN {
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)
# }