stat.tpm.omni: Construct omnibus truncated product method statistic.
Description Usage Arguments Details Value References Examples
Description
Construct omnibus truncated product method statistic.
Usage
1 | stat.tpm.omni(p, TAU1, M = NULL)
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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. |
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 = ∑_{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.
Value
omni - omnibus truncated product method statistic.
pval - p-values of each truncated product method tests.
References
1. Hong Zhang and Zheyang Wu. "TFisher Tests: Optimal and Adaptive Thresholding for Combining p-Values", submitted.
Examples
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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