Description Usage Arguments Details Value References Examples
Construct omnibus thresholding Fisher's (TFisher) p-value combination statistic.
1 2 | stat.tfisher.omni(p, TAU1, TAU2, M = NULL, MU = NULL, SIGMA2 = NULL,
P0 = NULL)
|
p |
- input p-values from potentially correlated input sstatistics. |
TAU1 |
- a vector of truncation parameters. Must be in non-descending order. |
TAU2 |
- a vector of normalization parameters. Must be in non-descending order. |
M |
- correlation matrix of the input statistics. Default = NULL assumes independence |
MU |
- a vector of means of TFisher statistics. Default = NULL. |
SIGMA2 |
- a vector of variances of TFisher statistics. Default = NULL. |
P0 |
- a vector of point masses of TFisher statistics. Default = NULL. |
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 TFisher statistics
TFisher_j = ∑_{i=1}^n -2\log(p_i/τ_{2j})I(p_i≤qτ_{1j})
, j = 1,...,d. The omnibus test statistic is the minimum p-value of these thresholding tests,
W_o = min_j G_j(Soft_j)
, where G_j is the survival function of Soft_j.
omni - omnibus TFisher statistic.
pval - p-values of each TFisher tests.
1. Hong Zhang and Zheyang Wu. "TFisher Tests: Optimal and Adaptive Thresholding for Combining p-Values", submitted.
1 2 3 4 5 6 | pval = runif(20)
TAU1 = c(0.01, 0.05, 0.5, 1)
TAU2 = c(0.1, 0.2, 0.5, 1)
stat.tfisher.omni(p=pval, TAU1=TAU1, TAU2=TAU2)
M = matrix(0.3,20,20) + diag(1-0.3,20)
stat.tfisher.omni(p=pval, TAU1=TAU1, TAU2=TAU2, M=M)
|
$omni
[1] 0.4666071
$pvals
[1] 1.0000000 1.0000000 0.6071336 0.4666071
$omni
[1] 0.4488222
$pvals
[1] 1.0000000 1.0000000 0.5060848 0.4488222
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