View source: R/test.phi.omni.R
test.phi.omni | R Documentation |
calculate the right-tail probability of omnibus phi-divergence statistics under general correlation matrix.
test.phi.omni(prob, M, K0, K1, S, onesided = FALSE, method = "ecc", ei = NULL)
prob |
- vector of input p-values. |
M |
- correlation matrix of input statistics (of the input p-values). |
K0 |
- vector of search range starts (from the k0th smallest p-value). |
K1 |
- vector of search range ends (at the k1th smallest p-value). |
S |
- vector of the phi-divergence test parameters. |
onesided |
- TRUE if the input p-values are one-sided. |
method |
- default = "ecc": the effective correlation coefficient method in reference 2. "ave": the average method in reference 3, which is an earlier version of reference 2. The "ecc" method is more accurate and numerically stable than "ave" method. |
ei |
- the eigenvalues of M if available. |
p-value of the omnibus test.
p-values of the individual phi-divergence test.
1. Hong Zhang, Jiashun Jin and Zheyang Wu. "Distributions and power of optimal signal-detection statistics in finite case", IEEE Transactions on Signal Processing (2020) 68, 1021-1033 2. Hong Zhang and Zheyang Wu. "The general goodness-of-fit tests for correlated data", Computational Statistics & Data Analysis (2022) 167, 107379 3. Hong Zhang and Zheyang Wu. "Generalized Goodness-Of-Fit Tests for Correlated Data", arXiv:1806.03668.
M = matrix(0.3,10,10) + diag(1-0.3, 10)
test.phi.omni(runif(10), M=M, K0=rep(1,2), K1=rep(5,2), S=c(1,2))
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