stat.phi.omni {SetTest} | R Documentation |
calculate the omnibus phi-divergence statistics under general correlation matrix.
Description
calculate the omnibus phi-divergence statistics under general correlation matrix.
Usage
stat.phi.omni(
p,
M,
K0 = rep(1, 2),
K1 = rep(length(M[1, ]), 2),
S = c(1, 2),
t = 30,
onesided = FALSE,
method = "ecc",
ei = NULL
)
Arguments
p |
- input pvalues. |
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. |
t |
- numerical truncation parameter. |
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. |
References
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.
Examples
p.test = runif(10)
M = toeplitz(1/(1:10)*(-1)^(0:9)) #alternating polynomial decaying correlation matrix
stat.phi.omni(p.test, M=M, K0=rep(1,2), K1=rep(5,2), S=c(1,2))