pphi.omni: calculate the left-tail probability of omnibus phi-divergence...

In SetTest: Group Testing Procedures for Signal Detection and Goodness-of-Fit

calculate the left-tail probability of omnibus phi-divergence statistics under general correlation matrix.

Description

calculate the left-tail probability of omnibus phi-divergence statistics under general correlation matrix.

Usage

pphi.omni(q, M, K0, K1, S, t = 30, onesided = FALSE, method = "ecc", ei = NULL)

Arguments

q	- quantile, must be a scalar.
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.

Value

Left-tail probability of omnibus phi-divergence statistics.

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

M = matrix(0.3,10,10) + diag(1-0.3, 10)
pphi.omni(0.05, M=M, K0=rep(1,2), K1=rep(5,2), S=c(1,2))

SetTest documentation built on Sept. 12, 2024, 7:41 a.m.