TFisher (version 0.2.1)

power.tfisher: Statistical power of thresholding Fisher's p-value combination test under Gaussian mixture model.

Description

Statistical power of thresholding Fisher's p-value combination test under Gaussian mixture model.

Usage

power.tfisher(alpha, n, tau1, tau2, eps = 0, mu = 0)

Value

Power of the thresholding Fisher's p-value combination test.

Arguments

alpha

- type-I error rate.

n

- dimension parameter, i.e. the number of input p-values.

tau1

- truncation parameter. 0 < tau1 <= 1.

tau2

- normalization parameter. tau2 >= tau1.

eps

- mixing parameter of the Gaussian mixture.

mu

- mean of non standard Gaussian model.

Details

We consider the following hypothesis test, $$H_0: X_i\sim F_0, H_a: X_i\sim (1-\epsilon)F_0+\epsilon F_1$$ , where \(\epsilon\) is the mixing parameter, \(F_0\) is the standard normal CDF and \(F = F_1\) is the CDF of normal distribution with \(\mu\) defined by mu and \(\sigma = 1\).

References

1. Hong Zhang and Zheyang Wu. "TFisher Tests: Optimal and Adaptive Thresholding for Combining p-Values", submitted.

See Also

stat.tfisher for the definition of the statistic.

Examples

alpha = 0.05
#If the alternative hypothesis Gaussian mixture with eps = 0.1 and mu = 1.2:#
power.tfisher(alpha, 100, 0.05, 0.25, eps = 0.1, mu = 1.2)