Exponential empirical likelihood for a one sample mean vector hypothesis testing {Compositional}R Documentation

Exponential empirical likelihood for a one sample mean vector hypothesis testing

Description

Exponential empirical likelihood for a one sample mean vector hypothesis testing.

Usage

eel.test1(x, mu, tol = 1e-06, R = 1)

Arguments

x

A matrix containing Euclidean data.

mu

The hypothesized mean vector.

tol

The tolerance value used to stop the Newton-Raphson algorithm.

R

The number of bootstrap samples used to calculate the p-value. If R = 1 (default value), no bootstrap calibration is performed

Details

Multivariate hypothesis test for a one sample mean vector. This is a non parametric test and it works for univariate and multivariate data.

Value

A list including:

p

The estimated probabilities.

lambda

The value of the Lagrangian parameter λ.

iter

The number of iterations required by the newton-Raphson algorithm.

info

The value of the log-likelihood ratio test statistic along with its corresponding p-value.

runtime

The runtime of the process.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

References

Jing Bing-Yi and Andrew TA Wood (1996). Exponential empirical likelihood is not Bartlett correctable. Annals of Statistics 24(1): 365-369.

Owen A. B. (2001). Empirical likelihood. Chapman and Hall/CRC Press.

See Also

el.test1, hotel1T2, james, hotel2T2, maov, el.test2, comp.test

Examples

x <- Rfast::rmvnorm(100, numeric(10), diag( rexp(10, 0.5) ) )
eel.test1(x, numeric(10) )
el.test1(x, numeric(10) )

[Package Compositional version 5.2 Index]