neyman.unif.test: Neyman-Barton test for uniformity

In uniftest: Tests for Uniformity

Description

Performs Neyman-Barton test for the hypothesis of uniformity.

Usage

neyman.unif.test(x, nrepl=2000, k=5)

Arguments

x	a numeric vector of data values.

nrepl	the number of replications in Monte Carlo simulation.
k	the number of Legendre polynomials.

Details

The Neyman-Barton test for uniformity is based on the following statistic:

N_k = ∑_{j=1}^{k}{≤ft(\frac{1}{√{n}}∑_{i=1}^{n}{π_j(x_i)}\right)^2},

where π_j(x_i) are Legendre polynomials orthogonal on the interval [0,1].

The p-value is computed by Monte Carlo simulation.

Value

A list with class "htest" containing the following components:

statistic	the value of the Neyman-Barton statistic.
p.value	the p-value for the test.
method	the character string "Neyman-Barton test for uniformity".
data.name	a character string giving the name(s) of the data.

Author(s)

Maxim Melnik and Ruslan Pusev

References

Neyman J. "Smooth" test for goodness-of-fit // Scand. Aktuarietidsrift. 1937. V. 20. P. 149-199.

Examples

neyman.unif.test(runif(100,0,1))
neyman.unif.test(runif(100,0.1,0.9))

Example output

Loading required package: orthopolynom
Loading required package: polynom

	Neyman test for uniformity

data:  runif(100, 0, 1)
N = 4.2142, p-value = 0.5165


	Neyman test for uniformity

data:  runif(100, 0.1, 0.9)
N = 23.831, p-value < 2.2e-16

uniftest documentation built on May 1, 2019, 7:33 p.m.