shapiro.exp.test: Shapiro-Wilk test for exponentiality
In exptest: Tests for Exponentiality

Description Usage Arguments Details Value Author(s) References Examples

Performs Shapiro-Wilk test for the composite hypothesis of exponentiality, see e.g. Shapiro and Wilk (1972).

1	shapiro.exp.test(x, nrepl=2000)

`x`	a numeric vector of data values.

nrepl

the number of replications in Monte Carlo simulation.

The Shapiro-Wilk test for exponentiality is based on the following statistic:

W = \frac{n(\overline{X} - X_{(1)})^2}{(n - 1)∑_{i=1}^n{(X_i - \overline{X})^2}}.

The p-value is computed by Monte Carlo simulation.

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

`statistic`	the value of the Shapiro-Wilk statistic.
`p.value`	the p-value for the test.
`method`	the character string "Shapiro-Wilk test for exponentiality".
`data.name`	a character string giving the name(s) of the data.

Alexey Novikov and Ruslan Pusev

Shapiro, S.S. and Wilk, M.B. (1972): An analysis of variance test for the exponential distribution (complete samples). — Technometrics, vol. 14, pp. 355-370.

1 2	shapiro.exp.test(rexp(100)) shapiro.exp.test(rchisq(100,1))

	Shapiro-Wilk test for exponentiality

data:  rexp(100)
W = 0.0099312, p-value = 0.416


	Shapiro-Wilk test for exponentiality

data:  rchisq(100, 1)
W = 0.0051621, p-value < 2.2e-16