weibullness (version 1.24.1)

ep.test: The Exponential Goodness-of-Fit Test from the Exponential Probability Plot

Description

Performs Goodness-of-fit test for the exponential distribution using the sample correlation from the exponential probability plot.

Usage

ep.test(x, a)

Value

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

statistic

the value of the test statistic (sample correlation from the exponential probability plot)

p.value

the p-value for the test.

sample.size

sample size (missing observations are deleted).

method

a character string indicating the exponential goodness-of-fit test.

data.name

a character string giving the name(s) of the data.

Arguments

x

a numeric vector of data values. Missing values are allowed, but the number of non-missing values must be between 3 and 1000.

a

the offset fraction to be used; typically in (0,1). See ppoints().

Author

Chanseok Park

Details

The exponential goodness-of-fit test is constructed using the sample correlation which is calculated using the associated exponential probability plot. The critical value is then looked up in Exponential.Plot.Quantiles. There is print method for class "htest".

References

Shapiro, S. S. and M. B. Wilk (1972). An Analysis of Variance Test for the Exponential Distribution (Complete Samples). Technometrics, 14(2), 355-370.

See Also

ks.test for performing the Kolmogorov-Smirnov test for the goodness of fit test of two samples.
shapiro.test for performing the Shapiro-Wilk test for normality.
wp.test for performing the Weibullness test based on the Weibull probability plot.

Examples

# For Exponential GOF. 
# Dataset from Section 2.5 of Shapiro and Wilk (1972).
x = c(6, 1, -4, 8, -2, 5, 0)
ep.test(x)