gp.test {weibullness}

Gumbel Goodness-of-Fit Test from a Gumbel Probability Plot

Description

Performs the statistical goodness-of-fit test for the Gumbel distribution using the sample correlation from the Gumbel probability plot.

Usage

gp.test(x, a)

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().

Details

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

Value

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

statistic

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

p.value

the p-value for the test.

sample.size

sample size (missing observations are deleted).

method

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

data.name

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

Author(s)

Chanseok Park

References

Kinnison, R. (1989). Correlation Coefficient Goodness-of-Fit Test for the Extreme-Value Distribution. The American Statistician, 43(2), 98-100.

Vogel, R. M. and C. N. Kroll (1989). Low-Flow Frequency Analysis Using Probability-Plot Correlation Coefficients. Journal of Water Resources Planning and Management, 115, 338-357.

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.

Examples

# Gumbel goodness-of-fit test. 
x = c(-3.16, -3.07, -2.24, -1.8, -1.48, -0.92, -0.87, -0.41, -0.06, 1.15)
gp.test(x)