weibullness (version 1.23.8)

wp.test: The Weibullness Test from the Weibull Plot

Description

Performs the statistical test of Weibullness (Goodness-of-fit test for the Weibull distribution) using the sample correlation from the Weibull plot.

Usage

wp.test(x, a)

Value

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

statistic

the value of the test statistic (sample correlation from the Weibull plot)

p.value

the p-value for the test.

sample.size

sample size (missing observations are deleted).

method

a character string indicating the Weibullness 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 Weibullness test is constructed using the sample correlation which is calculated using the associated Weibull plot. The critical value is then looked up in Weibull.Plot.Quantiles. There is print method for class "htest".

References

Park, C. (2017). Weibullness test and parameter estimation of the three-parameter Weibull model using the sample correlation coefficient. International Journal of Industrial Engineering - Theory, Applications and Practice, 24(4), 376-391.
tools:::Rd_expr_doi("10.23055/ijietap.2017.24.4.2848")

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.

Examples

library(weibullness)

# For Weibullness hypothesis test. 
x = rweibull(10, shape=1)
wp.test(x)