bs.test {bsgof}

The Birnbaum-Saunders goodness-of-fit test from the probability plot

Description

Performs goodness-of-fit test for the Birnbaum-Saunders distribution

Usage

bs.test(x, a)
bs.test.pvalue(r, n)
bs.test.critical(alpha, n)

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.

r

the sample correlation coefficient from the Birnbaum-Saunders probability plot; r is in (0,1).

n

the sample size.

alpha

the significance level.

Details

Using the sample correlation coefficeint from the Birnbaum-Saunders probability plot, it performs the goodness fit test for the Birnbaum-Saunders distribution.

Value

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

statistic

the value of the test statistic (sample correlation from the Birnbaum-Saunders probability plot)

p.value

the p-value for the test.

sample.size

sample size (missing observations are deleted).

method

a character string indicating the goodness fit test for the Birnbaum-Saunders distribution.

data.name

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

Author(s)

Chanseok Park

References

Park, C. and M. Wang (2023). A goodness-of-fit test for the Birnbaum-Saunders distribution based on the probability plot. ArXiv e-prints, 2308.10150.
doi: 10.48550/arXiv.2308.10150

See Also

ks.test for performing a one- or two-sample Kolmogorov-Smirnov test.
shapiro.test for performing the Shapiro-Wilk test of normality.
wp.test{weibullness} for performing the Weibullness test.

Examples

# For the goodness of fit test
x = c(1.2, 2.0, 3.3)
bs.test(x)

# p.value with r (sample correlation from the probability plot) and n (sample size) are given
bs.test.pvalue(r=0.6, n=10)

# Critical value with alpha (significane level) and n (sample size).
bs.test.critical(alpha=0.01, n=10)