ks.gp.weibull: Test of Kolmogorov-Smirnov for the generalized power...

In reliaR: Comprehensive Tools for some Probability Distributions

Test of Kolmogorov-Smirnov for the generalized power Weibull(GPW) distribution

Description

The function ks.gp.weibull() gives the values for the KS test assuming a generalized power Weibull(GPW) with shape parameter alpha and scale parameter theta. In addition, optionally, this function allows one to show a comparative graph between the empirical and theoretical cdfs for a specified data set.

Usage

ks.gp.weibull(x, alpha.est, theta.est, 
    alternative = c("less", "two.sided", "greater"), plot = FALSE, ...)

Arguments

x	vector of observations.
alpha.est	estimate of the parameter alpha
theta.est	estimate of the parameter theta
alternative	indicates the alternative hypothesis and must be one of "two.sided" (default), "less", or "greater".
plot	Logical; if TRUE, the cdf plot is provided.
...	additional arguments to be passed to the underlying plot function.

Details

The Kolmogorov-Smirnov test is a goodness-of-fit technique based on the maximum distance between the empirical and theoretical cdfs.

Value

The function ks.gp.weibull() carries out the KS test for the generalized power Weibull(GPW)

References

Nikulin, M. and Haghighi, F. (2006). A Chi-squared test for the generalized power Weibull family for the head-and-neck cancer censored data, Journal of Mathematical Sciences, Vol. 133(3), 1333-1341.

Pham, H. and Lai, C.D. (2007). On recent generalizations of the Weibull distribution, IEEE Trans. on Reliability, Vol. 56(3), 454-458.

See Also

pp.gp.weibull for PP plot and qq.gp.weibull for QQ plot

Examples

## Load data sets
data(repairtimes)
## Maximum Likelihood(ML) Estimates of alpha & theta for the data(repairtimes)
## Estimates of alpha & theta using 'maxLik' package
## alpha.est = 1.566093, theta.est = 0.355321

ks.gp.weibull(repairtimes, 1.566093, 0.355321, alternative = "two.sided", plot = TRUE)

reliaR documentation built on Nov. 5, 2025, 6:57 p.m.