reliaR (version 0.2)

ks.moew: Test of Kolmogorov-Smirnov for the Marshall-Olkin Extended Exponential(MOEW) distribution

Description

The function ks.moew() gives the values for the KS test assuming a MOEW with shape parameter alpha and tilt parameter lambda. 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.moew(x, alpha.est, lambda.est, 
    alternative = c("less", "two.sided", "greater"), plot = FALSE, ...)

Value

The function ks.moew() carries out the KS test for the MOEW

Arguments

x

vector of observations.

alpha.est

estimate of the parameter alpha

lambda.est

estimate of the parameter lambda

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.

References

Marshall, A. W., Olkin, I. (1997). A new method for adding a parameter to a family of distributions with application to the Weibull and Weibull families. Biometrika,84(3):641-652.

Marshall, A. W., Olkin, I. (2007). Life Distributions: Structure of Nonparametric, Semiparametric, and Parametric Families. Springer, New York.

See Also

pp.moew for PP plot and qq.moew for QQ plot

Examples

## Load data sets
data(sys2)
## Maximum Likelihood(ML) Estimates of alpha & lambda for the data(sys2)
## alpha.est = 0.3035937,  lambda.est = 279.2177754

ks.moew(sys2, 0.3035937, 279.2177754, alternative = "two.sided", plot = TRUE)