reliaR (version 0.01)

ks.chen: Test of Kolmogorov-Smirnov for the Chen distribution

Description

The function ks.chen() gives the values for the KS test assuming the Chen distribution with shape parameter beta and scale 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.chen(x, beta.est, lambda.est,  alternative = c("less", "two.sided", "greater"), plot = FALSE, ...)

Arguments

x

vector of observations.

beta.est

estimate of the parameter beta

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.

Value

The function ks.chen() carries out the KS test for the Chen.

Details

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

References

Castillo, E., Hadi, A.S., Balakrishnan, N. and Sarabia, J.M.(2004). Extreme Value and Related Models with Applications in Engineering and Science, John Wiley and Sons, New York.

Chen, Z.(2000). A new two-parameter lifetime distribution with bathtub shape or increasing failure rate function, Statistics and Probability Letters, 49, 155-161.

Pham, H. (2003). Handbook of Reliability Engineering, Springer-Verlag.

See Also

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

Examples

## Load data sets
data(sys2)
## Estimates of beta & lambda using 'maxLik' package
## beta.est = 0.262282404, lambda.est = 0.007282371

ks.chen(sys2, 0.262282404, 0.007282371, alternative = "two.sided", plot = TRUE)