abic.weibull.ext: Akaike information criterion (AIC) and Bayesian information...

Description Usage Arguments Value References See Also Examples

View source: R/WeibullExt.R

Description

The function abic.weibull.ext() gives the loglikelihood, AIC and BIC values assuming an Weibull Extension(WE) distribution with parameters alpha and beta.

Usage

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abic.weibull.ext(x, alpha.est, beta.est)

Arguments

x

vector of observations

alpha.est

estimate of the parameter alpha

beta.est

estimate of the parameter beta

Value

The function abic.weibull.ext() gives the loglikelihood, AIC and BIC values.

References

Akaike, H. (1978). A new look at the Bayes procedure, Biometrika, 65, 53-59.

Claeskens, G. and Hjort, N. L. (2008). Model Selection and Model Averaging, Cambridge University Press, London.

Konishi., S. and Kitagawa, G.(2008). Information Criteria and Statistical Modeling, Springer Science+Business Media, LLC.

Schwarz, S. (1978). Estimating the dimension of the model, Annals of Statistics, 6, 461-464.

Spiegelhalter, D. J., Best, N. G., Carlin, B. P. and van der Linde, A. (2002). Bayesian measures of complexity and fit, Journal of the Royal Statistical Society Series B 64, 1-34.

See Also

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

Examples

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## Load data sets
data(sys2)
## Maximum Likelihood(ML) Estimates of alpha & beta for the data(sys2)
## Estimates of alpha & beta using 'maxLik' package
## alpha.est = 0.00019114, beta.est = 0.14696242

## Values of AIC, BIC and LogLik for the data(sys2)
abic.weibull.ext(sys2, 0.00019114, 0.14696242)

reliaR documentation built on May 1, 2019, 9:51 p.m.