abic.gumbel: Akaike information criterion (AIC) and Bayesian information...

Description Usage Arguments Value References See Also Examples

View source: R/Gumbel.R

Description

The function abic.gumbel() gives the loglikelihood, AIC and BIC values assuming an Gumbel distribution with parameters mu and sigma.

Usage

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abic.gumbel(x, mu.est, sigma.est)

Arguments

x

vector of observations

mu.est

estimate of the parameter mu

sigma.est

estimate of the parameter sigma

Value

The function abic.gumbel() 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.gumbel for PP plot and qq.gumbel for QQ plot

Examples

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## Load data sets
data(dataset2)
## Maximum Likelihood(ML) Estimates of mu & sigma for the data(dataset2)
## Estimates of mu & sigma using 'maxLik' package
## mu.est = 212.157, sigma.est = 151.768

## Values of AIC, BIC and LogLik for the data(dataset2)
abic.gumbel(dataset2, 212.157, 151.768)

reliaR documentation built on May 29, 2017, 12:34 p.m.

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