# abic.gumbel: Akaike information criterion (AIC) and Bayesian information... In reliaR: Package for some probability distributions.

## Description

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

## Usage

 `1` ```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.

`pp.gumbel` for `PP` plot and `qq.gumbel` for `QQ` plot

## Examples

 ```1 2 3 4 5 6 7 8``` ```## 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) ```

### Example output

```\$LogLik
[1] -734.5823

\$AIC
[1] 1473.165

\$BIC
[1] 1478.584
```

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