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

## Description

The function `abic.logis.exp()` gives the `loglikelihood`, `AIC` and `BIC` values assuming an Logistic-Exponential(LE) distribution with parameters alpha and lambda.

## Usage

 `1` ```abic.logis.exp(x, alpha.est, lambda.est) ```

## Arguments

 `x` vector of observations `alpha.est` estimate of the parameter alpha `lambda.est` estimate of the parameter lambda

## Value

The function `abic.logis.exp()` 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.logis.exp` for `PP` plot and `qq.logis.exp` for `QQ` plot
 ```1 2 3 4 5 6 7 8``` ```## Load data sets data(bearings) ## Maximum Likelihood(ML) Estimates of alpha & lambda for the data(bearings) ## Estimates of alpha & lambda using 'maxLik' package ## alpha.est = 2.36754, lambda.est = 0.01059 ## Values of AIC, BIC and LogLik for the data(bearings) abic.logis.exp(bearings, 2.36754, 0.01059) ```