# BIC.clme: Bayesian information criterion In CLME: Constrained Inference for Linear Mixed Effects Models

## Description

Calculates the Bayesian information criterion for objects of class `clme`.

Calculates the Akaike and Bayesian information criterion for objects of class `clme`.

## Usage

 ```1 2 3 4 5``` ```## S3 method for class 'clme' BIC(object, ..., k = log(nobs(object)/(2 * pi))) ## S3 method for class 'summary.clme' BIC(object, ..., k = log(nobs(object)/(2 * pi))) ```

## Arguments

 `object` object of class `clme`. `...` space for additional arguments. `k` value multiplied by number of coefficients

## Details

The log-likelihood is assumed to be the Normal distribution. The model uses residual bootstrap methodology, and Normality is neither required nor assumed. Therefore the log-likelihood and these information criterion may not be useful measures for comparing models. For `k=2`, the function computes the AIC. To obtain BIC, set k = log( n/(2*pi) ); which the method `BIC.clme` does.

## Value

Returns the Bayesian information criterion (numeric).

`CLME-package` `clme`
`CLME-package` `clme`
 ```1 2 3 4 5 6 7 8``` ```data( rat.blood ) cons <- list(order = "simple", decreasing = FALSE, node = 1 ) clme.out <- clme(mcv ~ time + temp + sex + (1|id), data = rat.blood , constraints = cons, seed = 42, nsim = 0) BIC( clme.out ) BIC( clme.out, k=log( nobs(clme.out)/(2*pi) ) ) ```