AIC.clme {CLME}

Akaike information criterion

Description

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

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

Usage

## S3 method for class 'clme'
AIC(object, ..., k = 2)

## S3 method for class 'summary.clme'
AIC(object, ..., k = 2)

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 information criterion (numeric).

See Also

CLME-package clme

CLME-package clme

Examples

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)

AIC( clme.out )
AIC( clme.out, k=log( nobs(clme.out)/(2*pi) ) )