AIC.clme: Akaike information criterion

In CLME: Constrained Inference for Linear Mixed Effects Models

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) ) )

CLME documentation built on July 8, 2020, 5:49 p.m.