DIC function

Deviance Information Criterion (DIC)

Computes the Deviance Information Criterion (DIC), which is a generalization of the Akaike Information Criterion. Models with smaller DIC are considered to fit better than models with larger DIC.

DIC(object, ...)

Arguments

• object: an instance of class opm whose DIC is wanted.
• ...: further arguments passed to other methods.

Returns

a numeric value with the corresponding DIC

Details

DIC is defined as $DIC = 2*\bar{D} - D_\theta$

where: $\bar{D} = -2 mean(log-likelihood at parameter samples)$

$D_\theta = -2 * log(likelihood at expected value of parameters)$

DIC is calculated as: 2 * (-2 * mean(log-likelihood at each element of parameter samples)) -(-2 * log(likelihood at mean parameter sample value))

Note

Note the speed of computation of the DIC in proportional to the number of sampled values of the parameters in the opm object.