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, ...)an instance of class opm whose DIC is wanted.
further arguments passed to other methods.
a numeric value with the corresponding DIC
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))