dic: DIC
In effectFusion: Bayesian Effect Fusion for Categorical Predictors

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/dic.R

This function computes the DIC (deviance information criterion) for the estimated model in a fusion object.

dic(x)

`x`	an object of class `fusion`

The DIC can be easily computed from the MCMC output and is defined as DIC = 2 \overline{D(θ)} - D(\overline{θ}), where \overline{D(θ)} = \frac{1}{M} ∑ \limits_{m=1}^{M} D(θ^{(m)}) is the average posterior deviance and D(\bar{θ}) is the deviance evaluated at \bar{θ} = \frac{1}{M} ∑ \limits_{m=1}^{M} θ^{(m)}. θ^{(m)} are samples from the posterior of the model and M is the number of MCMC iterations.

The DIC for the estimated model in the fusion object.

Daniela Pauger, Magdalena Leitner <effectfusion.jku@gmail.com>

Spiegelhalter, D., Best, N., Carlin, B., and van der Linde, A. (2002). Bayesian Measures of Model Complexity and Fit. J. R. Statist. Soc. B, 64(4), 583-639. doi: 10.1111/1467-9868.00353

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