boral (version 1.7)

get.dic: Extract Deviance Information Criterion for boral model

Description

Calculates the Deviance Information Criterion (DIC) for a boral model fitted using JAGS.

Usage

get.dic(jagsfit)

Arguments

jagsfit

The jags.model component of the output, from a model fitted using boral with save.model = TRUE.

Value

DIC value for the jags model.

Details

Details regarding the Deviance Information Criterion may be found in (Spiegelhalter et al., 2002; Ntzoufras, 2011; Gelman et al., 2013). The DIC here is based on the conditional log-likelihood i.e., the latent variables (and row effects if applicable) are treated as "fixed effects". A DIC based on the marginal likelihood is obtainable from get.more.measures, although this requires a much longer time to compute. For models with overdispered count data, conditional DIC may not perform as well as marginal DIC (Millar, 2009)

References

• Gelman et al. (2013). Bayesian data analysis. CRC press.

• Millar, R. B. (2009). Comparison of hierarchical Bayesian models for overdispersed count data using DIC and Bayes' factors. Biometrics, 65, 962-969.

• Ntzoufras, I. (2011). Bayesian modeling using WinBUGS (Vol. 698). John Wiley & Sons.

• Spiegelhalter, et al. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64, 583-639.

Examples

# NOT RUN {
## NOTE: The values below MUST NOT be used in a real application;
## they are only used here to make the examples run quick!!!
example_mcmc_control <- list(n.burnin = 10, n.iteration = 100, 
     n.thin = 1)
     
library(mvabund) ## Load a dataset from the mvabund package
data(spider)
y <- spider$abun
n <- nrow(y)
p <- ncol(y)
    
spiderfit_nb <- boral(y, family = "negative.binomial", lv.control = list(num.lv = 2),
     save.model = TRUE, calc.ics = TRUE, mcmc.control = example_mcmc_control)

spiderfit_nb$ics ## DIC returned as one of several information criteria.
# }