boral (version 2.0.2)

get.dic: Extract Deviance Information Criterion for a fitted model

Description

lifecycle::badge("defunct")

Calculates the Deviance Information Criterion (DIC) for a model fitted using JAGS. WARNING: As of version 1.6, this function is no longer maintained (and probably doesn't work properly, if at all)!

Usage

get.dic(jagsfit)

Value

DIC value for the jags model.

Arguments

jagsfit

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

Author

tools:::Rd_package_author("boral")

Maintainer: tools:::Rd_package_maintainer("boral")

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

if (FALSE) {
## 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)
     
testpath <- file.path(tempdir(), "jagsboralmodel.txt")


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,
     model.name = testpath)

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