Computing DIC
Functions to extract deviance information criterion (DIC).
DIC(mod, return.pD = FALSE, ...) ## S3 method for class 'bugs' DIC(mod, return.pD = FALSE, ...) ## S3 method for class 'rjags' DIC(mod, return.pD = FALSE, ...) ## S3 method for class 'jagsUI' DIC(mod, return.pD = FALSE, ...)
mod |
an object of class |
return.pD |
logical. If |
... |
additional arguments passed to the function. |
DIC is implemented for bugs, rjags, and
jagsUI classes. The function extracts the deviance
information criterion (DIC, Spiegelhalter et al. 2002) or the
effective number of parameters (pD).
DIC the DIC or pD depending on the values of the arguments.
The actual DIC values are not really interesting in themselves, as they depend directly on the data, parameters estimated, and likelihood function. Furthermore, a single value does not tell much about model fit. Information criteria become relevant when compared to Yone another for a given data set and set of candidate models. Model selection with hierarchical models is problematic as the classic DIC is not appropriate for such types of models (Millar 2009).
Marc J. Mazerolle
Millar, R. B. (2009) Comparison of hierarchical Bayesian models for overdispersed count data using DIC and Bayes' factors. Biometrics, 65, 962–969.
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., van der Linde, A. (2002). Bayesian measures of complexity and fit. Journal of the Royal Statistical Society, Series B 64, 583–639.
##from ?jags example in R2jags package
## Not run:
require(R2jags)
##example model file
model.file <- system.file(package="R2jags", "model", "schools.txt")
file.show(model.file)
##data
J <- 8.0
y <- c(28.4,7.9,-2.8,6.8,-0.6,0.6,18.0,12.2)
sd <- c(14.9,10.2,16.3,11.0,9.4,11.4,10.4,17.6)
##arrange data in list
jags.data <- list (J = J, y = y, sd = sd)
##initial values
jags.inits <- function(){
list(theta=rnorm(J, 0, 100), mu=rnorm(1, 0, 100),
sigma=runif(1, 0, 100))
}
##parameters to be monitored
jags.parameters <- c("theta", "mu", "sigma")
##run model
schools.sim <- jags(data = jags.data, inits = jags.inits,
parameters = jags.parameters,
model.file = model.file,
n.chains = 3, n.iter = 10)
##note that n.iter should be higher
##extract DIC
DIC(schools.sim)
##extract pD
DIC(schools.sim, return.pD = TRUE)
detach(package:R2jags)
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