SpatialExtremes (version 2.1-0)

DIC: Deviance Information Criterion

Description

This function computes the Deviance Information Criterion (DIC), and related quantities, which is a hierarchical modeling generalization of the Akaike Information Criterion. It is useful for Bayesian model selection.

Usage

DIC(object, ..., fun = "mean")

Arguments

object

An object of class latent --- typically this will be the output of latent.

…

Optional arguments. Not implemented.

fun

A chararcter string giving the name of the function to be used to summarize the Markov chain. The default is to consider the posterior mean.

Value

A vector of length 3 that returns the DIC, effective number of parameters eNoP and an estimate of the expected deviance Dbar.

Details

The deviance is $$D(\theta) = -2 \log \pi(y \mid \theta),$$ where \(y\) are the data, \(\theta\) are the unknown parameters of the models and \(\pi(y \mid \theta)\) is the likelihood function. Thus the expected deviance, a measure of how well the model fits the data, is given by $$\overline{D} = {\rm E}_{\theta}[D(\theta)],$$ while the effective number of parameters is $$p_D = \overline{D} - D(\theta^*),$$ where \(\theta^*\) is point estimate of the posterior distribution, e.g., the posterior mean. Finally the DIC is given by $${\rm DIC} = p_D + \overline{D}.$$

In accordance with the AIC, models with smaller DIC should be preferred to models with larger DIC. Roughly speaking, differences of more than 10 might rule out the model with the higher DIC, differences between 5 and 10 are substantial.

References

Spiegelhalter, D. J., Best, N. G., Carlin, B. P. and Van Der Linde, A. (2002) Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B 64, 583--639.

See Also

AIC

Examples

# NOT RUN {
## Generate realizations from the model
n.site <- 15
n.obs <- 35
coord <- cbind(lon = runif(n.site, -10, 10), lat = runif(n.site, -10 , 10))

gp.loc <- rgp(1, coord, "powexp", sill = 4, range = 20, smooth = 1)
gp.scale <- rgp(1, coord, "powexp", sill = 0.4, range = 5, smooth = 1)
gp.shape <- rgp(1, coord, "powexp", sill = 0.01, range = 10, smooth = 1)

locs <- 26 + 0.5 * coord[,"lon"] + gp.loc
scales <- 10 + 0.2 * coord[,"lat"] + gp.scale
shapes <- 0.15 + gp.shape

data <- matrix(NA, n.obs, n.site)
for (i in 1:n.site)
  data[,i] <- rgev(n.obs, locs[i], scales[i], shapes[i])

loc.form <- y ~ lon
scale.form <- y ~ lat
shape.form <- y ~ 1

hyper <- list()
hyper$sills <- list(loc = c(1,8), scale = c(1,1), shape = c(1,0.02))
hyper$ranges <- list(loc = c(2,20), scale = c(1,5), shape = c(1, 10))
hyper$smooths <- list(loc = c(1,1/3), scale = c(1,1/3), shape = c(1, 1/3))
hyper$betaMeans <- list(loc = rep(0, 2), scale = c(9, 0), shape = 0)
hyper$betaIcov <- list(loc = solve(diag(c(400, 100))),
                       scale = solve(diag(c(400, 100))),
                       shape = solve(diag(c(10), 1, 1)))

## We will use an exponential covariance function so the jump sizes for
## the shape parameter of the covariance function are null.
prop <- list(gev = c(2.5, 1.5, 0.3), ranges = c(40, 20, 20), smooths = c(0,0,0))
start <- list(sills = c(4, .36, 0.009), ranges = c(24, 17, 16), smooths
              = c(1, 1, 1),  beta = list(loc = c(26, 0), scale = c(10, 0),
                               shape = c(0.15)))

mc <- latent(data, coord, loc.form = loc.form, scale.form = scale.form,
             shape.form = shape.form, hyper = hyper, prop = prop, start = start,
             n = 500)
DIC(mc)
# }