spBayes (version 0.0-2)

sp.DIC: Calculates model DIC and associated statistics given a ggt.sp object

Description

The function sp.DIC calculates model DIC and associated statistics given a ggt.sp object.

Usage

sp.DIC(ggt.sp.obj, DIC.marg=TRUE, DIC.unmarg=TRUE,
         start=1, end, thin=1, verbose=TRUE, ...)

Arguments

ggt.sp.obj

an object returned by ggt.sp (i.e., object of class ggt.sp).

DIC.marg

a logical value indicating if marginalized DIC and associated statistics should be calculated.

DIC.unmarg

a logical value indicating if unmarginalized DIC and associated statistics should be calculated.

start

specifies the first sample included in the DIC calculation. This is useful for those who choose to acknowledge chain burn-in.

end

specifies the last sample included in the DIC calculation. The default is to include from start to nrow(ggt.sp.obj$p.samples).

thin

a sample thinning factor. The default of 1 considers all samples between start and end. For example, if thin = 10 then 1 in 10 samples are considered between start and end.

verbose

if TRUE calculation progress is printed to the screen; otherwise, nothing is printed to the screen.

...

currently no additional arguments.

Value

• DIC.marg
  a matrix holding marginalized DIC and associated statistics.
• DIC.unmarg
  a matrix holding unmarginalized DIC and associated statistics.
• sp.effects
  if DIC.ummarg is true and if the ggt.sp.obj does not include sp.effects then sp.DIC calculates the random spatial effects and includes them in the return object.

Details

Please refer to Section 3.3 in the vignette.

References

Banerjee, S., Carlin, B.P., and Gelfand, A.E. (2004). Hierarchical modeling and analysis for spatial data. Chapman and Hall/CRC Press, Boca Raton, Fla. Further information on the package spBayes can be found at: http://blue.fr.umn.edu/spatialBayes.

See Also

ggt.sp

Examples

data(FBC07)

Y.2 <- FBC07[1:100,"Y.2"]
coords <- as.matrix(FBC07[1:100,c("coord.X", "coord.Y")])

##Fit some model with ggt.sp.
K.prior <- prior(dist="IG", shape=2, scale=5)
Psi.prior <- prior(dist="IG", shape=2, scale=5)
phi.prior <- prior(dist="LOGUNIF", a=0.06, b=3)

var.update.control <-
  list("K"=list(starting=5, tuning=0.5, prior=K.prior),
       "Psi"=list(starting=5, tuning=0.5, prior=Psi.prior),
       "phi"=list(starting=0.1, tuning=0.5, prior=phi.prior)
       )

beta.control <- list(update="GIBBS", prior=prior(dist="FLAT"))

run.control <- list("n.samples"=1000, "sp.effects"=TRUE)

Fit <-
  ggt.sp(formula=Y.2~1, run.control=run.control,
         coords=coords, var.update.control=var.update.control,
         beta.update.control=beta.control,
         cov.model="exponential")

##Now with the ggt.sp object, Fit, calculate the DIC
##for both the unmarginalized and marginalized models.
##The likelihoods for these models are given by equation 6 and 7
##within the vignette.

DIC <- sp.DIC(Fit)
print(DIC)