library(spBayes)

set.seed(1234)

###########################################
##     Compare DIC between non-spatial
##           and spatial models
###########################################

data(FBC07.dat)
Y.2 <- FBC07.dat[1:150,"Y.2"]
coords <- as.matrix(FBC07.dat[1:150,c("coord.X", "coord.Y")])

##############################
##Non-spatial model
##############################
m.1 <- bayes.lm.conjugate(Y.2~1, n.samples = 2000,
                          beta.prior.mean=0, beta.prior.precision=0,
                          prior.shape=-0.5, prior.rate=0)

summary(m.1$p.samples)

dic.m1 <- sp.DIC(m.1)


##> dic.m1
##                  [,1]
##bar.D       503.069362
##D.bar.Omega 501.058448
##pD            2.010914
##DIC         501.058448

##############################
##Spatial model
##############################

m.2 <- sp.lm(Y.2~1, coords=coords, knots=c(6,6,0),
             starting=list("phi"=0.1,"sigma.sq"=5, "tau.sq"=5),
             sp.tuning=list("phi"=0.03, "sigma.sq"=0.03, "tau.sq"=0.03),
             priors=list("phi.Unif"=c(0.06, 3), "sigma.sq.IG"=c(2, 5),
               "tau.sq.IG"=c(2, 5)),
             cov.model="exponential",
             n.samples=2000, verbose=TRUE, n.report=100, sp.effects=TRUE)

summary(m.2$p.samples)

dic.m2 <- sp.DIC(m.2)

##> dic.m2
##$DIC.marg
##                 value
##bar.D       479.904433
##D.bar.Omega 476.856815
##pD            3.047617
##DIC         482.952050

##$DIC.unmarg
##                value
##bar.D       330.50408
##D.bar.Omega 258.64266
##pD           71.86142
##DIC         402.36550