50% off | Unlimited Data & AI Learning

Last chance! 50% off unlimited learning

Sale ends in

Learn R Programming
spBayes (version 0.1-5)

spDIC: Calculates DIC and associated statistics given a model object

Description

The function spDIC calculates model DIC and associated statistics given a bayesLMRef, bayesLMConjugate, spGGT, spLM, spMvLM, bayesGeostatExact, or spGLM object.

Usage

spDIC(sp.obj, start=1, end, thin=1, verbose=TRUE, ...)

Arguments

sp.obj
an object returned by bayesLMRef, bayesLMConjugate, spGGT,
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 prediction calculation. The default is to use all posterior samples in sp.obj.
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

  • A list with some of the following tags:
  • DICa matrix holding DIC and associated statistics
  • sp.effectsif sp.obj specifies a spatial model without pre-calculated spatial effects then spDIC calculates the spatial effects.

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.

See Also

bayesLMRef, bayesLMConjugate, spGGT, bayesGeostatExact, spLM, spGLM

Examples

Run this code
###########################################
##          DIC for spLM
###########################################

##Use some more observations
data(rf.n200.dat)

Y <- rf.n200.dat$Y
coords <- as.matrix(rf.n200.dat[,c("x.coords","y.coords")])

##############################
##With and without predictive
##process
##############################
m.1a <- spLM(Y~1, coords=coords, knots=c(5, 5, 0),
             starting=list("phi"=0.6,"sigma.sq"=1, "tau.sq"=1),
             sp.tuning=list("phi"=0.01, "sigma.sq"=0.05, "tau.sq"=0.05),
             priors=list("phi.Unif"=c(0.3, 3), "sigma.sq.IG"=c(2, 1),
               "tau.sq.IG"=c(2, 1)),
             cov.model="exponential",
             n.samples=1000, verbose=FALSE, n.report=100)

print(spDIC(m.1a, start=100, thin=2)$DIC)

m.1b <- spLM(Y~1, coords=coords, knots=c(7, 7, 0),
             starting=list("phi"=0.6,"sigma.sq"=1, "tau.sq"=1),
             sp.tuning=list("phi"=0.01, "sigma.sq"=0.05, "tau.sq"=0.05),
             priors=list("phi.Unif"=c(0.3, 3), "sigma.sq.IG"=c(2, 1),
               "tau.sq.IG"=c(2, 1)),
             cov.model="exponential",
             n.samples=1000, verbose=FALSE, n.report=100)

print(spDIC(m.1b, start=100, thin=2)$DIC)

m.2 <- spLM(Y~1, coords=coords, 
             starting=list("phi"=0.6,"sigma.sq"=1, "tau.sq"=1),
             sp.tuning=list("phi"=0.01, "sigma.sq"=0.05, "tau.sq"=0.05),
             priors=list("phi.Unif"=c(0.3, 3), "sigma.sq.IG"=c(2, 1),
               "tau.sq.IG"=c(2, 1)),
             cov.model="exponential",
             n.samples=1000, verbose=FALSE, n.report=100)

print(spDIC(m.2, start=100, thin=2)$DIC)

###########################################
##    DIC for bayesGeostatExact
###########################################
data(FORMGMT.dat)

n = nrow(FORMGMT.dat)
p = 5 ##an intercept an four covariates

n.samples <- 10

coords <- cbind(FORMGMT.dat$Longi, FORMGMT.dat$Lat)

phi <- 3/0.07

beta.prior.mean <- rep(0, times=p)
beta.prior.precision <- matrix(0, nrow=p, ncol=p)

alpha <- 1/1.5

sigma.sq.prior.shape <- 2.0
sigma.sq.prior.rate <- 10.0

##With covariates
m.3 <-
  bayesGeostatExact(Y~X1+X2+X3+X4, data=FORMGMT.dat,
                      n.samples=n.samples,
                      beta.prior.mean=beta.prior.mean,
                      beta.prior.precision=beta.prior.precision,
                      coords=coords, phi=phi, alpha=alpha,
                      sigma.sq.prior.shape=sigma.sq.prior.shape,
                      sigma.sq.prior.rate=sigma.sq.prior.rate,
                      sp.effects=FALSE, verbos=FALSE)

print(spDIC(m.3)$DIC)

##Without covariates
p <- 1 ##intercept only
beta.prior.mean <- 0
beta.prior.precision <- 0

m.4 <-
  bayesGeostatExact(Y~1, data=FORMGMT.dat,
                      n.samples=n.samples,
                      beta.prior.mean=beta.prior.mean,
                      beta.prior.precision=beta.prior.precision,
                      coords=coords, phi=phi, alpha=alpha,
                      sigma.sq.prior.shape=sigma.sq.prior.shape,
                      sigma.sq.prior.rate=sigma.sq.prior.rate,
                      sp.effects=FALSE, verbose=FALSE)

print(spDIC(m.4)$DIC)

##Lower DIC is better, so go with the covariates.

###########################################
##         DIC for spGGT
###########################################
data(FBC07.dat)

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

##m.5 some model with spGGT.
K.prior <- prior(dist="IG", shape=2, scale=5)
Psi.prior <- prior(dist="IG", shape=2, scale=5)
phi.prior <- prior(dist="UNIF", a=0.06, b=3)

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

m.5 <-
  spGGT(formula=Y.2~1, run.control=run.control,
         coords=coords, var.update.control=var.update.control,
         beta.update.control=beta.control,
         cov.model="exponential", verbose=FALSE)

DIC <- spDIC(m.5$DIC)
print(DIC)

###########################################
##     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 <- bayesLMConjugate(Y.2~1, n.samples = 2000,
                          beta.prior.mean=0, beta.prior.precision=0,
                          prior.shape=-0.5, prior.rate=0, verbose=FALSE)

summary(m.1$p.samples)

dic.m1 <- spDIC(m.1)
print(dic.m1)

##############################
##Spatial model
##############################
m.2 <- spLM(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=FALSE, n.report=100)

summary(m.2$p.samples)

dic.m2 <- spDIC(m.2)
print(dic.m2)

Run the code above in your browser using DataLab