spDiag: Model fit diagnostics DIC and GP

Description

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

Usage

spDiag(sp.obj, start=1, end, thin=1, n.report=100, verbose=TRUE, ...)

Arguments

sp.obj

an object returned by bayesLMRef, bayesLMConjugate, spGGT,

start

specifies the first sample included in the 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.

n.report

the interval to report progress.

...

currently no additional arguments.

Value

A list with some of the following tags:
DICa matrix holding DIC and associated statistics, see Banerjee et al. (2004) for details.
GPa matrix holding GP and associated statistics, see Gelfand and Ghosh (1998) for details. This is only available for bayesLMRef, bayesLMConjugate,spLM, spMvLM, bayesGeostatExact, and mvLM objects
sp.effectsif sp.obj specifies a spatial model without pre-calculated spatial effects then spDiag calculates the spatial effects.

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. Gelfand A.E. and Ghosh, S.K. (1998). Model choice: a minimum posterior predictive loss approach. Biometrika. 85:1-11.

Examples

Run this code

data(rf.n200.dat)

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

n.samples <- 1000

##non-spatial regression
m.1 <- bayesLMRef(lm(Y~1), n.samples=n.samples)


##spatial regression
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=n.samples, verbose=TRUE, n.report=100)

##compare
print(spDiag(m.1))
print(spDiag(m.2, start=500, thin=2))

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