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#

#Example:

coal.by1<-krige.bayes( coords=coal.m[,2:3],data=coal.m[,4],

prior=prior.control(phi.discrete=seq(0, 3, l=21)))

#nugget is fixed to zero or anyother value (here 0):

#

# see the histograms of the posteriors

# for the mean, the partial sill and the range:

X11()

par(mfrow=c(1,3))

hist(coal.by1$posterior$sample$beta)

hist(coal.by1$posterior$sample$sigmasq)

hist(coal.by1$posterior$sample$phi)

# to see your function type:  coal.by1$call

#posterior for MEAN

coal.by1$posterior$beta$summary

# mean   median mode.cond

# 9.740055 9.740496  9.758056

#posterior for partial sill

coal.by1$posterior$sigmasq

# mean   median mode.cond

# 9.740055 9.740496  9.758056

#posterior for range

coal.by1$posterior$phi

#     mean    median      mode

# 0.8645331 0.7894737 0.7894737

###B. Bayesian prediction:

#USE  ksline for prediction, this function is still very slow,

#though it is being updated.

#locations where we want Bayesian prediction:

loci<-matrix(c(2,3.5,4,5.5),

ncol=2,byrow=T)

loci

    [,1] [,2]

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