Nothing
######################################################################
##
### Commentary: MCMC analysis of the rhizoctonia data.
##
######################################################################
library(geoBayes)
data(rhizoctonia)
### Create prediction grid
predgrid <- mkpredgrid2d(rhizoctonia[c("Xcoord", "Ycoord")],
par.x = 100, chull = TRUE, exf = 1.2)
rhizdata <- stackdata(rhizoctonia, predgrid$grid)
### Define the model
corrf <- "spherical"
kappa <- 0
ssqdf <- 1
ssqsc <- 1
betm0 <- 0
betQ0 <- .01
phiprior <- c(100, 1, 1000, 100) # U(100, 200)
phisc <- 2.5
omgprior <- c(2, 1, 1, 0) # Exp(mean = 2)
omgsc <- .3
linkp <- 20
### MCMC sizes
Nout <- 1000
Nthin <- 10
Nbi <- 300
emt <- mcsglmm(Infected ~ 1, 'binomial', rhizdata, weights = Total,
atsample = ~ Xcoord + Ycoord,
Nout = Nout, Nthin = Nthin, Nbi = Nbi,
betm0 = betm0, betQ0 = betQ0, ssqdf = ssqdf, ssqsc = ssqsc,
corrprior = list(phi = phiprior, omg = omgprior), linkp = linkp,
corrfcn = corrf, kappa = kappa,
corrtuning = list(phi = phisc, omg = omgsc, kappa = 0),
dispersion = 1, test = TRUE)
emc <- update(emt, test = FALSE)
emcmc <- mcmcmake(emc)
plot.ts(cbind(phi = emc$MCMC$phi, omg = emc$MCMC$omg,
beta = c(emc$MCMC$beta), ssq = emc$MCMC$ssq),
nc = 2)
summary(emcmc[, c("phi", "omg", "beta", "ssq")])
plot(emcmc[, c("phi", "omg", "beta", "ssq")])
library(geoR)
z0pred <- rowMeans(emc$MCMC$z[!emc$MCMC$whichobs, ])
geoR:::image.kriging(locations = predgrid$xygrid,
borders = predgrid$borders, values = z0pred,
x.leg = c(3150, 3450), y.leg = c(1120, 1200),
col = gray((64:32)/64))
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