Compute the Bayes factors.
1 
bf1obj 
Output from the function 
linkp,phi,omg,kappa 
Optional scalar or vector or

useCV 
Whether to use control variates for finer corrections. 
Computes the Bayes factors using the importance weights at the new
points. The new points are taken from the grid derived by
expanding the parameter values inputted. The arguments
linkp
phi
omg
kappa
correspond to the
link function, spatial range, relative nugget, and correlation
function parameters respectively.
An array of size length(linkp) * length(phi) *
length(omg) * length(kappa)
containing the Bayes factors for each
combination of the parameters.
Doss, H. (2010). Estimation of large families of Bayes factors from Markov chain output. Statistica Sinica, 20(2), 537.
Roy, V., Evangelou, E., and Zhu, Z. (2015). Efficient estimation and prediction for the Bayesian spatial generalized linear mixed model with flexible link functions. Biometrics. http://dx.doi.org/10.1111/biom.12371
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35  ## Not run:
data(rhizoctonia)
### Define the model
corrf < "spherical"
kappa < 0
ssqdf < 1
ssqsc < 1
betm0 < 0
betQ0 < .01
linkp < "probit"
### Skeleton points
philist < c(100, 140, 180)
omglist < c(.5, 1)
parlist < expand.grid(phi=philist, linkp=linkp, omg=omglist, kappa = kappa)
### MCMC sizes
Nout < 100
Nthin < 1
Nbi < 0
### Take MCMC samples
runs < list()
for (i in 1:NROW(parlist)) {
runs[[i]] < mcsglmm(Infected ~ 1, 'binomial', rhizoctonia, weights = Total,
atsample = ~ Xcoord + Ycoord,
Nout = Nout, Nthin = Nthin, Nbi = Nbi,
betm0 = betm0, betQ0 = betQ0,
ssqdf = ssqdf, ssqsc = ssqsc,
phistart = parlist$phi[i], omgstart = parlist$omg[i],
linkp = parlist$linkp[i], kappa = parlist$kappa[i],
corrfcn = corrf, phisc = 0, omgsc = 0)
}
bf < bf1skel(runs)
bfall < bf2new(bf, phi = seq(100, 200, 10), omg = seq(0, 2, .2))
plotbf2(bfall, c("phi", "omg"))
## End(Not run)

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