geoBayes (version 0.3.0)

bf2optim: Empirical Bayes estimator

Description

Estimation by empirical Bayes.

Usage

bf2optim(bf1obj, paroptim, useCV = TRUE, control = list())

Arguments

bf1obj

Output from the function bf1skel which contains the Bayes factors and importance sampling weights.

paroptim

A named list with the components "linkp", "phi", "omg", "kappa". Each component must be numeric with length 1, 2, or 3 with elements in increasing order but for the binomial family linkp is also allowed to be the character "logit" and "probit". If the com

useCV

Whether to use control variates for finer corrections.

control

A list of control parameters for the optimisation. See optim.

Value

• The output from the function optim.

Details

This function is a wrap around bf2new using the "L-BFGS-B" method of the function optim to estimate the parameters.

Examples

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)
est <- bf2optim(bf, list(linkp = linkp, phi = c(100, 200), omg = c(0, 2)))
est