# bf2new: Compute the Bayes factors at new points In geoBayes: Analysis of Geostatistical Data using Bayes and Empirical Bayes Methods

## Description

Compute the Bayes factors.

## Usage

 `1` ```bf2new(bf1obj, linkp, phi, omg, kappa, useCV = TRUE) ```

## Arguments

 `bf1obj` Output from the function `bf1skel` which contains the Bayes factors and importance sampling weights. `linkp, phi, omg, kappa` Optional scalar or vector or `NULL`. If scalar or vector, the Bayes factors are calculated at those values with respect to the reference model used in `bf1skel`. If missing or `NULL` then the unique values from the MCMC chains that were inputted in `bf1skel` will be used. `useCV` Whether to use control variates for finer corrections.

## Details

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.

## Value

An array of size ```length(linkp) * length(phi) * length(omg) * length(kappa)``` containing the Bayes factors for each combination of the parameters.

## References

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, 72(1), 289-298.

## Examples

 ``` 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 36``` ```## Not run: data(rhizoctonia) ### Define the model corrf <- "spherical" kappa <- 0 ssqdf <- 1 ssqsc <- 1 betm0 <- 0 betQ0 <- .01 family <- "binomial.probit" ### Skeleton points philist <- c(100, 140, 180) omglist <- c(.5, 1) parlist <- expand.grid(linkp=0, phi=philist, 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, family, rhizoctonia, weights = Total, atsample = ~ Xcoord + Ycoord, Nout = Nout, Nthin = Nthin, Nbi = Nbi, betm0 = betm0, betQ0 = betQ0, ssqdf = ssqdf, ssqsc = ssqsc, phi = parlist\$phi[i], omg = parlist\$omg[i], linkp = parlist\$linkp[i], kappa = parlist\$kappa[i], corrfcn = corrf, corrtuning=list(phi = 0, omg = 0, kappa = 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) ```

geoBayes documentation built on May 2, 2019, 3:14 a.m.