# Compute the Bayes factors at new points Compute the Bayes factors. ```r 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. ## Returns An array of size `length(linkp) * length(phi) * length(omg) * length(kappa)` containing the Bayes factors for each combination of the parameters. ## 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. ## Examples ```r ## 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) ``` ## 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.

bf2new function