# Convert to an `mcmc` object Convert to an `mcmc` object. ```r mcmcmake(...) ``` ## Arguments - `...`: Output(s) from the functions mentioned in the Details. ## Returns An mcmc object. ## Details This function takes as input the one or more output(s) from function `mcsglmm` or `mcstrga` and returns an `mcmc` object or an `mcmc.list` object for coda. The function requires the `coda` package to be installed. The spatial random field components are assigned the names `z_*` where `*` is a number beginning at 1. Similarly, the regressor coefficients are assigned the names `beta_*` if not unique, or simply `beta` if there is only one regressor. The names `ssq`, `tsq`, `phi`, `omg` correspond to the partial sill, measurement error variance, spatial range, and relative nugget parameters respectively. ## Examples ```r ## Not run: ### Load the data data(rhizoctonia) rhiz <- na.omit(rhizoctonia) rhiz$IR <- rhiz$Infected/rhiz$Total # Incidence rate of the # rhizoctonia disease ### Define the model corrf <- "spherical" ssqdf <- 1 ssqsc <- 1 tsqdf <- 1 tsqsc <- 1 betm0 <- 0 betQ0 <- diag(.01, 2, 2) phiprior <- c(200, 1, 1000, 100) # U(100, 300) phisc <- 1 omgprior <- c(3, 1, 1000, 0) # U(0, 3) omgsc <- 1.3 linkp <- 1 ## MCMC parameters Nout <- 100 Nbi <- 0 Nthin <- 1 ### Run MCMC sample <- mcstrga(Yield ~ IR, data = rhiz, atsample = ~ Xcoord + Ycoord, corrf = corrf, Nout = Nout, Nthin = Nthin, Nbi = Nbi, betm0 = betm0, betQ0 = betQ0, ssqdf = ssqdf, ssqsc = ssqsc, tsqdf = tsqdf, tsqsc = tsqsc, linkp = linkp, corrprior = list(phi = phiprior, omg = omgprior), corrtuning = list(phi = phisc, omg = omgsc, kappa = 0), test = FALSE) mcsample <- mcmcmake(sample) plot(mcsample[, c("phi", "omg", "beta_1", "beta_2", "ssq", "tsq")], density = FALSE) summary(mcsample[, c("phi", "omg", "beta_1", "beta_2", "ssq", "tsq")]) ## End(Not run) ``` ## See Also Functions such as `plot.mcmc` and `summary.mcmc` in the `coda` package. The function `do.call` can be used to pass arguments stored in a list.

mcmcmake function