R/svcTPGBinom.R

In spOccupancy: Single-Species, Multi-Species, and Integrated Spatial Occupancy Models

svcTPGBinom <- function(formula, data, inits, priors, 
                        tuning, svc.cols = 1, cov.model = 'exponential', NNGP = TRUE, 
                        n.neighbors = 15, search.type = 'cb', n.batch, 
                        batch.length, accept.rate = 0.43, n.omp.threads = 1, 
                        verbose = TRUE, ar1 = FALSE, n.report = 100, 
                        n.burn = round(.10 * n.batch * batch.length), 
                        n.thin = 1, n.chains = 1, 
                        k.fold, k.fold.threads = 1, k.fold.seed = 100, 
                        k.fold.only = FALSE, ...){

  ptm <- proc.time()

  # Make it look nice
  if (verbose) {
    cat("----------------------------------------\n");
    cat("\tPreparing the data\n");
    cat("----------------------------------------\n");
  }

  # Functions ---------------------------------------------------------------
  logit <- function(theta, a = 0, b = 1) {log((theta-a)/(b-theta))}
  logit.inv <- function(z, a = 0, b = 1) {b-(b-a)/(1+exp(z))}
  rigamma <- function(n, a, b){
    1/rgamma(n = n, shape = a, rate = b)
  }

  # Check for unused arguments ------------------------------------------
  formal.args <- names(formals(sys.function(sys.parent())))
  elip.args <- names(list(...))
  for(i in elip.args){
      if(! i %in% formal.args)
          warning("'",i, "' is not an argument")
  }
  # Call ----------------------------------------------------------------
  # Returns a call in which all of the specified arguments are
  # specified by their full names.
  cl <- match.call()

  # Some initial checks -------------------------------------------------
  if (missing(data)) {
    stop("error: data must be specified")
  }
  if (!is.list(data)) {
    stop("error: data must be a list")
  }
  names(data) <- tolower(names(data))
  if (missing(formula)) {
    stop("error: formula must be specified")
  }
  if (!'y' %in% names(data)) {
    stop("error: detection-nondetection data y must be specified in data")
  }
  y <- as.matrix(data$y)
  if (!'weights' %in% names(data)) {
    stop("error: weights (binomial denominator) must be specified in data")
  }
  weights <- as.matrix(data$weights)
  # Check occupancy covariates
  if (!'covs' %in% names(data)) {
    if (formula == ~ 1) {
      if (verbose) {
        message("Covariates (covs) not specified in data.\nAssuming intercept only model.\n")
      }
      data$covs <- matrix(1, dim(y)[1], dim(y)[2])
    } else {
      stop("error: covs must be specified in data for model with covariates")
    }
  }
  if (!is.list(data$covs)) {
    stop("error: covs must be a list of matrices, data frames, and/or vectors")
  }
  if (!is.matrix(data$coords) & !is.data.frame(data$coords)) {
    stop("error: coords must be a matrix or data frame")
  }
  coords <- as.matrix(data$coords)
  # Check if all spatial coordinates are unique. 
  unique.coords <- unique(data$coords)
  if (nrow(unique.coords) < nrow(data$coords)) {
    stop("coordinates provided in coords are not all unique. spOccupancy requires each site to have its own unique pair of spatial coordinates. This may be the result of an error in preparing the data list, or you will need to change what you consider a 'site' in order to meet this requirement.") 
  }
  if (!missing(k.fold)) {
    if (!is.numeric(k.fold) | length(k.fold) != 1 | k.fold < 2) {
      stop("error: k.fold must be a single integer value >= 2")  
    }
  }

  # Neighbors and Ordering ----------------------------------------------
  if (NNGP) {
    u.search.type <- 2 
    ## Order by x column. Could potentially allow this to be user defined. 
    ord <- order(coords[,1]) 
    # Reorder everything to align with NN ordering
    y <- y[ord, , drop = FALSE]
    weights <- weights[ord, , drop = FALSE]
    coords <- coords[ord, , drop = FALSE]
    # Occupancy covariates
    for (i in 1:length(data$covs)) {
      if (!is.null(dim(data$covs[[i]]))) { # Time/space varying
        data$covs[[i]] <- data$covs[[i]][ord, , drop = FALSE]
      } else { # Space-varying
        data$covs[[i]] <- data$covs[[i]][ord]
      }
    } 
  }

  # Reformat covariates ---------------------------------------------------
  # Make both covariates a data frame. Unlist is necessary for when factors
  # are supplied. 
  y.big <- y
  # Get occurrence covariates in proper format
  # Subset covariates to only use those that are included in the analysis
  data$covs <- data$covs[names(data$covs) %in% all.vars(formula)]
  # Null model support
  if (length(data$covs) == 0) {
    data$covs <- list(int = matrix(1, nrow = dim(y)[1], ncol = dim(y)[2]))
  }
  # Ordered by year, then site within year. 
  data$covs <- data.frame(lapply(data$covs, function(a) unlist(c(a))))
  # Check if only site-level covariates are included
  if (nrow(data$covs) == dim(y)[1]) {
    data$covs <- as.data.frame(mapply(rep, data$covs, dim(y)[2]))
  }

  # Checking missing values ---------------------------------------------
  # y -------------------------------
  y.na.test <- apply(y.big, 1, function(a) sum(!is.na(a)))
  if (sum(y.na.test == 0) > 0) {
    stop("error: some sites in y have all missing detection histories. Remove these sites from all objects in the 'data' argument, then use 'predict' to obtain predictions at these locations/time points if desired.")
  }
  # covs ------------------------
  if (sum(is.na(data$covs)) != 0) {
    stop("error: missing values in covs. Please remove these sites from all objects in data or somehow replace the NA values with non-missing values (e.g., mean imputation).") 
  }

  # Check whether random effects are sent in as numeric, and
  # return error if they are. 
  # Occurrence ----------------------
  if (!is.null(findbars(formula))) {
    occ.re.names <- sapply(findbars(formula), all.vars)
    for (i in 1:length(occ.re.names)) {
      if (is(data$covs[, occ.re.names[i]], 'factor')) {
        stop(paste("error: random effect variable ", occ.re.names[i], " specified as a factor. Random effect variables must be specified as numeric.", sep = ''))
      } 
      if (is(data$covs[, occ.re.names[i]], 'character')) {
        stop(paste("error: random effect variable ", occ.re.names[i], " specified as character. Random effect variables must be specified as numeric.", sep = ''))
      }
    }
  }

  # Check ar1 parameter ---------------------------------------------------
  if (!(ar1 %in% c(TRUE, FALSE))) {
    stop("error: ar1 must be either TRUE or FALSE")
  }

  # Formula -------------------------------------------------------------
  # Occupancy -----------------------
  if (is(formula, 'formula')) {
    tmp <- parseFormula(formula, data$covs)
    X <- as.matrix(tmp[[1]])
    X.re <- as.matrix(tmp[[4]])
    x.re.names <- colnames(X.re)
    x.names <- tmp[[2]]
  } else {
    stop("error: formula is misspecified")
  }
  # Get RE level names
  re.level.names <- lapply(data$covs[, x.re.names, drop = FALSE],
			   function (a) sort(unique(a)))

  # Get basic info from inputs ------------------------------------------
  # Number of sites
  J <- dim(y.big)[1]
  # Total number of years
  n.years.max <- dim(y.big)[2]
  # Number of years for each site
  n.years <- apply(y.big, 1, function(a) sum(!is.na(a)))
  # Number of occupancy effects
  p <- ncol(X)
  # Number of occurrence random effect parameters
  p.re <- ncol(X.re)
  # Number of latent occupancy random effect values
  n.re <- length(unlist(apply(X.re, 2, unique)))
  n.re.long <- apply(X.re, 2, function(a) length(unique(a)))
  if (missing(n.batch)) {
    stop("error: must specify number of MCMC batches")
  }
  if (missing(batch.length)) {
    stop("error: must specify length of each MCMC batch")
  }
  n.samples <- n.batch * batch.length
  if (n.burn > n.samples) {
    stop("error: n.burn must be less than n.samples")
  }
  if (n.thin > n.samples) {
    stop("error: n.thin must be less than n.samples")
  }
  # Check if n.burn, n.thin, and n.samples result in an integer and error if otherwise.
  if (((n.samples - n.burn) / n.thin) %% 1 != 0) {
    stop("the number of posterior samples to save ((n.samples - n.burn) / n.thin) is not a whole number. Please respecify the MCMC criteria such that the number of posterior samples saved is a whole number.")
  }
  # Check SVC columns -----------------------------------------------------
  if (is.character(svc.cols)) {
    # Check if all column names in svc are in covs
    if (!all(svc.cols %in% x.names)) {
        missing.cols <- svc.cols[!(svc.cols %in% x.names)]
        stop(paste("error: variable name ", paste(missing.cols, collapse=" and "), " not inurrence covariates", sep=""))
    }
    # Convert desired column names into the numeric column index
    svc.cols <- (1:p)[x.names %in% svc.cols]
    
  } else if (is.numeric(svc.cols)) {
    # Check if all column indices are in 1:p
    if (!all(svc.cols %in% 1:p)) {
        missing.cols <- svc.cols[!(svc.cols %in% (1:p))]
        stop(paste("error: column index ", paste(missing.cols, collapse=" "), " not in design matrix columns", sep=""))
    }
  }
  p.svc <- length(svc.cols)

  # Get indices for mapping different values in Z. 
  # Index that links observations to sites. 
  z.site.indx <- rep(1:J, n.years.max) - 1
  z.year.indx <- rep(1:n.years.max, each = J) - 1
  z.dat.indx <- c(ifelse(!is.na(weights), 1, 0))
  y <- c(y)

  # Get random effect matrices all set ----------------------------------
  if (p.re > 1) {
    for (j in 2:p.re) {
      X.re[, j] <- X.re[, j] + max(X.re[, j - 1]) + 1
    }
  }

  # Priors --------------------------------------------------------------
  if (missing(priors)) {
    priors <- list()
  }
  names(priors) <- tolower(names(priors))
  # Priors --------------------------------------------------------------
  if (missing(priors)) {
    priors <- list()
  }
  names(priors) <- tolower(names(priors))
  # beta -----------------------
  if ("beta.normal" %in% names(priors)) {
    if (!is.list(priors$beta.normal) | length(priors$beta.normal) != 2) {
      stop("error: beta.normal must be a list of length 2")
    }
    mu.beta <- priors$beta.normal[[1]]
    sigma.beta <- priors$beta.normal[[2]]
    if (length(mu.beta) != p & length(mu.beta) != 1) {
      if (p == 1) {
        stop(paste("error: beta.normal[[1]] must be a vector of length ",
        	     p, " with elements corresponding to betas' mean", sep = ""))
      } else {
        stop(paste("error: beta.normal[[1]] must be a vector of length ",
        	     p, " or 1 with elements corresponding to betas' mean", sep = ""))
      }
    }
    if (length(sigma.beta) != p & length(sigma.beta) != 1) {
      if (p == 1) {
        stop(paste("error: beta.normal[[2]] must be a vector of length ",
      	   p, " with elements corresponding to betas' variance", sep = ""))
      } else {
        stop(paste("error: beta.normal[[2]] must be a vector of length ",
      	   p, " or 1 with elements corresponding to betas' variance", sep = ""))
      }
    }
    if (length(sigma.beta) != p) {
      sigma.beta <- rep(sigma.beta, p)
    }
    if (length(mu.beta) != p) {
      mu.beta <- rep(mu.beta, p)
    }
    Sigma.beta <- sigma.beta * diag(p)
  } else {
    if (verbose) {
      message("No prior specified for beta.normal.\nSetting prior mean to 0 and prior variance to 2.72\n")
    }
    mu.beta <- rep(0, p)
    sigma.beta <- rep(2.72, p)
    Sigma.beta <- diag(p) * 2.72
  }
  # phi -----------------------------
  # Get distance matrix which is used if priors are not specified
  coords.D <- iDist(coords)
  if ("phi.unif" %in% names(priors)) {
    if (!is.list(priors$phi.unif) | length(priors$phi.unif) != 2) {
      stop("error: phi.unif must be a list of length 2")
    }
    phi.a <- priors$phi.unif[[1]]
    phi.b <- priors$phi.unif[[2]]
    if (length(phi.a) != p.svc & length(phi.a) != 1) {
      stop(paste("error: phi.unif[[1]] must be a vector of length ", 
      	   p.svc, 
           " or 1 with elements corresponding to phis' lower bound for each covariate with spatially-varying coefficients",
           sep = ""))
    }
    if (length(phi.b) != p.svc & length(phi.b) != 1) {
      stop(paste("error: phi.unif[[2]] must be a vector of length ", 
      	   p.svc, 
           " or 1 with elements corresponding to phis' upper bound for each covariate with spatially-varying coefficients", sep = ""))
    }
    if (length(phi.a) != p.svc) {
      phi.a <- rep(phi.a, p.svc)
    }
    if (length(phi.b) != p.svc) {
      phi.b <- rep(phi.b, p.svc)
    }
  } else {
    if (verbose) {
    message("No prior specified for phi.unif.\nSetting uniform bounds based on the range of observed spatial coordinates.\n")
    }
    phi.a <- rep(3 / max(coords.D), p.svc)
    phi.b <- rep(3 / sort(unique(c(coords.D)))[2], p.svc)
  }
  # sigma.sq -----------------------------
  if (("sigma.sq.ig" %in% names(priors)) & ("sigma.sq.unif" %in% names(priors))) {
    stop("error: cannot specify both an IG and a uniform prior for sigma.sq")
  }
  if ("sigma.sq.ig" %in% names(priors)) {
    sigma.sq.ig <- TRUE
    if (!is.list(priors$sigma.sq.ig) | length(priors$sigma.sq.ig) != 2) {
      stop("error: sigma.sq.ig must be a list of length 2")
    }
    sigma.sq.a <- priors$sigma.sq.ig[[1]]
    sigma.sq.b <- priors$sigma.sq.ig[[2]]
    if (length(sigma.sq.a) != p.svc & length(sigma.sq.a) != 1) {
      stop(paste("error: sigma.sq.ig[[1]] must be a vector of length ", 
      	   p.svc, " or 1 with elements corresponding to sigma.sqs' shape for each covariate with spatially-varying coefficients", sep = ""))
    }
    if (length(sigma.sq.b) != p.svc & length(sigma.sq.b) != 1) {
      stop(paste("error: sigma.sq.ig[[2]] must be a vector of length ", 
      	   p.svc, " or 1 with elements corresponding to sigma.sqs' scale for each covariate with spatially-varying coefficients", sep = ""))
    }
    if (length(sigma.sq.a) != p.svc) {
      sigma.sq.a <- rep(sigma.sq.a, p.svc)
    }
    if (length(sigma.sq.b) != p.svc) {
      sigma.sq.b <- rep(sigma.sq.b, p.svc)
    }
  } else if ("sigma.sq.unif" %in% names(priors)) { # uniform prior
    sigma.sq.ig <- FALSE
    if (!is.list(priors$sigma.sq.unif) | length(priors$sigma.sq.unif) != 2) {
      stop("error: sigma.sq.unif must be a list of length 2")
    }
    sigma.sq.a <- priors$sigma.sq.unif[[1]]
    sigma.sq.b <- priors$sigma.sq.unif[[2]]
    if (length(sigma.sq.a) != p.svc & length(sigma.sq.a) != 1) {
      stop(paste("error: sigma.sq.unif[[1]] must be a vector of length ", 
      	   p.svc, " or 1 with elements corresponding to sigma.sqs' shape for each covariate with spatially-varying coefficients", sep = ""))
    }
    if (length(sigma.sq.b) != p.svc & length(sigma.sq.b) != 1) {
      stop(paste("error: sigma.sq.unif[[2]] must be a vector of length ", 
      	   p.svc, " or 1 with elements corresponding to sigma.sqs' scale for each covariate with spatially-varying coefficients", sep = ""))
    }
    if (length(sigma.sq.a) != p.svc) {
      sigma.sq.a <- rep(sigma.sq.a, p.svc)
    }
    if (length(sigma.sq.b) != p.svc) {
      sigma.sq.b <- rep(sigma.sq.b, p.svc)
    }
  } else {
    if (verbose) {
      message("No prior specified for sigma.sq.\nUsing an inverse-Gamma prior with the shape parameter set to 2 and scale parameter to 1.\n")
    }
    sigma.sq.ig <- TRUE
    sigma.sq.a <- rep(2, p.svc)
    sigma.sq.b <- rep(1, p.svc)
  }

  # nu -----------------------------
  if (cov.model == "matern") {
    if (!"nu.unif" %in% names(priors)) {
      stop("error: nu.unif must be specified in priors value list")
    }
    nu.a <- priors$nu.unif[[1]]
    nu.b <- priors$nu.unif[[2]]
    if (!is.list(priors$nu.unif) | length(priors$nu.unif) != 2) {
      stop("error: nu.unif must be a list of length 2")
    }
    if (length(nu.a) != p.svc & length(nu.a) != 1) {
      stop(paste("error: nu.unif[[1]] must be a vector of length ", 
      	   p.svc, " or 1 with elements corresponding to nus' lower bound for each covariate with spatially-varying coefficients", sep = ""))
    }
    if (length(nu.b) != p.svc & length(nu.b) != 1) {
      stop(paste("error: nu.unif[[2]] must be a vector of length ", 
      	   p.svc, " or 1 with elements corresponding to nus' upper bound for each covariate with spatially-varying coefficients", sep = ""))
    }
    if (length(nu.a) != p.svc) {
      nu.a <- rep(nu.a, p.svc)
    }
    if (length(nu.b) != p.svc) {
      nu.b <- rep(nu.b, p.svc)
    }
  } else {
    nu.a <- rep(0, p.svc)
    nu.b <- rep(0, p.svc)
  }
  # sigma.sq.psi --------------------
  if (p.re > 0) {
    if ("sigma.sq.psi.ig" %in% names(priors)) {
      if (!is.list(priors$sigma.sq.psi.ig) | length(priors$sigma.sq.psi.ig) != 2) {
        stop("error: sigma.sq.psi.ig must be a list of length 2")
      }
      sigma.sq.psi.a <- priors$sigma.sq.psi.ig[[1]]
      sigma.sq.psi.b <- priors$sigma.sq.psi.ig[[2]]
      if (length(sigma.sq.psi.a) != p.re & length(sigma.sq.psi.a) != 1) {
        if (p.re == 1) {
        stop(paste("error: sigma.sq.psi.ig[[1]] must be a vector of length ", 
        	   p.re, " with elements corresponding to sigma.sq.psis' shape", sep = ""))
        } else {
        stop(paste("error: sigma.sq.psi.ig[[1]] must be a vector of length ", 
        	   p.re, " or 1 with elements corresponding to sigma.sq.psis' shape", sep = ""))
        }
      }
      if (length(sigma.sq.psi.b) != p.re & length(sigma.sq.psi.b) != 1) {
        if (p.re == 1) {
          stop(paste("error: sigma.sq.psi.ig[[2]] must be a vector of length ", 
        	   p.re, " with elements corresponding to sigma.sq.psis' scale", sep = ""))
        } else {
          stop(paste("error: sigma.sq.psi.ig[[2]] must be a vector of length ", 
        	   p.re, " or 1with elements corresponding to sigma.sq.psis' scale", sep = ""))
        }
      }
      if (length(sigma.sq.psi.a) != p.re) {
        sigma.sq.psi.a <- rep(sigma.sq.psi.a, p.re)
      }
      if (length(sigma.sq.psi.b) != p.re) {
        sigma.sq.psi.b <- rep(sigma.sq.psi.b, p.re)
      }
    }   else {
      if (verbose) {	    
        message("No prior specified for sigma.sq.psi.ig.\nSetting prior shape to 0.1 and prior scale to 0.1\n")
      }
      sigma.sq.psi.a <- rep(0.1, p.re)
      sigma.sq.psi.b <- rep(0.1, p.re)
    }
  } else {
    sigma.sq.psi.a <- 0
    sigma.sq.psi.b <- 0
  }

  if (ar1) {
    # rho -----------------------------
    if ("rho.unif" %in% names(priors)) {
      if (!is.vector(priors$rho.unif) | !is.atomic(priors$rho.unif) | length(priors$rho.unif) != 2) {
        stop("error: rho.unif must be a vector of length 2 with elements corresponding to rho's lower and upper bounds")
      }
      rho.a <- priors$rho.unif[1]
      rho.b <- priors$rho.unif[2]
    } else {
      if (verbose) {
        message("No prior specified for rho.unif.\nSetting uniform bounds to -1 and 1.\n")
      }
      rho.a <- -1 
      rho.b <- 1 
    }
    
    # sigma.sq.t.t ----------------------
    if ("sigma.sq.t.ig" %in% names(priors)) { 
      if (!is.vector(priors$sigma.sq.t.ig) | !is.atomic(priors$sigma.sq.t.ig) | length(priors$sigma.sq.t.ig) != 2) {
        stop("error: sigma.sq.t.ig must be a vector of length 2 with elements corresponding to sigma.sq.t's shape and scale parameters")
      }
      sigma.sq.t.a <- priors$sigma.sq.t.ig[1]
      sigma.sq.t.b <- priors$sigma.sq.t.ig[2]
    } else {
      if (verbose) {
        message("No prior specified for sigma.sq.t.\nUsing an inverse-Gamma prior with the shape parameter set to 2 and scale parameter to 0.5.\n")
      }
      sigma.sq.t.a <- 2
      sigma.sq.t.b <- 0.5
    }
  } else {
    rho.a <- 0
    rho.b <- 0
    sigma.sq.t.a <- 0
    sigma.sq.t.b <- 0
  }

  # Starting values -----------------------------------------------------
  if (missing(inits)) {
    inits <- list()
  }
  names(inits) <- tolower(names(inits))
  # beta -----------------------
  if ("beta" %in% names(inits)) {
    beta.inits <- inits[["beta"]]
    if (length(beta.inits) != p & length(beta.inits) != 1) {
      if (p == 1) {
        stop(paste("error: initial values for beta must be of length ", p,
      	     sep = ""))

      } else {
        stop(paste("error: initial values for beta must be of length ", p, " or 1",
        	     sep = ""))
      }
    }
    if (length(beta.inits) != p) {
      beta.inits <- rep(beta.inits, p)
    }
  } else {
    beta.inits <- rnorm(p, mu.beta, sqrt(sigma.beta))
    if (verbose) {
      message('beta is not specified in initial values.\nSetting initial values to random values from the prior distribution\n')
    }
  }
  # phi -----------------------------
  if ("phi" %in% names(inits)) {
    phi.inits <- inits[["phi"]]
    if (length(phi.inits) != p.svc & length(phi.inits) != 1) {
      stop(paste("error: initial values for phi must be of length ", p.svc, " or 1", 
      	   sep = ""))
    }
    if (length(phi.inits) != p.svc) {
      phi.inits <- rep(phi.inits, p.svc)
    }
  } else {
    phi.inits <- runif(p.svc, phi.a, phi.b)
    if (verbose) {
      message("phi is not specified in initial values.\nSetting initial value to random values from the prior distribution\n")
    }
  }
  # sigma.sq ------------------------
  if ("sigma.sq" %in% names(inits)) {
    sigma.sq.inits <- inits[["sigma.sq"]]
    if (length(sigma.sq.inits) != p.svc & length(sigma.sq.inits) != 1) {
      stop(paste("error: initial values for sigma.sq must be of length ", p.svc,  " or 1",
      	   sep = ""))
    }
    if (length(sigma.sq.inits) != p.svc) {
      sigma.sq.inits <- rep(sigma.sq.inits, p.svc)
    }
  } else {
    if (sigma.sq.ig) {
      sigma.sq.inits <- rigamma(p.svc, sigma.sq.a, sigma.sq.b)
    } else {
      sigma.sq.inits <- runif(p.svc, sigma.sq.a, sigma.sq.b)
    }
    if (verbose) {
      message("sigma.sq is not specified in initial values.\nSetting initial values to random values from the prior distribution\n")
    }
  }
  # nu ------------------------
  if ("nu" %in% names(inits)) {
    nu.inits <- inits[["nu"]]
    if (length(nu.inits) != p.svc & length(nu.inits) != 1) {
      stop(paste("error: initial values for nu must be of length ", p.svc,  " or 1",
      	   sep = ""))
    }
    if (length(nu.inits) != p.svc) {
      nu.inits <- rep(nu.inits, p.svc)
    }
  } else {
    if (cov.model == 'matern') {
      if (verbose) {
        message("nu is not specified in initial values.\nSetting initial values to random values from the prior distribution\n")
      }
      nu.inits <- runif(p.svc, nu.a, nu.b)
    } else {
      nu.inits <- rep(0, p.svc)
    }
  }
  # w ---------------------------------
  # Just set initial W values to 0. 
  w.inits <- rep(0, J * p.svc)
  # sigma.sq.psi -------------------
  if (p.re > 0) {
    if ("sigma.sq.psi" %in% names(inits)) {
      sigma.sq.psi.inits <- inits[["sigma.sq.psi"]]
      if (length(sigma.sq.psi.inits) != p.re & length(sigma.sq.psi.inits) != 1) {
        if (p.re == 1) {
          stop(paste("error: initial values for sigma.sq.psi must be of length ", p.re, 
      	       sep = ""))
        } else {
          stop(paste("error: initial values for sigma.sq.psi must be of length ", p.re, 
      	       " or 1", sep = ""))
        }
      }
      if (length(sigma.sq.psi.inits) != p.re) {
        sigma.sq.psi.inits <- rep(sigma.sq.psi.inits, p.re)
      }
    } else {
      sigma.sq.psi.inits <- runif(p.re, 0.5, 10)
      if (verbose) {
        message("sigma.sq.psi is not specified in initial values.\nSetting initial values to random values between 0.5 and 10\n")
      }
    }
    beta.star.indx <- rep(0:(p.re - 1), n.re.long)
    beta.star.inits <- rnorm(n.re, 0, sqrt(sigma.sq.psi.inits[beta.star.indx + 1]))
  } else {
    sigma.sq.psi.inits <- 0
    beta.star.indx <- 0
    beta.star.inits <- 0
  }
  if (ar1) {
    # rho -----------------------------
    if ("rho" %in% names(inits)) {
      rho.inits <- inits[["rho"]]
      if (length(rho.inits) != 1) {
        stop("error: initial values for rho must be of length 1")
      }
    } else {
      rho.inits <- runif(1, rho.a, rho.b)
      if (verbose) {
        message("rho is not specified in initial values.\nSetting initial value to random value from the prior distribution\n")
      }
    }
    # sigma.sq.t ------------------------
    if ("sigma.sq.t" %in% names(inits)) {
      sigma.sq.t.inits <- inits[["sigma.sq.t"]]
      if (length(sigma.sq.t.inits) != 1) {
        stop("error: initial values for sigma.sq.t must be of length 1")
      }
    } else {
      sigma.sq.t.inits <- runif(1, 0.5, 10)
      if (verbose) {
        message("sigma.sq.t is not specified in initial values.\nSetting initial value to random value between 0.5 and 10\n")
      }
    }
  } else {
    rho.inits <- 0
    sigma.sq.t.inits <- 0
  }

  # Should initial values be fixed --
  if ("fix" %in% names(inits)) {
    fix.inits <- inits[["fix"]]
    if ((fix.inits != TRUE) & (fix.inits != FALSE)) {
      stop(paste("error: inits$fix must take value TRUE or FALSE"))
    }
  } else {
    fix.inits <- FALSE
  }
  if (verbose & fix.inits & (n.chains > 1)) {
    message("Fixing initial values across all chains\n")
  }

  # Covariance Model ----------------------------------------------------
  # Order must match util.cpp spCor.
  cov.model.names <- c("exponential", "spherical", "matern", "gaussian")
  if(! cov.model %in% cov.model.names){
    stop("error: specified cov.model '",cov.model,"' is not a valid option; choose from ", 
         paste(cov.model.names, collapse=", ", sep="") ,".")}
  # Obo for cov model lookup on c side
  cov.model.indx <- which(cov.model == cov.model.names) - 1

  # Prep for SVCs ---------------------------------------------------------
  X.w <- X[, svc.cols, drop = FALSE]

  # Get tuning values ---------------------------------------------------
  # Not accessed, but necessary to keep things in line. 
  sigma.sq.tuning <- rep(0, p.svc)
  phi.tuning <- rep(0, p.svc)
  nu.tuning <- rep(0, p.svc)
  rho.tuning <- 0
  sigma.sq.t.tuning <- 0
  if (missing(tuning)) {
    phi.tuning <- rep(1, p.svc)
    rho.tuning <- 1
    if (cov.model == 'matern') {
      nu.tuning <- rep(1, p.svc)
    }
    if (!sigma.sq.ig) {
      sigma.sq.tuning <- 1
    }
  } else {
    names(tuning) <- tolower(names(tuning))
    # phi ---------------------------
    if(!"phi" %in% names(tuning)) {
      stop("error: phi must be specified in tuning value list")
    }
    phi.tuning <- tuning$phi
    if (length(phi.tuning) == 1) {
      phi.tuning <- rep(tuning$phi, p.svc)
    } else if (length(phi.tuning) != p.svc) {
      stop(paste("error: phi tuning must be either a single value or a vector of length ",
      	   p.svc, sep = ""))
    }
    if (ar1) {
      # rho ---------------------------
      if(!"rho" %in% names(tuning)) {
        stop("error: rho must be specified in tuning value list")
      }
      rho.tuning <- tuning$rho
      if (length(rho.tuning) != 1) {
        stop("error: rho tuning must be a single value")
      } 
    }
    if (cov.model == 'matern') {
      # nu --------------------------
      if(!"nu" %in% names(tuning)) {
        stop("error: nu must be specified in tuning value list")
      }
      nu.tuning <- tuning$nu
      if (length(nu.tuning) == 1) {
        nu.tuning <- rep(tuning$nu, p.svc)
      } else if (length(nu.tuning) != p.svc) {
        stop(paste("error: nu tuning must be either a single value or a vector of length ",
        	   p.svc, sep = ""))
      }
    }
    if (!sigma.sq.ig) {
      # sigma.sq --------------------------
      if(!"sigma.sq" %in% names(tuning)) {
        stop("error: sigma.sq must be specified in tuning value list")
      }
      sigma.sq.tuning <- tuning$sigma.sq
      if (length(sigma.sq.tuning) == 1) {
        sigma.sq.tuning <- rep(tuning$sigma.sq, p.svc)
      } else if (length(sigma.sq.tuning) != p.svc) {
        stop(paste("error: sigma.sq tuning must be either a single value or a vector of length ",
		   p.svc, sep = ""))
      }
    }
  }
  # Log the tuning values since they are used in the AMCMC.
  # Need to shift the order depending on what's in the model. 
  if (ar1) {
    if (cov.model == 'matern') {
      tuning.c <- log(c(sigma.sq.tuning, phi.tuning, nu.tuning, 
			sigma.sq.t.tuning, rho.tuning))
    } else {
      tuning.c <- log(c(sigma.sq.tuning, phi.tuning,  
			sigma.sq.t.tuning, rho.tuning))
    }
  } else {
    tuning.c <- log(c(sigma.sq.tuning, phi.tuning, nu.tuning))
  }
  # Set model.deviance to NA for returning when no cross-validation
  model.deviance <- NA
  curr.chain <- 1

  if (!NNGP) {
    stop("error: svcTPGBinom is currently only implemented for NNGP models. Please set NNGP = TRUE") 
  } else {
    # Nearest Neighbor Search ---------------------------------------------
    if(verbose){
      cat("----------------------------------------\n");
      cat("\tBuilding the neighbor list\n");
      cat("----------------------------------------\n");
    }

    search.type.names <- c("brute", "cb")
    
    if(!search.type %in% search.type.names){
      stop("error: specified search.type '",search.type,
	   "' is not a valid option; choose from ", 
	   paste(search.type.names, collapse=", ", sep="") ,".")
    }
    
    ## Indexes
    if(search.type == "brute"){
      indx <- mkNNIndx(coords, n.neighbors, n.omp.threads)
    } else{
      indx <- mkNNIndxCB(coords, n.neighbors, n.omp.threads)
    }
    
    nn.indx <- indx$nnIndx
    nn.indx.lu <- indx$nnIndxLU
    nn.indx.run.time <- indx$run.time
    
    storage.mode(nn.indx) <- "integer"
    storage.mode(nn.indx.lu) <- "integer"

    if(verbose){
      cat("----------------------------------------\n");
      cat("Building the neighbors of neighbors list\n");
      cat("----------------------------------------\n");
    }
    
    indx <- mkUIndx(J, n.neighbors, nn.indx, nn.indx.lu, u.search.type)
    
    u.indx <- indx$u.indx
    u.indx.lu <- indx$u.indx.lu
    ui.indx <- indx$ui.indx
    u.indx.run.time <- indx$run.time

    # Set storage for all variables ---------------------------------------
    storage.mode(y) <- "double"
    storage.mode(X) <- "double"
    storage.mode(X.w) <- "double"
    consts <- c(J, p, p.re, n.re, n.years.max, p.svc)
    storage.mode(consts) <- "integer"
    storage.mode(weights) <- "double"
    storage.mode(coords) <- "double"
    storage.mode(beta.inits) <- "double"
    storage.mode(phi.inits) <- "double"
    storage.mode(sigma.sq.inits) <- "double"
    storage.mode(nu.inits) <- "double"
    storage.mode(w.inits) <- "double"
    storage.mode(z.year.indx) <- "integer"
    storage.mode(z.dat.indx) <- "integer"
    storage.mode(mu.beta) <- "double"
    storage.mode(Sigma.beta) <- "double"
    storage.mode(phi.a) <- "double"
    storage.mode(phi.b) <- "double"
    storage.mode(nu.a) <- "double"
    storage.mode(nu.b) <- "double"
    storage.mode(sigma.sq.a) <- "double"
    storage.mode(sigma.sq.b) <- "double"
    storage.mode(sigma.sq.ig) <- "integer"
    storage.mode(tuning.c) <- "double"
    storage.mode(n.batch) <- "integer"
    storage.mode(batch.length) <- "integer"
    storage.mode(accept.rate) <- "double"
    storage.mode(n.omp.threads) <- "integer"
    storage.mode(verbose) <- "integer"
    storage.mode(n.report) <- "integer"
    storage.mode(nn.indx) <- "integer"
    storage.mode(nn.indx.lu) <- "integer"
    storage.mode(u.indx) <- "integer"
    storage.mode(u.indx.lu) <- "integer"
    storage.mode(ui.indx) <- "integer"
    storage.mode(n.neighbors) <- "integer"
    storage.mode(cov.model.indx) <- "integer"
    storage.mode(n.burn) <- "integer"
    storage.mode(n.thin) <- "integer"
    storage.mode(curr.chain) <- "integer"
    storage.mode(n.chains) <- "integer"
    n.post.samples <- length(seq(from = n.burn + 1, 
				 to = n.samples, 
				 by = as.integer(n.thin)))
    storage.mode(n.post.samples) <- "integer"
    # For occurrence random effects
    storage.mode(X.re) <- "integer"
    beta.level.indx <- sort(unique(c(X.re)))
    storage.mode(beta.level.indx) <- "integer"
    storage.mode(sigma.sq.psi.inits) <- "double"
    storage.mode(sigma.sq.psi.a) <- "double"
    storage.mode(sigma.sq.psi.b) <- "double"
    storage.mode(n.re.long) <- "integer"
    storage.mode(beta.star.inits) <- "double"
    storage.mode(beta.star.indx) <- "integer"
    # AR1 parameters
    storage.mode(ar1) <- "integer"
    ar1.vals <- c(rho.a, rho.b, sigma.sq.t.a, sigma.sq.t.b, 
                  rho.inits, sigma.sq.t.inits)
    storage.mode(ar1.vals) <- "double"

    # svcTPGBinomNNGP
    out.tmp <- list()
    out <- list()
    if (!k.fold.only) {
      for (i in 1:n.chains) {
        # Change initial values if i > 1
        if ((i > 1) & (!fix.inits)) {
          beta.inits <- rnorm(p, mu.beta, sqrt(sigma.beta))
          if (sigma.sq.ig) {
            sigma.sq.inits <- rigamma(p.svc, sigma.sq.a, sigma.sq.b)
          } else {
            sigma.sq.inits <- runif(p.svc, sigma.sq.a, sigma.sq.b)
          }
          phi.inits <- runif(p.svc, phi.a, phi.b)
          if (cov.model == 'matern') {
            nu.inits <- runif(p.svc, nu.a, nu.b)
          }
          if (p.re > 0) {
            sigma.sq.psi.inits <- runif(p.re, 0.5, 10)
            beta.star.inits <- rnorm(n.re, 0, sqrt(sigma.sq.psi.inits[beta.star.indx + 1]))
          }
          if (ar1) {
            ar1.vals[5] <- runif(1, rho.a, rho.b)
            ar1.vals[6] <- runif(1, 0.5, 10)	
          }
        }
        storage.mode(curr.chain) <- "integer"
        out.tmp[[i]] <- .Call("svcTPGBinomNNGP", y, X, X.w, coords, X.re,  
                              consts, weights, n.re.long,
                              n.neighbors, nn.indx, nn.indx.lu, u.indx, u.indx.lu, ui.indx,
                              beta.inits, sigma.sq.psi.inits, beta.star.inits,
                              phi.inits, sigma.sq.inits, nu.inits,
                              w.inits, z.year.indx, z.dat.indx, 
                              beta.star.indx, beta.level.indx,
                              mu.beta, Sigma.beta,  
                              phi.a, phi.b, sigma.sq.a, sigma.sq.b, nu.a, nu.b,
                              sigma.sq.psi.a, sigma.sq.psi.b, ar1, ar1.vals,
                              tuning.c, cov.model.indx, n.batch, batch.length, accept.rate, 
                              n.omp.threads, verbose, n.report,  
                              n.burn, n.thin, n.post.samples, curr.chain, n.chains, sigma.sq.ig)
        curr.chain <- curr.chain + 1
      }

      # Get the names for theta which makes Rhat calc easier   
      if (cov.model != 'matern') {
        theta.names <- paste(rep(c('sigma.sq', 'phi'), each = p.svc), 
			     x.names[svc.cols], sep = '-')
      } else {
        theta.names <- paste(rep(c('sigma.sq', 'phi', 'nu'), each = p.svc), 
			     x.names[svc.cols], sep = '-')
      } 
      if (ar1) {
        theta.names <- c(theta.names, 'sigma.sq.t', 'rho')
      }
      n.theta <- length(theta.names)

      # Calculate R-Hat ---------------
      out <- list()
      out$rhat <- list()
      if (n.chains > 1) {
        out$rhat$beta <- gelman.diag(mcmc.list(lapply(out.tmp, function(a) 
        					      mcmc(t(a$beta.samples)))), 
        			     autoburnin = FALSE, multivariate = FALSE)$psrf[, 2]
        out$rhat$theta <- gelman.diag(mcmc.list(lapply(out.tmp, function(a) 
          					      mcmc(t(a$theta.samples)))), 
          			      autoburnin = FALSE, multivariate = FALSE)$psrf[, 2]
        if (p.re > 0) {
          out$rhat$sigma.sq.psi <- as.vector(gelman.diag(mcmc.list(lapply(out.tmp, function(a) 
          					       mcmc(t(a$sigma.sq.psi.samples)))), 
          			           autoburnin = FALSE, multivariate = FALSE)$psrf[, 2])
        }

      } else {
        out$rhat$beta <- rep(NA, p)
        out$rhat$theta <- rep(NA, n.theta)
        if (p.re > 0) {
          out$rhat$sigma.sq.psi <- rep(NA, p.re)
        }
      }

      # Put everything into an MCMC objects
      out$beta.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$beta.samples))))
      colnames(out$beta.samples) <- x.names
      out$theta.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$theta.samples))))
      colnames(out$theta.samples) <- theta.names
      if (ar1) {
        out$eta.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$eta.samples))))
      } 
      # Return things back in the original order
      out$coords <- coords[order(ord), ]
      out$X <- array(X, dim = c(J, n.years.max, p))
      out$X <- out$X[order(ord), , , drop = FALSE]
      dimnames(out$X)[[3]] <- x.names
      out$X.re <- array(X.re, dim = c(J, n.years.max, p.re))
      out$X.re <- out$X.re[order(ord), , , drop = FALSE]
      dimnames(out$X.re)[[3]] <- x.re.names
      out$X.w <- array(X.w, dim = c(J, n.years.max, p.svc))
      out$X.w <- out$X.w[order(ord), , , drop = FALSE]
      dimnames(out$X.w)[[3]] <- x.names[svc.cols]
      # Account for case when intercept only spatial model. 
      if (p.svc == 1) {
        tmp <- do.call(rbind, lapply(out.tmp, function(a) t(a$w.samples)))
        tmp <- tmp[, order(ord), drop = FALSE]
        out$w.samples <- array(NA, dim = c(n.post.samples * n.chains, p.svc, J))
        out$w.samples[, 1, ] <- tmp
      } else {
        out$w.samples <- do.call(abind, lapply(out.tmp, function(a) array(a$w.samples, 
          								dim = c(p.svc, J, n.post.samples))))
        out$w.samples <- out$w.samples[, order(ord), ]
        out$w.samples <- aperm(out$w.samples, c(3, 1, 2))
      }
      out$y.rep.samples <- do.call(abind, lapply(out.tmp, function(a) array(a$y.rep.samples, 
        								dim = c(J, n.years.max, n.post.samples))))
      out$y.rep.samples <- out$y.rep.samples[order(ord), , ]
      out$y.rep.samples <- aperm(out$y.rep.samples, c(3, 1, 2))
      out$psi.samples <- do.call(abind, lapply(out.tmp, function(a) array(a$psi.samples, 
        								dim = c(J, n.years.max, n.post.samples))))
      out$psi.samples <- out$psi.samples[order(ord), , ]
      out$psi.samples <- aperm(out$psi.samples, c(3, 1, 2))
      out$like.samples <- do.call(abind, lapply(out.tmp, function(a) array(a$like.samples, 
        								dim = c(J, n.years.max, n.post.samples))))
      out$like.samples <- out$like.samples[order(ord), , ]
      out$like.samples <- aperm(out$like.samples, c(3, 1, 2))
      out$y <- y.big[order(ord), , drop = FALSE]

      if (p.re > 0) {
        out$sigma.sq.psi.samples <- mcmc(
          do.call(rbind, lapply(out.tmp, function(a) t(a$sigma.sq.psi.samples))))
        colnames(out$sigma.sq.psi.samples) <- x.re.names
        out$beta.star.samples <- mcmc(
          do.call(rbind, lapply(out.tmp, function(a) t(a$beta.star.samples))))
        tmp.names <- unlist(re.level.names)
        beta.star.names <- paste(rep(x.re.names, n.re.long), tmp.names, sep = '-')
        colnames(out$beta.star.samples) <- beta.star.names
        out$re.level.names <- re.level.names
      }
      # Calculate effective sample sizes
      out$ESS <- list()
      out$ESS$beta <- effectiveSize(out$beta.samples)
      out$ESS$theta <- effectiveSize(out$theta.samples)
      if (p.re > 0) {
        out$ESS$sigma.sq.psi <- effectiveSize(out$sigma.sq.psi.samples)
      }
      out$call <- cl
      out$n.samples <- batch.length * n.batch
      out$n.neighbors <- n.neighbors
      out$cov.model.indx <- cov.model.indx
      out$svc.cols <- svc.cols
      out$type <- "NNGP"
      out$n.post <- n.post.samples
      out$n.thin <- n.thin
      out$n.burn <- n.burn
      out$n.chains <- n.chains
      out$ar1 <- as.logical(ar1)
      if (p.re > 0) {
        out$psiRE <- TRUE
      } else {
        out$psiRE <- FALSE
      }
    }
    # K-fold cross-validation ---------
    if (!missing(k.fold)) {
      if (verbose) {
        cat("----------------------------------------\n");
        cat("\tCross-validation\n");
        cat("----------------------------------------\n");
        message(paste("Performing ", k.fold, "-fold cross-validation using ", k.fold.threads,
        	        " thread(s).", sep = ''))
      }
      set.seed(k.fold.seed)
      # Number of sites in each hold out data set. 
      sites.random <- sample(1:J)    
      sites.k.fold <- split(sites.random, sites.random %% k.fold)
      registerDoParallel(k.fold.threads)
      model.deviance <- foreach (i = 1:k.fold, .combine = sum) %dopar% {
        curr.set <- sort(sites.random[sites.k.fold[[i]]])
	year.indx <- !((z.site.indx + 1) %in% curr.set)
        y.fit <- y[year.indx]
	y.0 <- y[!year.indx]
	y.big.fit <- y.big[-curr.set, , drop = FALSE]
	y.big.0 <- y.big[curr.set, , drop = FALSE]
	X.fit <- X[year.indx, , drop = FALSE]
	X.0 <- X[!year.indx, , drop = FALSE]
	X.w.fit <- X.w[year.indx, , drop = FALSE]
	X.w.0 <- X.w[!year.indx, , drop = FALSE]
        coords.fit <- coords[-curr.set, , drop = FALSE]
        coords.0 <- coords[curr.set, , drop = FALSE]
	J.fit <- nrow(coords.fit)
	J.0 <- nrow(coords.0)
	weights.fit <- weights[-curr.set, , drop = FALSE]
	weights.0 <- weights[curr.set, , drop = FALSE]
	# Random Occurrence Effects
        X.re.fit <- X.re[year.indx, , drop = FALSE]
        X.re.0 <- X.re[!year.indx, , drop = FALSE]
        n.re.fit <- length(unique(c(X.re.fit)))
        n.re.long.fit <- apply(X.re.fit, 2, function(a) length(unique(a)))
        if (p.re > 0) {	
          beta.star.indx.fit <- rep(0:(p.re - 1), n.re.long.fit)
          beta.level.indx.fit <- sort(unique(c(X.re.fit)))
          beta.star.inits.fit <- rnorm(n.re.fit, 0,
          			      sqrt(sigma.sq.psi.inits[beta.star.indx.fit + 1]))
          re.level.names.fit <- list()
          for (t in 1:p.re) {
            tmp.indx <- beta.level.indx.fit[beta.star.indx.fit == t - 1]
            re.level.names.fit[[t]] <- unlist(re.level.names)[tmp.indx + 1]    
          }
        } else {
          beta.star.indx.fit <- beta.star.indx
          beta.level.indx.fit <- beta.level.indx
          beta.star.inits.fit <- beta.star.inits
          re.level.names.fit <- re.level.names
        }
	# Get all the indices for linking to the new fitted data. 
        # Order of c(y.big): All sites year 1 v 1, All sites year 2 v 1, ....
        # Subtract 1 for indices in C
        z.site.indx.fit <- rep(1:J.fit, n.years.max) - 1
        z.year.indx.fit <- rep(1:n.years.max, each = J.fit) - 1
        z.dat.indx.fit <- c(ifelse(!is.na(weights.fit), 1, 0))

        # Nearest Neighbor Search ---
	verbose.fit <- FALSE
	n.omp.threads.fit <- 1
        ## Indexes
        if(search.type == "brute"){
          indx <- mkNNIndx(coords.fit, n.neighbors, n.omp.threads.fit)
        } else{
          indx <- mkNNIndxCB(coords.fit, n.neighbors, n.omp.threads.fit)
        }
        
        nn.indx.fit <- indx$nnIndx
        nn.indx.lu.fit <- indx$nnIndxLU
        
        indx <- mkUIndx(J.fit, n.neighbors, nn.indx.fit, 
      		  nn.indx.lu.fit, u.search.type)
        
        u.indx.fit <- indx$u.indx
        u.indx.lu.fit <- indx$u.indx.lu
        ui.indx.fit <- indx$ui.indx

        storage.mode(y.fit) <- "double"
        storage.mode(X.fit) <- "double"
        storage.mode(X.w.fit) <- "double"
        consts.fit <- c(J.fit, p, p.re, n.re.fit, 
			n.years.max, p.svc)
        storage.mode(consts.fit) <- "integer"
        storage.mode(weights.fit) <- "double"
        storage.mode(coords.fit) <- "double"
        storage.mode(z.year.indx.fit) <- "integer"
        storage.mode(z.dat.indx.fit) <- "integer"
	w.inits.fit <- rep(0, J.fit * p.svc)
	storage.mode(w.inits.fit) <- "double"
        storage.mode(n.omp.threads.fit) <- "integer"
        storage.mode(verbose.fit) <- "integer"
        storage.mode(nn.indx.fit) <- "integer"
        storage.mode(nn.indx.lu.fit) <- "integer"
        storage.mode(u.indx.fit) <- "integer"
        storage.mode(u.indx.lu.fit) <- "integer"
        storage.mode(ui.indx.fit) <- "integer"
	curr.chain <- 1
        storage.mode(curr.chain) <- "integer"
        # For occurrence random effects
        storage.mode(X.re.fit) <- "integer"
	storage.mode(n.re.long.fit) <- "integer"
        storage.mode(beta.level.indx.fit) <- "integer"
        storage.mode(beta.star.inits.fit) <- "double"
        storage.mode(beta.star.indx.fit) <- "integer"

        out.fit <- .Call("svcTPGBinomNNGP", y.fit, X.fit, X.w.fit, coords.fit, 
			 X.re.fit,  
                         consts.fit, weights.fit, n.re.long.fit, 
                         n.neighbors, nn.indx.fit, nn.indx.lu.fit, u.indx.fit, 
			 u.indx.lu.fit, ui.indx.fit,
                         beta.inits, sigma.sq.psi.inits, 
                         beta.star.inits.fit,  
			 phi.inits, sigma.sq.inits, nu.inits,
                         w.inits.fit, z.year.indx.fit,
                         z.dat.indx.fit,
                         beta.star.indx.fit, beta.level.indx.fit,
                         mu.beta, Sigma.beta,  
                         phi.a, phi.b, sigma.sq.a, sigma.sq.b, nu.a, nu.b,
                         sigma.sq.psi.a, sigma.sq.psi.b, 
			 ar1, ar1.vals,
                         tuning.c, cov.model.indx, n.batch, batch.length, accept.rate, 
                         n.omp.threads.fit, verbose.fit, n.report,  
                         n.burn, n.thin, n.post.samples, curr.chain, n.chains, sigma.sq.ig)

        out.fit$beta.samples <- mcmc(t(out.fit$beta.samples))
        colnames(out.fit$beta.samples) <- x.names
        out.fit$theta.samples <- mcmc(t(out.fit$theta.samples))
        if (ar1) {
          out.fit$eta.samples <- mcmc(t(out.fit$eta.samples))
	  out.fit$ar1 <- TRUE
        } else {
          out.fit$ar1 <- FALSE
	}
        # Account for case when intercept only spatial model. 
        if (p.svc == 1) {
          tmp <- t(out.fit$w.samples)
          out.fit$w.samples <- array(NA, dim = c(p.svc, J.fit, n.post.samples * n.chains))
          out.fit$w.samples[1, , ] <- tmp
        } else {
          out.fit$w.samples <- array(out.fit$w.samples, dim = c(p.svc, J.fit, n.post.samples))
        }
        out.fit$w.samples <- aperm(out.fit$w.samples, c(3, 1, 2))
        out.fit$X <- array(X.fit, dim = c(J.fit, n.years.max, p))
	dimnames(out.fit$X)[[3]] <- x.names
        out.fit$y <- y.big.fit
        out.fit$call <- cl
        out.fit$type <- "NNGP"
        out.fit$n.neighbors <- n.neighbors
        out.fit$n.samples <- n.samples
        out.fit$coords <- coords.fit
        out.fit$cov.model.indx <- cov.model.indx
        out.fit$svc.cols <- svc.cols
        out.fit$n.post <- n.post.samples
        out.fit$n.thin <- n.thin
        out.fit$n.burn <- n.burn
        out.fit$n.chains <- 1
        if (p.re > 0) {
          out.fit$sigma.sq.psi.samples <- mcmc(t(out.fit$sigma.sq.psi.samples))
          colnames(out.fit$sigma.sq.psi.samples) <- x.re.names
          out.fit$beta.star.samples <- mcmc(t(out.fit$beta.star.samples))
          tmp.names <- unlist(re.level.names.fit)
          beta.star.names <- paste(rep(x.re.names, n.re.long.fit), tmp.names, sep = '-')
          colnames(out.fit$beta.star.samples) <- beta.star.names
          out.fit$re.level.names <- re.level.names.fit
          out.fit$X.re <- array(X.re.fit, dim = c(J.fit, n.years.max, p.re))
	  dimnames(out.fit$X.re)[[3]] <- x.re.names
        }
        if (p.re > 0) {
          out.fit$psiRE <- TRUE
        } else {
          out.fit$psiRE <- FALSE	
        }
        class(out.fit) <- "svcTPGBinom"

	# Predict occurrence at new sites
        if (p.re > 0) {
          tmp <- unlist(re.level.names)
          X.re.0 <- matrix(tmp[c(X.re.0 + 1)], nrow(X.re.0), ncol(X.re.0))
          colnames(X.re.0) <- x.re.names
          X.0 <- cbind(X.0, X.re.0)
        }
	tmp.names <- colnames(X.0)
	X.0 <- array(X.0, dim = c(J.0, n.years.max, ncol(X.0)))
	dimnames(X.0)[[3]] <- tmp.names
        out.pred <- predict.svcTPGBinom(out.fit, X.0, coords.0, weights.0 = weights.0,
                                        t.cols = 1:n.years.max, verbose = FALSE)

	like.samples <- matrix(NA, nrow(X.0), ncol(X.0))
	for (j in 1:nrow(X.0)) {
          for (t in 1:ncol(X.0)) {
            like.samples[j, t] <- mean(dbinom(y.big.0[j, t], weights.0[j, t], 
	      			   out.pred$psi.0.samples[, j, t]))
	  }
        }
        sum(log(like.samples), na.rm = TRUE)
      } # parallel loop
      model.deviance <- -2 * model.deviance
      # Return objects from cross-validation
      out$k.fold.deviance <- model.deviance
      stopImplicitCluster()  
    } # cross-validation
  } # NNGP
  class(out) <- "svcTPGBinom"
  out$run.time <- proc.time() - ptm
  out
}