reorders domains in areas

R/smoothArea.R

@@ -403,7 +403,7 @@ plot.svysae <- function(x, return_list = F, plot.factor = NULL, ...) {
  split(combined_est, combined_est$plot),
  function(x) {
  ggplot2::ggplot(x, ggplot2::aes(x = domain, y = mean, color = method)) +
- ggplot2::geom_point(position = position_dodge(width = 0.5)) + 
+ ggplot2::geom_point(position = ggplot2::position_dodge(width = 0.5)) + 
  ggplot2::geom_linerange(ggplot2::aes(x = domain, ymin = lower, ymax = upper), 
  position = ggplot2::position_dodge(width = 0.5)) + 
  ggplot2::scale_color_discrete(name = "Method") + 

R/smoothUnit.R

@@ -157,7 +157,7 @@ smoothUnit <- function(formula,
  ftxt <- paste("resp ~ 1")
  if (length(all.vars(cov.frm)) > 0) {
  ftxt <- 
- paste(ftxt, paste(all.vars(cov.frm), collapse = " + "), sep = " + ")
+ paste(ftxt, as.character(cov.frm)[-1], sep = " + ")
  }
 
  if (is.null(adj.mat)) {
@@ -177,7 +177,6 @@ smoothUnit <- function(formula,
  model.method <- "bym2.model"
  ftxt <- paste0(ftxt, " + f(domain.struct, model = 'bym2', graph=adj.mat, hyper = hyperpc.bym.int)")
  }
-
  # fit model
  mod.frm <- as.formula(ftxt)
  fit <- INLA::inla(mod.frm, family = family, data = mod.dat,
@@ -192,7 +191,7 @@ smoothUnit <- function(formula,
 
  # identify indices of fixed effects and random effects
  fe.idx <- grep(colnames(mm.pop)[1], rownames(samp.all[[1]]$latent))
- fe.idx <- fe.idx:(fe.idx + length(all.vars(cov.frm)))
+ fe.idx <- fe.idx:(fe.idx + ncol(mm.pop) - 1)
  re.idx <- grep("domain.struct", x = rownames(samp.all[[1]]$latent))
 
  # aggregate sample predictions