Only pass one raster, and the one that is in memory.

R-package/R/inner_procedure.r

@@ -112,7 +112,7 @@ sample_nb <- function(raster,
       res <- parallel::mclapply(
         1:nb_mean,
         model_opt_r,
-        raster = raster,
+        raster = rast,
         sample_type = sample_type,
         buffer = buffer,
         model = model,
@@ -124,7 +124,6 @@ sample_nb <- function(raster,
         mtry = mtry,
         mod.error = mod.error,
         pbtn1 = pbtn1,
-        rast = rast,
         max_samples_per_class = max_samples_per_class,
         mc.cores = cores,
         mc.preschedule = TRUE,
@@ -138,7 +137,7 @@ sample_nb <- function(raster,
     } else {
       for (k in 1:nb_mean) {
         res <- model_opt_r(
-          raster = raster,
+          raster = rast,
           sample_type = sample_type,
           buffer = buffer,
           model = model,
@@ -151,7 +150,6 @@ sample_nb <- function(raster,
           mtry = mtry,
           mod.error = mod.error,
           pbtn1 = pbtn1,
-          rast = rast,
           max_samples_per_class = max_samples_per_class
         )
         points_list[[k]] <- res$points

R-package/R/model_opt.r

@@ -15,7 +15,6 @@
 #' @param mtry number of predictor used at random forest splitting nodes (mtry << n predictors)
 #' @param mod.error threshold for model error until which iteration is being executed
 #' @param pbtn1 matrix for points
-#' @param rast raster
 #' @param max_samples_per_class maximum number of samples per class
 #' 
 #' @return a list with 4 elements:
@@ -37,12 +36,11 @@ model_opt_r <- function(k,
                         mtry,
                         mod.error,
                         pbtn1,
-                        rast,
                         max_samples_per_class) {
   points <- NULL
   models <- NULL
   oobe <- matrix(NA, nrow = n, ncol = 1)
-  rast <- raster::mask(rast, raster::calc(rast, fun = sum))
+  rast <- raster::mask(raster, raster::calc(raster, fun = sum))
   for (j in 1:n) {
     ###Vorbereitung Klassifizierung
     if (j == 1) {
@@ -152,7 +150,7 @@ model_opt_r <- function(k,
       nam <-
         as.vector(fasterize::fasterize(sf::st_as_sf(poly), rast[[1]], field = "V1"))[-attr(test1, "na.action")]
       
-      co<-raster::xyFromCell(rast, c(1:raster::ncell(rast))[-attr(test1,"na.action")])
+      co <- raster::xyFromCell(rast, c(1:raster::ncell(rast))[-attr(test1,"na.action")])
       pbtn1 <- as.data.frame(cbind(nam, co))
       sp::coordinates(pbtn1) <- c("x", "y")
                  

R-package/man/model_opt_r.Rd

@@ -18,7 +18,6 @@ model_opt_r(
   mtry,
   mod.error,
   pbtn1,
-  rast,
   max_samples_per_class
 )
 }
@@ -49,8 +48,6 @@ model_opt_r(
 
 \item{pbtn1}{matrix for points}
 
-\item{rast}{raster}
-
 \item{max_samples_per_class}{maximum number of samples per class}
 }
 \value{