fix pp

Author: elgabbas

.lintr

@@ -101,7 +101,7 @@ exclusions: list(
 	"R/dwf_river_length.R" = list(line_length_linter = Inf),
 	"R/mod_prepare_knots.R" = list(unnecessary_nesting_linter = 70:80),
 	"R/mod_prepare_hpc.R" = list(line_length_linter = Inf),
-	"R/mod_potprocess.R" = list(line_length_linter = 1011:1062),
+	"R/mod_postprocess.R" = list(line_length_linter = 1011:1062),
 	"R/dwf_wetness_index.R" = list(line_length_linter = Inf),
 	"R/dwf_soil.R" = list(line_length_linter = Inf)
 	)

DESCRIPTION

@@ -3,7 +3,7 @@ Package: IASDT.R
 Title: Modelling the distribution of invasive alien plant species in
     Europe
 Version: 0.1.09
-Date: 2025-09-21
+Date: 2025-09-23
 Authors@R: 
     person("Ahmed", "El-Gabbas", , "ahmed.el-gabbas@ufz.de", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-2225-088X"))
@@ -25,7 +25,7 @@ Imports:
     fs (>= 1.6.4),
     future (>= 1.34.0),
     future.apply (>= 1.11.3),
-    ggplot2 (>= 3.5.1),
+    ggplot2 (<= 3.5.2),
     Hmsc,
     Rcpp (>= 1.0.13),
     rlang (>= 1.1.4),
@@ -140,4 +140,4 @@ Encoding: UTF-8
 Language: en-GB
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.2
+RoxygenNote: 7.3.3

R/mod_cv_evaluate.R

@@ -64,7 +64,7 @@ mod_cv_evaluate <- function(model_dir = NULL, cv_type = "cv_dist") {
 
   # Model predictions -----
   model_predictions <- fs::path(
-    model_dir, "model_prediction", "no_clamp", "prediction_current_R.qs2")
+    model_dir, "model_prediction", "no_clamp", "prediction_current_r.qs2")
   if (!ecokit::check_data(model_predictions)) {
     ecokit::stop_ctx(
       "Model prediction file does not exist",

R/mod_predict_hmsc.R

@@ -526,19 +526,19 @@ predict_hmsc <- function(
   ecokit::cat_time(
     "Save ppEta / post as small chunks", level = 1L, verbose = verbose)
   chunk_size <- 25
-  chunkIDs <- ceiling(seq_along(post) / chunk_size)
+  chunk_ids <- ceiling(seq_along(post) / chunk_size)
   chunks <- purrr::map_chr(
-    .x = seq_len(max(chunkIDs)),
+    .x = seq_len(max(chunk_ids)),
     .f = ~ {
-      IDs <- which(chunkIDs == .x)
+      IDs <- which(chunk_ids == .x)
       Ch <- list(ppEta = ppEta[IDs], post = post[IDs])
       chunk_file <- fs::path(
         temp_dir, paste0(model_name, "_preds_ch", .x, ".qs2"))
       ecokit::save_as(object = Ch, out_path = chunk_file)
       return(chunk_file)
     })
 
-  rm(chunkIDs, post, ppEta, envir = environment())
+  rm(chunk_ids, post, ppEta, envir = environment())
   invisible(gc())
 
   seeds <- sample.int(.Machine$integer.max, predN)
@@ -866,7 +866,6 @@ get1prediction <- function(
 
   updateZ <- updateEta <- NULL
 
-
   if (!is.null(seed)) {
     set.seed(seed)
   }

R/mod_predict_maps.R

@@ -259,7 +259,6 @@ predict_maps <- function(
   ## Species information -----
 
   species_info <- IASDT.R::get_species_name(env_file = env_file) %>%
-    janitor::clean_names() %>%
     dplyr::select(ias_id, taxon_name, species_name, class, order, family)
 
   # # |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| #
@@ -418,7 +417,9 @@ predict_maps <- function(
     # Models are trained and predictions are made only at grid cells with > 0 %
     # coverage. Mask layer to exclude grid cells with zero % coverage from
     # predictions.
-    r_hab_mask <- terra::classify(r_hab, cbind(0, NA), others = 1)
+    # r_hab_mask <- terra::classify(r_hab, cbind(0, NA), others = 1)
+    # Update 2025_09: make predictions at the full study area, and if needed
+    # predictions at 0% coverage could be masked
 
     r_hab <- stats::setNames(log10(r_hab + 0.1), "habitat_log")
 
@@ -759,13 +760,8 @@ predict_maps <- function(
   ## Merge static predictors -----
   ecokit::cat_time("Merge static predictors", level = 1L)
 
-  static_predictors <- terra::rast(static_predictors)
-
-  # If Habitat predictor is used, grid cells with zero % coverage are
-  # excluded from predictions
-  if (hab_predictor) {
-    static_predictors <- terra::mask(static_predictors, r_hab_mask)
-  }
+  static_predictors <- terra::rast(static_predictors) %>%
+    terra::mask(ecokit::load_as(path_grid_r, unwrap_r = TRUE))
 
   # # ..................................................................... ###
   # # ..................................................................... ###
@@ -871,7 +867,7 @@ predict_maps <- function(
     do_clamp <- prediction_options$clamp[[id]]
 
     # Name of the current option
-    option_name <- prediction_options$climate_name[[id]]
+    option_name <- prediction_options$name[[id]]
 
     # Name of the current model
     model_name <- paste0(
@@ -929,7 +925,7 @@ predict_maps <- function(
     path_prediction_sf <- fs::path(
       path_prediction, paste0("prediction_", option_name, "_sf.qs2"))
     path_prediction_r <- fs::path(
-      path_prediction, paste0("prediction_", option_name, "_R.qs2"))
+      path_prediction, paste0("prediction_", option_name, "_r.qs2"))
     path_prediction_summary <- fs::path(
       path_prediction, paste0("prediction_", option_name, "_summary.RData"))
 
@@ -1140,7 +1136,7 @@ predict_maps <- function(
           # use relevant folder containing the current predictions. This is
           # determined by `path_prediction`, which is not the same whether
           # clamping is used or not
-          path = path_prediction, pattern = "prediction_current.*_R.qs2",
+          path = path_prediction, pattern = "prediction_current.*_r.qs2",
           full.names = TRUE) %>%
           ecokit::load_as(unwrap_r = TRUE) %>%
           terra::subset(mean_names)
@@ -1326,7 +1322,7 @@ predict_maps <- function(
     current_mean <- list.files(
       path = dplyr::if_else(
         clamp_pred, path_prediction_clamp, path_prediction_no_clamp),
-      pattern = "prediction_current.*_R.qs2", full.names = TRUE)
+      pattern = "prediction_current.*_r.qs2", full.names = TRUE)
 
     # --------------------------------------------------------- #
 
@@ -1429,7 +1425,7 @@ predict_maps <- function(
             clamp_pred, path_prediction_clamp, path_prediction_no_clamp),
           paste0(
             "prediction_", stringr::str_replace(time_period, "-", "_"), "_",
-            climate_scenario, "_ensemble_R.qs2"))) %>%
+            climate_scenario, "_ensemble_r.qs2"))) %>%
       dplyr::select(tidyselect::all_of(vars_to_select)) %>%
       tidyr::nest(data = -ensemble_file)
 
@@ -1525,7 +1521,7 @@ predict_maps <- function(
           climate_scenario, "_ensemble"),
         file_pred_sf = NA_character_,
         file_pred_r = stringr::str_replace(
-          file_pred_summary, "_summary.RData", "_R.qs2")) %>%
+          file_pred_summary, "_summary.RData", "_r.qs2")) %>%
       dplyr::distinct()
   }
   # # ..................................................................... ###

R/mod_predict_maps_cv.R

@@ -120,7 +120,7 @@ predict_maps_cv <- function(
     path_prediction, paste0("prediction_", model_name, ".qs2"))
   path_eval_file <- fs::path(path_eval, paste0("eval_", model_name, ".qs2"))
   path_preds_r <- fs::path(
-    path_prediction, paste0("prediction_", model_name, "_R.qs2"))
+    path_prediction, paste0("prediction_", model_name, "_r.qs2"))
   path_preds_summary <- fs::path(
     path_prediction, paste0("prediction_", model_name, "_summary.RData"))
 

R/mod_slurm_refit.R

@@ -40,7 +40,7 @@ mod_slurm_refit <- function(
   # # ..................................................................... ###
 
   if (is.null(job_name)) {
-    job_name <- paste0(basename(model_dir), "_RF")
+    job_name <- paste0(basename(model_dir), "_refit")
   }
 
   # # ..................................................................... ###

README.md

@@ -81,4 +81,4 @@ If you use the `IASDT.R` package, please cite it as:
 > <a href="https://biodt.eu" target="_blank">https://biodt.eu</a>.
 
 <span style="     color: grey !important;">Last update:
-2025-09-21</span>
+2025-09-23</span>