Bundled rasters, point files, and prediction outputs

Several non-R objects ship in inst/extdata/ for use in function examples and the package vignette. They cannot be portably serialized as .rda files, so they are stored as GeoTIFF and CSV files and loaded via system.file.

Details

extdata/rasters_raw/: Raw synthetic environmental rasters before alignment. Includes elevation.tif, forest_cover_1.tif through forest_cover_15.tif, pr_ann_1.tif through pr_ann_15.tif, and prseas_<year>_<season>.tif for each of 15 years and 4 seasons (60 files).
extdata/rasters_aligned/: Same rasters after raster_align has reprojected and masked them to the reference grid.
extdata/rasters_scaled/: Aligned rasters z-scored using the scaling parameters from the seasonal extraction. Used by examples and modeling functions that work with the seasonal predictor set.
extdata/rasters_scaled_annual/: Aligned rasters z-scored using the scaling parameters from the annual extraction. Used by examples and modeling functions that work with the annual predictor set (pr_ann instead of prseas).
extdata/points/synthetic_occurrence_points.csv: The raw synthetic presence dataset: 150 points with x, y, year, season, and pres = 1.
extdata/points/synthetic_occurrence_points.shp: Same points as a shapefile.
extdata/points/synthetic_user_presences.csv: A second-species presence dataset for demonstrating method = "user_data" in generate_absences. Points were derived from buffer- constrained environmental pseudoabsences of the primary species and reformatted as presences (pres = 1). Same column structure as synthetic_occurrence_points.csv (x, y, year, season, pres).
extdata/points/extracted_seasonal_*.csv: Outputs from temporally_explicit_extraction using the seasonal predictor set: _Raw_Values.csv, _Scaled_Values.csv, and _Scaling_Parameters.csv.
extdata/points/extracted_annual_*.csv: Same outputs but using the annual predictor set.
extdata/predictions/: Per-timestep fold-vote rasters from the seasonal workflow's generate_spatiotemporal_predictions call. Fifteen files (Prediction_<year>_Spring.tif) suitable for direct input to summarize_raster_outputs.
extdata/binary/consensus_stack.tif: Multi-layer GeoTIFF of binary suitable / unsuitable rasters produced by summarize_raster_outputs with consensus = 3. Fifteen layers, one per year, ordered 1 through 15.
extdata/binary/frequency_raster.tif: Companion single-layer raster giving the proportion of years each pixel was classified as suitable.
extdata/precomputed/: Precomputed prediction outputs read directly by the modeling vignettes (V3a-V3d) so that generate_spatiotemporal_predictions does not need to be rerun at vignette build time. One subdirectory per model type (glm/, gam/, rf/, hv/), each containing preds.rds and a pred_tifs/ folder. preds.rds is the list returned by generate_spatiotemporal_predictions for that model ($timestep_metrics, $overall_summary, $prediction_files, and $model_type), with $prediction_files reduced to bare file names rather than absolute build-time paths. pred_tifs/ holds the 60 per-timestep fold-vote rasters (Prediction_<year>_<season>.tif, 15 years x 4 seasons) projected from that model. The vignettes load preds.rds and rebuild $prediction_files from pred_tifs/ via system.file.

Example load patterns:


  ### Aligned raster directory
  aln_dir <- system.file("extdata/rasters_aligned",
                         package = "TemporalModelR")

  ### Consensus stack (multi-layer)
  binary_stack <- terra::rast(system.file(
    "extdata/binary/consensus_stack.tif", package = "TemporalModelR"
  ))

  ### Frequency raster
  frequency_rast <- terra::rast(system.file(
    "extdata/binary/frequency_raster.tif", package = "TemporalModelR"
  ))

  ### Per-timestep prediction directory
  pred_dir <- system.file("extdata/predictions",
                          package = "TemporalModelR")

  ### Precomputed GLM prediction object and its rasters
  glm_preds <- readRDS(system.file(
    "extdata/precomputed/glm/preds.rds", package = "TemporalModelR"
  ))
  glm_pred_files <- list.files(
    system.file("extdata/precomputed/glm/pred_tifs",
                package = "TemporalModelR"),
    pattern = "\.tif$", full.names = TRUE
  )