test_that("geodist works with points and polygon in geographic space", { data(splotdata) studyArea <- rnaturalearth::ne_countries(continent = "South America", returnclass = "sf") set.seed(1) folds <- data.frame("folds"=sample(1:3, nrow(splotdata), replace=TRUE)) folds <- CreateSpacetimeFolds(folds, spacevar="folds", k=3) dist_geo <- geodist(x=splotdata, modeldomain=studyArea, cvfolds=folds$indexOut, type = "geo") mean_sample2sample <- round(mean(dist_geo[dist_geo$what=="sample-to-sample","dist"])) mean_CV_distances <- round(mean(dist_geo[dist_geo$what=="CV-distances","dist"])) nrow_dist <- nrow(dist_geo) expect_equal(mean_sample2sample, 20321) expect_equal(mean_CV_distances, 25616) expect_equal(nrow_dist, 3410) }) test_that("geodist works with points and polygon in feature space", { data(splotdata) studyArea <- rnaturalearth::ne_countries(continent = "South America", returnclass = "sf") set.seed(1) folds <- data.frame("folds"=sample(1:3, nrow(splotdata), replace=TRUE)) folds <- CreateSpacetimeFolds(folds, spacevar="folds", k=3) predictors <- terra::rast(system.file("extdata","predictors_chile.tif", package="CAST")) dist_fspace <- geodist(x = splotdata, modeldomain = predictors, cvfolds=folds$indexOut, type = "feature", variables = c("bio_1","bio_12", "elev")) mean_sample2sample <- round(mean(dist_fspace[dist_fspace$what=="sample-to-sample","dist"]), 4) mean_CV_distances <- round(mean(dist_fspace[dist_fspace$what=="CV-distances","dist"]), 4) expect_equal(mean_sample2sample, 0.0843) expect_equal(mean_CV_distances, 0.1036) }) test_that("geodist works space with points and preddata in geographic space", { aoi <- sf::st_as_sfc("POLYGON ((0 0, 10 0, 10 10, 0 10, 0 0))", crs="epsg:25832") tpoints <- sf::st_as_sfc("MULTIPOINT ((1 1), (1 2), (2 2), (2 3), (1 4), (5 4))", crs="epsg:25832") |> sf::st_cast("POINT") set.seed(1) ppoints <- suppressWarnings(sf::st_sample(aoi, 20, type="regular")) |> sf::st_set_crs("epsg:25832") set.seed(1) folds <- data.frame("folds"=sample(1:3, length(tpoints), replace=TRUE)) folds <- CreateSpacetimeFolds(folds, spacevar="folds", k=3) dist_geo <- geodist(x=tpoints, modeldomain=aoi, preddata=ppoints, type = "geo") mean_sample2sample <- round(mean(dist_geo[dist_geo$what=="sample-to-sample","dist"]), 4) mean_prediction_to_sample <- round(mean(dist_geo[dist_geo$what=="prediction-to-sample","dist"]), 4) expect_equal(mean_sample2sample, 1.4274) expect_equal(mean_prediction_to_sample, 2.9402) }) test_that("geodist works with points and preddata in feature space", { aoi <- sf::st_as_sfc("POLYGON ((0 0, 10 0, 10 10, 0 10, 0 0))", crs="epsg:25832") tpoints <- sf::st_as_sfc("MULTIPOINT ((1.5 1.5), (1.5 2.5), (2.5 2.5), (2.5 3.5), (1.5 4.5), (5.5 4.5))", crs="epsg:25832") |> sf::st_cast("POINT") set.seed(1) ppoints <- suppressWarnings(sf::st_sample(aoi, 20, type="regular")) |> sf::st_set_crs("epsg:25832") raster <- terra::rast(nrows=10, ncols=10, nlyrs=1, xmin=0, xmax=10, ymin=0, ymax=10, crs="epsg:25832", vals=1:100) dist <- geodist(x=tpoints, modeldomain=raster, preddata=ppoints, type = "feature") mean_sample2sample <- round(mean(dist[dist$what=="sample-to-sample","dist"]), 4) mean_prediction_to_sample <- round(mean(dist[dist$what=="prediction-to-sample","dist"]), 4) expect_equal(mean_sample2sample, 0.3814) expect_equal(mean_prediction_to_sample, 1.0783) }) test_that("geodist works with points and raster in geographic space", { tpoints <- sf::st_as_sfc("MULTIPOINT ((1 1), (1 2), (2 2), (2 3), (1 4), (5 4))", crs="epsg:25832") |> sf::st_cast("POINT") raster <- terra::rast(nrows=10, ncols=10, nlyrs=1, xmin=0, xmax=10, ymin=0, ymax=10, crs="epsg:25832", vals=1:100) dist <- geodist(x=tpoints, modeldomain=raster, type = "geo") mean_sample2sample <- round(mean(dist[dist$what=="sample-to-sample","dist"]), 4) expect_equal(mean_sample2sample, 1.4274) }) test_that("geodist works with points and raster in feature space", { tpoints <- sf::st_as_sfc("MULTIPOINT ((1.5 1.5), (1.5 2.5), (2.5 2.5), (2.5 3.5), (1.5 4.5), (5.5 4.5))", crs="epsg:25832") |> sf::st_cast("POINT") raster <- terra::rast(nrows=10, ncols=10, nlyrs=1, xmin=0, xmax=10, ymin=0, ymax=10, crs="epsg:25832", vals=1:100) dist <- geodist(x=tpoints, modeldomain=raster, type = "feature") mean_sample2sample <- round(mean(dist[dist$what=="sample-to-sample","dist"]), 4) expect_equal(mean_sample2sample, 0.3814) }) test_that("geodist works with points and stars raster in geographic space", { tpoints <- sf::st_as_sfc("MULTIPOINT ((1.5 1.5), (1.5 2.5), (2.5 2.5), (2.5 3.5), (1.5 4.5), (5.5 4.5))", crs="epsg:25832") |> sf::st_cast("POINT") raster <- terra::rast(nrows=10, ncols=10, nlyrs=1, xmin=0, xmax=10, ymin=0, ymax=10, crs="epsg:25832", vals=1:100) |> stars::st_as_stars() dist <- geodist(x=tpoints, modeldomain=raster, type = "feature") mean_sample2sample <- round(mean(dist[dist$what=="sample-to-sample","dist"]), 4) expect_equal(mean_sample2sample, 0.3814) }) test_that("geodist works with points and test data in geographic space", { aoi <- sf::st_as_sfc("POLYGON ((0 0, 10 0, 10 10, 0 10, 0 0))", crs="epsg:25832") tpoints <- sf::st_as_sfc("MULTIPOINT ((1 1), (1 2), (2 2), (2 3), (1 4), (5 4))", crs="epsg:25832") |> sf::st_cast("POINT") set.seed(1) test_point <- suppressWarnings(sf::st_sample(aoi, 20, type="regular")) |> sf::st_set_crs("epsg:25832") dist <- geodist(x=tpoints, modeldomain=aoi, testdata=test_point, type = "geo") mean_sample2sample <- round(mean(dist[dist$what=="sample-to-sample","dist"]), 4) mean_test_to_sample <- round(mean(dist[dist$what=="test-to-sample","dist"]), 4) expect_equal(mean_sample2sample, 1.4274) expect_equal(mean_test_to_sample, 2.9402) }) test_that("geodist works with points and test data in feature space", { aoi <- sf::st_as_sfc("POLYGON ((0 0, 10 0, 10 10, 0 10, 0 0))", crs="epsg:25832") raster <- terra::rast(nrows=10, ncols=10, nlyrs=1, xmin=0, xmax=10, ymin=0, ymax=10, crs="epsg:25832", vals=1:100) tpoints <- sf::st_as_sfc("MULTIPOINT ((1 1), (1 2), (2 2), (2 3), (1 4), (5 4))", crs="epsg:25832") |> sf::st_cast("POINT") set.seed(1) test_points <- suppressWarnings(sf::st_sample(aoi, 20, type="random")) |> sf::st_set_crs("epsg:25832") dist <- geodist(x=tpoints, modeldomain=raster, testdata = test_points, type = "feature") mean_sample2sample <- round(mean(dist[dist$what=="sample-to-sample","dist"]), 4) mean_test_to_sample <- round(mean(dist[dist$what=="test-to-sample","dist"]), 4) expect_equal(mean_sample2sample, 0.3814) expect_equal(mean_test_to_sample, 1.4524) }) test_that("geodist works with categorical variables in feature space", { set.seed(1234) predictor_stack <- terra::rast(system.file("extdata","predictors_2012-03-25.tif",package="CAST")) predictors <- c("DEM","TWI", "NDRE.M", "Easting", "Northing", "fct") predictor_stack$fct <- factor(c(rep(LETTERS[1], terra::ncell(predictor_stack)/2), rep(LETTERS[2], terra::ncell(predictor_stack)/2))) predictor_stack <- predictor_stack[[predictors]] studyArea <- predictor_stack studyArea[!is.na(studyArea)] <- 1 studyArea <- terra::as.polygons(studyArea, values = FALSE, na.all = TRUE) |> sf::st_as_sf() |> sf::st_union() pts <- clustered_sample(studyArea, 30, 5, 60) pts <- sf::st_transform(pts, crs = sf::st_crs(studyArea)) pts <- terra::extract(predictor_stack, terra::vect(pts), ID=FALSE, bind=TRUE) |> sf::st_as_sf() test_pts <- clustered_sample(studyArea, 50, 5, 20) folds <- data.frame("folds"=sample(1:3, nrow(pts), replace=TRUE)) folds <- CreateSpacetimeFolds(folds, spacevar="folds", k=3) sf::st_as_sf(terra::extract(predictor_stack,terra::vect(pts$geometry),bind=TRUE)) dist <- geodist(x=pts, modeldomain=predictor_stack, type = "feature", testdata = test_pts, cvfolds = folds$indexOut) mean_sample2sample <- round(mean(dist[dist$what=="sample-to-sample","dist"]), 4) mean_prediction2sample <- round(mean(dist[dist$what=="prediction-to-sample","dist"]), 4) mean_test2sample <- round(mean(dist[dist$what=="test-to-sample","dist"]), 4) mean_CV_distance <- round(mean(dist[dist$what=="CV-distances","dist"]), 4) expect_equal(mean_sample2sample, 0.0459) expect_equal(mean_prediction2sample, 0.1625) expect_equal(mean_test2sample, 0.2358) expect_equal(mean_CV_distance, 0.0663) }) test_that("geodist works in temporal space", { data(cookfarm) dat <- sf::st_as_sf(cookfarm,coords=c("Easting","Northing")) sf::st_crs(dat) <- 26911 trainDat <- dat[dat$altitude==-0.3&lubridate::year(dat$Date)==2010,] predictionDat <- dat[dat$altitude==-0.3&lubridate::year(dat$Date)==2011,] dist <- CAST::geodist(trainDat,preddata = predictionDat,type="time",time_unit="days") mean_sample2sample <- round(mean(dist[dist$what=="sample-to-sample","dist"]), 4) mean_prediction_to_sample <- round(mean(dist[dist$what=="prediction-to-sample","dist"]), 4) expect_equal(mean_sample2sample, 0.02) expect_equal(mean_prediction_to_sample, 194.7656) dist <- CAST::geodist(trainDat,preddata = predictionDat,type="time",time_unit="hours") mean_prediction_to_sample <- round(mean(dist[dist$what=="prediction-to-sample","dist"]), 2) expect_equal(mean_prediction_to_sample, 4674.37) }) test_that("geodist works in temporal space and with CV", { data(cookfarm) dat <- sf::st_as_sf(cookfarm,coords=c("Easting","Northing")) sf::st_crs(dat) <- 26911 trainDat <- dat[dat$altitude==-0.3&lubridate::year(dat$Date)==2010,] predictionDat <- dat[dat$altitude==-0.3&lubridate::year(dat$Date)==2011,] trainDat$week <- lubridate::week(trainDat$Date) set.seed(100) cvfolds <- CreateSpacetimeFolds(trainDat,timevar = "week") dist <- CAST::geodist(trainDat,preddata = predictionDat,cvfolds = cvfolds$indexOut, type="time",time_unit="days") mean_cv <- round(mean(dist[dist$what=="CV-distances","dist"]), 4) expect_equal(mean_cv, 2.4048) } )