context("Test raster data prediction")
library(klrfome)
test_that("sim_trend and pred_var_stack", {
set.seed(717)
sigma = 0.5
lambda = 0.1
dist_metric = "euclidean"
sim_data <- get_sim_data(site_samples = 800, N_site_bags = 75)
formatted_data <- format_site_data(sim_data, N_sites=10, train_test_split=0.8,
sample_fraction = 0.9, background_site_balance=1)
train_data <- formatted_data[["train_data"]]
train_presence <- formatted_data[["train_presence"]]
test_data <- formatted_data[["test_data"]]
test_presence <- formatted_data[["test_presence"]]
K <- build_K(train_data, sigma = sigma, dist_metric = dist_metric, progress = FALSE)
train_log_pred <- KLR(K, train_presence, lambda, 100, 0.001, verbose = 0)
test_log_pred <- KLR_predict(test_data, train_data, dist_metric = dist_metric,
train_log_pred[["alphas"]], sigma, progress = FALSE)
params <- list(train_data = train_data,
alphas_pred = train_log_pred[["alphas"]],
sigma = sigma,
lambda = lambda,
means = formatted_data$means,
sds = formatted_data$sds)
### width and hieght of roving focal window (required)
ngb = 5
### Number of rows and columns in prediction rasters
## needed for making simulated rasters, as well as for predicting real-world rasters
cols = 50
rows = 50
### Create simulated environmental rasters (sim data only) ####
s_var1r <- NLMR::nlm_gaussianfield(cols,rows, autocorr_range = 20)
s_var1 <- rescale_sim_raster(s_var1r, 50, 10)
s_var2 <- rescale_sim_raster(s_var1r, 3, 2)
b_var1r <- NLMR::nlm_gaussianfield(cols,rows,autocorr_range = 20)
b_var1 <- rescale_sim_raster(b_var1r, 100, 20)
b_var2 <- rescale_sim_raster(b_var1r, 6, 3)
### Create a site-present trend surface (sim data only)
sim_sites_n = 3
trend_coords <- sim_trend(cols, rows, n = sim_sites_n)
coords <- trend_coords$coords
trend <- trend_coords$trend
inv_trend <- abs(1-trend)
var1 <- (s_var1 * trend) + (b_var1 * inv_trend)
var2 <- (s_var2 * trend) + (b_var2 * inv_trend)
#### end simulated data creation ####
### Create raster stack of predictor variables
pred_var_stack <- raster::stack(var1, var2)
names(pred_var_stack) <- c("var1","var2")
### scale rasters to training data
pred_var_stack_scaled <- scale_prediction_rasters(pred_var_stack, params, verbose = 0)
### Predict raster (single chunk, not in parallel)
pred_rast <- KLR_raster_predict(pred_var_stack_scaled, ngb = ngb, params, split = FALSE, ppside = NULL,
progress = FALSE, parallel = FALSE)
# check to make sure all sim site coordinates are reported
expect_true(nrow(trend_coords$coords) == sim_sites_n)
# check to make sure trend_coords has a raster
expect_is(trend_coords$trend, "RasterLayer")
# check to see that pred_var_stack is a raster stack
expect_is(pred_var_stack, "RasterStack")
# check to see that pred_var_stack_scaled is a raster stack
expect_is(pred_var_stack_scaled, "RasterStack")
# check to see that pred_rast is a raster
expect_is(pred_rast, "RasterLayer")
# check to make sure raster predictions are greater than or equal to zero
expect_true(all(range(pred_rast@data@values) >= 0))
})
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