add data-generating script again

data-raw/generate_minimal_example_data.R

@@ -0,0 +1,55 @@
+## Here we generate data for the minimal example and for test_generate_commonness_matrix_from_gdm.R 
+
+# library("gdm")
+
+# create random species composition
+set.seed(42) 
+
+nspecies <- 20
+nsites <- 15
+presence_prob <- 0.3 # probability for each species to be present at each site
+
+get_species_list <- function(nspecies, nsites, presence_prob)
+{
+  # generate a siteXspecies list with a random set of species presences/absences
+  # fill list with random species presences
+  m <- matrix(nrow = nspecies, ncol = nsites, data = 0)
+
+  for (row in 1:ncol(m)) {
+    for (col in 1:nrow(m)) {
+      if (runif(1) < presence_prob) {
+        m[col, row] <- 1
+      }
+    }
+  }
+
+  mode(m) <- "integer"
+
+  return(m)
+}
+
+# random species composition
+species_list <- get_species_list(nspecies = nspecies, nsites = nsites, 
+                                 presence_prob = presence_prob) 
+
+# calculation of Bray-Curtis dissimilarity with the gdm package: 
+# bioData is required by the gdm package, but does not affect 
+# the observed Bray-Curtis dissimilarity we will use later
+bioData <- data.frame(site_id = 1:nsites, x_coords = rep(13, nsites), 
+                      y_coords = rep(10, nsites)) 
+
+bioData <- cbind(bioData, t(species_list)) 
+
+predData <- data.frame(site_id = 1:nsites, preds = runif(nsites))
+
+sitepairs <- gdm::formatsitepair(bioData = bioData, bioFormat = 1, abundance = FALSE, 
+                                 siteColumn = "site_id",
+                                 XColumn = "x_coords", YColumn = "y_coords", 
+                                 predData = predData)
+
+alpha_div <- colSums(species_list) 
+gdm_result <- gdm::gdm(sitepairs, geo = TRUE) 
+
+minimal_example_data <- list(species_list = species_list, alpha_list = alpha_div, beta_list = gdm_result$observed)
+
+usethis::use_data(minimal_example_data)