Checks:

[rvosa]

• de volgorde van de namen van de layers (die in een vector stonden) klopte niet in relatie tot de volgorde waarin de lagen als TIF bestanden werden ingelezen. Vermoedelijk is het dus zo dat alle variable importance plots daardoor niet klopten.

  trait-geo-diverse-ungulates/doc/Niche_Overlap.Rmd

  Lines 103 to 131 in 2e8261f

  	files.names <- list.files(paste(REPO_HOME, "/data/GIS/5_deg", sep = ""))
  	# Turn the file names into layer names: strip the prefix, which might include
  	# the resolution, and strip the file extension
  	gis.layers.names <- files.names
  	gis.layers.names <- gsub('current_5arcmin_','',gis.layers.names)
  	gis.layers.names <- gsub('.tif','',gis.layers.names)
  	# Combine the layer names with those we've already read from BIOCLIM
  	gis.layers.names <- c(names(gis.layers),gis.layers.names)
  	# Iterate over files
  	for (i in 1:length(files.names)) {
  	# Stack with previously read layers
  	gis.layers <- stack(
  	gis.layers,
  	# Read as raster
  	raster(
  	# Construct file name
  	paste(REPO_HOME, "/data/GIS/5_deg/", files.names[i], sep = "")
  	)
  	)
  	}
  	# Apply all names
  	names(gis.layers) <- gis.layers.names

• klopt dit verhaal? Wat doen we hiermee?

  trait-geo-diverse-ungulates/doc/Niche_Overlap.Rmd

  Lines 257 to 263 in 2e8261f

  	Afterwards the occurrence dataset is split in k-fold partitions: a training
  	dataset containing 75% of the data and a test dataset containing 25% of the
  	data. The maxent model is constructed using the maxent function from the dismo
  	R package (Hijmans & Elith, 2013). The function extracts abiotic environmental
  	data for the training occurrence locations and 1000 random sampled background
  	locations, resulting in a model maxent object that can be used to predict which
  	other locations are suitable.

• dit moet anders. Als bepaalde soorten niet werken dan moeten ze structureel uit de lijst met taxa want op deze manier gaan we geheid ergens fouten maken met de volgorde

  trait-geo-diverse-ungulates/doc/Niche_Overlap.Rmd

  Line 295 in 2e8261f

  for (i in c(1:134, 136:150, 152:154)) {

• Vicagna vicugna klopt niet: de geslachtsnaam is incorrect en deze typefout heeft zich inmiddels door de rest van de bestanden en resultaten heen verspreid
• de syntax moet netter: we zouden middels styler (of anderszins) de code laten inspringen, dus niet zoals dit blok:

  trait-geo-diverse-ungulates/doc/Niche_Overlap.Rmd

  Lines 367 to 408 in 2e8261f

  	# combine two lists of valid and invalid models
  	output_AUC_valid <- data.frame(matrix(ncol = 3, nrow = length(list_species_model_high_accuracy)))
  	colnames(output_AUC_valid) <- c("taxon","trainingAUC","validation")
  	AUC.csv <- paste(REPO_HOME, "/results/maxent/AUCvalues.csv", sep="")
  	for (i in 1:length(list_species_model_high_accuracy)) {
  	open_species_model <- list_species_model_high_accuracy[[i]]
  	name <- names(list_species_model_high_accuracy[i])
  	name_underscore<- gsub( "_", " ", name)
  	trainingAUC<-open_species_model@results[[5,1]]
  	output_AUC_valid[i,] <- c(name_underscore, trainingAUC, "valid")
  	}
  	output_AUC_invalid <- data.frame(matrix(ncol = 3, nrow = length(list_species_model_low_accuracy)))
  	colnames(output_AUC_invalid) <- c("taxon","trainingAUC","validation")
  	for (i in 1:length(list_species_model_low_accuracy)) {
  	open_species_model <- list_species_model_low_accuracy[[i]]
  	name <- names(list_species_model_low_accuracy[i])
  	name_underscore<- gsub( "_", " ", name)
  	trainingAUC<-open_species_model@results[[5,1]]
  	output_AUC_invalid[i,] <- c(name_underscore, trainingAUC, "invalid")
  	}
  	combined_auc<- rbind(output_AUC_invalid, output_AUC_valid)
  	write.csv(combined_auc, file= AUC.csv)