Merge two SWD objects.
mergeSWD(swd1, swd2, only_presence = FALSE)
swd1 | SWD object. |
---|---|
swd2 | SWD object. |
only_presence | logical, if |
The merged SWD object.
In case the two SWD objects have different columns, only the common columns are used in the merged object.
The SWD object is created in a way that the presence locations are always before than the absence/background locations.
Sergio Vignali
# Acquire environmental variables files <- list.files(path = file.path(system.file(package = "dismo"), "ex"), pattern = "grd", full.names = TRUE) predictors <- raster::stack(files) # Prepare presence and background locations p_coords <- virtualSp$presence bg_coords <- virtualSp$background # Create SWD object data <- prepareSWD(species = "Virtual species", p = p_coords, a = bg_coords, env = predictors, categorical = "biome")#>#># Split only presence locations in training (80%) and testing (20%) datasets datasets <- trainValTest(data, test = 0.2, only_presence = TRUE) train <- datasets[[1]] test <- datasets[[2]] # Merge the training and the testing datasets together merged <- mergeSWD(train, test, only_presence = TRUE) # Split presence and absence locations in training (80%) and testing (20%) datasets#> [[1]] #> Object of class SWD #> #> Species: Virtual species #> Presence locations: 320 #> Absence locations: 5000 #> #> Variables: #> --------- #> Continuous: bio1 bio12 bio16 bio17 bio5 bio6 bio7 bio8 #> Categorical: biome #> #> [[2]] #> Object of class SWD #> #> Species: Virtual species #> Presence locations: 80 #> Absence locations: 5000 #> #> Variables: #> --------- #> Continuous: bio1 bio12 bio16 bio17 bio5 bio6 bio7 bio8 #> Categorical: biome #>datasets <- trainValTest(data, test = 0.2) train <- datasets[[1]] test <- datasets[[2]] # Merge the training and the testing datasets together merged <- mergeSWD(train, test)