Merge two SWD objects.

mergeSWD(swd1, swd2, only_presence = FALSE)

Arguments

swd1

SWD object.

swd2

SWD object.

only_presence

logical, if TRUE only for the presence locations are merged and the absence/background locations are taken only from the swd1 object, default is FALSE.

Value

The merged SWD object.

Details

  • 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.

Author

Sergio Vignali

Examples

# 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")
#> Extracting predictor information for presence locations...
#> Extracting predictor information for absence/background locations...
# 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)