| EX | Predictions |
Predictions
Now we create a spatial prediction with our model and do a visual inspection of the map we created. Load the pre-trained model using the readRDS() function.
Then load all the predictor variables from the raster file “bioclim.tif” using the terra::rast() function. This raster object has to contain all environmental variables used during model training.
To create the spatial prediction, the terra::predict() function is utilized. It takes the raster object and the pre-trained model as inputs, and generates predictions for each cell in the raster. The na.rm=T argument specifies that missing values should be ignored during the prediction process.
You can visualize the prediction using the terra::plot() function, this allows a visual assessment of the predicted spatial distribution of the modeled species. If you save your predicted raster you can also visualize it in a GIS, for example in the open-source software QGIS.
library(caret)
library(terra)
library(sf)
library(Metrics)
library(ecospat)
library(raster)
# set your working directory
setwd("D:/sdmWorkflow_Kurs/")
# load your model
mod=readRDS("GamBoostModel.RDS")
# now we will create a spatial prediction of your model
# first load all your predictor variables
r=terra::rast("bioclim.tif")
# use the terra::predict function to create a spatial predcition:
pred=terra::predict(object = r,model= mod, na.rm=T)
# have a look at your predction
terra::plot(pred)
terra::writeRaster(pred, "prediction_aglais_caschmirensis.tif")