Predict
Predict the output for a new dataset given a trained SDMmodel model.
## S4 method for signature 'SDMmodel' predict( object, data, type = NULL, clamp = TRUE, filename = "", format = "GTiff", extent = NULL, progress = "", ... )
object |
SDMmodel object. |
data |
|
type |
character. Output type, see details, used only for Maxent and
Maxnet methods, default is |
clamp |
logical for clumping during prediction, used only for Maxent
and Maxnet methods, default is |
filename |
character. Output file name for the prediction map, used only
when |
format |
character. The output format, see writeRaster for all the options, default is "GTiff". |
extent |
extent object, if provided it restricts
the prediction to the given extent, default is |
progress |
character to display a progress bar: "text", "window" or "" (default) for no progress bar. |
... |
Additional arguments to pass to the writeRaster function. |
filename, format, extent, progress, and ... are arguments used only when the prediction is done for a stack object.
For models trained with the Maxent method the argument type can be:
"raw", "logistic" and "cloglog". The function performs the prediction in
R without calling the MaxEnt Java software. This results in a faster
computation for large datasets and might result in a slightly different
output compared to the Java software.
For models trained with the Maxnet method the argument type can be:
"link", "exponential", "logistic" and "cloglog", see maxnet
for more details.
For models trained with the ANN method the function uses the "raw" output type.
For models trained with the RF method the output is the probability of class 1.
For models trained with the BRT method the function uses the number of trees defined to train the model and the "response" output type.
Sergio Vignali
Wilson P.D., (2009). Guidelines for computing MaxEnt model output values from a lambdas file.
# 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 presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data, test = 0.2, only_presence = TRUE)
train <- datasets[[1]]
test <- datasets[[2]]
# Train a model
model <- train(method = "Maxnet", data = train, fc = "l")
# Make cloglog prediction for the test dataset
predict(model, data = test, type = "cloglog")
# Make logistic prediction for the all study area
predict(model, data = predictors, type = "logistic")
## Not run:
# Make logistic prediction for the all study area and save it in a file
# The function saves the file in your working directory
predict(model, data = predictors, type = "logistic", filename = "my_map")
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.