Predict for Cross Validation
Predict the output for a new dataset given a trained SDMmodelCV model. The output is given as the provided function applied to the prediction of the k models.
## S4 method for signature 'SDMmodelCV' predict( object, data, fun = "mean", type = NULL, clamp = TRUE, filename = "", format = "GTiff", extent = NULL, ... )
object |
SDMmodelCV object. |
data |
data.frame, SWD or raster stack with the data for the prediction. |
fun |
character. function used to combine the output of the k models,
default is |
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 |
... |
Additional arguments to pass to the writeRaster function. |
filename, format, extent, and ... arguments are used only when the prediction is done for a stack object.
When a character vector is passed to the fun argument, than all the
given functions are applied and a named list is returned, see examples.
When filename is provided and the fun argument contains more than one
function name, the saved files are named as filename_fun, see example.
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")
# Create 4 random folds splitting only the presence data
folds <- randomFolds(data, k = 4, only_presence = TRUE)
model <- train(method = "Maxnet", data = data, fc = "l", folds = folds)
# Make cloglog prediction for the all study area and get the result as
# average of the k models
predict(model, data = predictors, fun = "mean", type = "cloglog")
# Make cloglog prediction for the all study area, get the average, standard
# deviation, and maximum values of the k models, and save the output in three
# files
## Not run:
# The following commands save the output in the working directory
maps <- predict(model, data = predictors, fun = c("mean", "sd", "max"),
type = "cloglog", filename = "prediction")
# In this case three files are created: prediction_mean.tif,
# prediction_sd.tif and prediction_max.tif
plotPred(maps$mean)
plotPred(maps$sd)
plotPred(maps$max)
# Make logistic prediction for the all study area, given as standard
# deviation of the k models, and save it in a file
predict(model, data = predictors, fun = "sd", type = "logistic",
filename = "my_map")
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.