Model Report
Make a report that shows the main results.
modelReport( model, folder, test = NULL, type = NULL, response_curves = FALSE, only_presence = FALSE, jk = FALSE, env = NULL, clamp = TRUE, permut = 10, factors = NULL )
model |
SDMmodel object. |
folder |
character. The name of the folder in which to save the output. The folder is created in the working directory. |
test |
SWD object with the test locations, default is
|
type |
character. The output type used for "Maxent" and "Maxnet"
methods, possible values are "cloglog" and "logistic", default is
|
response_curves |
logical, if |
only_presence |
logical, if |
jk |
logical, if |
env |
stack. If provided it computes and adds a
prediction map to the output, default is |
clamp |
logical for clumping during prediction, used for response curves
and for the prediction map, default is |
permut |
integer. Number of permutations, default is 10. |
factors |
list with levels for factor variables, see predict |
The function produces a report similar to the one created by MaxEnt software.
Sergio Vignali
# If you run the following examples with the function example(), you may want
# to set the argument ask like following: example("modelReport", ask = FALSE)
# 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 = "lq")
# Create the report
## Not run:
modelReport(model, type = "cloglog", folder = "my_folder", test = test,
response_curves = TRUE, only_presence = TRUE, jk = TRUE,
env = predictors, permut = 2)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.