SDMtune (version 0.1.0)

train: Train

Description

Train a model using the given method.

Usage

train(method = c("Maxent", "Maxnet"), p, a, rep = 1, verbose = TRUE,
  folds = NULL, seed = NULL, ...)

Arguments

method

character. Possible values are "Maxent" or "Maxnet".

p

'>SWD object with the presence locations.

a

'>SWD object with the absence or background locations.

rep

numeric. Number of replicates, used for cross validation. Default is 1, meaning no cross validation is performed.

verbose

logical if TRUE shows a progress bar if replicates are greater then 1, default is TRUE.

folds

numeric. Vector containing the indexes for the k-fold partition of the training data, if not provided the function randomly creates the folds, default is NULL.

seed

integer. The value used to set the seed for the fold partition, used if folds is not provided, default is NULL.

...

Arguments passed to the relative method, see details.

Value

An '>SDMmodel or '>SDMmodelCV object.

Details

• For the Maxent method, possible arguments are:

  • reg: numeric. The value of the regularization multiplier, default is 1.

  • fc: vector. The value of the feature classes, possible values are combinations of "l", "q", "p", "h" and "t", default is "lqph".

  • iter: numeric. Number of iterations used by the MaxEnt algorithm, default is 500.

  • extra_args: vector. Extra arguments used to run MaxEnt, default is "removeduplicates=false" and "addsamplestobackground=false". In case this is not your expected behavior you can assign extra_args = "" or you can change or add any other additional arguments extending the default settings (e.g. extra_args = c("removeduplicates=true, addsamplestobackground=true)"**)

• For the Maxnet method, possible arguments are:

  • reg: numeric. The value of the regularization intensity, default is 1.

  • fc: vector. The value of the feature classes, possible values are combinations of "l", "q", "p", "h" and "t", default is "lqph". For more details see maxnet.

Examples

# NOT RUN {
# 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 locations
p_coords <- condor[, 1:2]

# Prepare background locations
bg_coords <- dismo::randomPoints(predictors, 5000)

# Create SWD object
presence <- prepareSWD(species = "Vultur gryphus", coords = p_coords,
                       env = predictors, categorical = "biome")
bg <- prepareSWD(species = "Vultur gryphus", coords = bg_coords,
                 env = predictors, categorical = "biome")

# Split presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(presence, test = 0.2)
train <- datasets[[1]]
test <- datasets[[2]]

# Train a Maxent model
model <- train(method = "Maxent", p = train, a = bg, fc = "l", reg = 1.5,
               iter = 700)

# Train a Maxnet model
model <- train(method = "Maxnet", p = train, a = bg, fc = "lq", reg = 1.5)

# Train a Maxnet model with cross validation
model <- train(method = "Maxnet", p = train, a = bg, fc = "l", reg = 0.8,
               rep = 4)
# }