biomod2 (version 4.2-5)

bm_ModelingOptions: Configure the modeling options for each selected model

Description

Parameterize and/or tune biomod2's single models options.

Usage

bm_ModelingOptions(
  data.type,
  models = c("ANN", "CTA", "FDA", "GAM", "GBM", "GLM", "MARS", "MAXENT", "MAXNET", "RF",
    "SRE", "XGBOOST"),
  strategy,
  user.val = NULL,
  user.base = "bigboss",
  bm.format = NULL,
  calib.lines = NULL
)

Value

A BIOMOD.models.options of object that can be used to build species distribution model(s) with the BIOMOD_Modeling function.

Arguments

data.type

a character corresponding to the data type to be used, must be either binary, binary.PA, abundance, compositional

models

a vector containing model names to be computed, must be among ANN, CTA, FDA, GAM, GBM, GLM, MARS, MAXENT, MAXNET, RF, SRE, XGBOOST

strategy

a character corresponding to the method to select models' parameters values, must be either default, bigboss, user.defined, tuned

user.val

(optional, default NULL)
A list containing parameters values for some (all) models

user.base

(optional, default bigboss)
A character, default or bigboss used when strategy = 'user.defined'. It sets the bases of parameters to be modified by user defined values.

bm.format

(optional, default NULL)
A BIOMOD.formated.data or BIOMOD.formated.data.PA object returned by the BIOMOD_FormatingData function

calib.lines

(optional, default NULL)
A data.frame object returned by get_calib_lines or bm_CrossValidation functions

Author

Damien Georges, Wilfried Thuiller, Maya Gueguen

Details

This function creates a BIOMOD.models.options object containing parameter values for each single model that can be run within biomod2 through BIOMOD_Modeling function.

12 models are currently available, and are listed within the ModelsTable dataset.

Different strategies are available to set those parameters, through the strategy argument :

default

all parameters names and values are directly retrieve from functions to be called through formalArgs and formals functions respectively

bigboss

default parameter values are updated with values predefined by biomod2 team

user.defined

default parameter values are updated with values provided by the user

tuned

default parameter values are updated by calling bm_Tuning function

See Also

ModelsTable, BIOMOD.models.options, bm_Tuning, BIOMOD_Modeling

Other Secundary functions: bm_BinaryTransformation(), bm_CrossValidation(), bm_FindOptimStat(), bm_MakeFormula(), bm_PlotEvalBoxplot(), bm_PlotEvalMean(), bm_PlotRangeSize(), bm_PlotResponseCurves(), bm_PlotVarImpBoxplot(), bm_PseudoAbsences(), bm_RunModelsLoop(), bm_SRE(), bm_SampleBinaryVector(), bm_SampleFactorLevels(), bm_Tuning(), bm_VariablesImportance()

Examples

library(terra)

# Load species occurrences (6 species available)
data(DataSpecies)
head(DataSpecies)

# Select the name of the studied species
myRespName <- 'GuloGulo'

# Get corresponding presence/absence data
myResp <- as.numeric(DataSpecies[, myRespName])

# Get corresponding XY coordinates
myRespXY <- DataSpecies[, c('X_WGS84', 'Y_WGS84')]

# Load environmental variables extracted from BIOCLIM (bio_3, bio_4, bio_7, bio_11 & bio_12)
data(bioclim_current)
myExpl <- terra::rast(bioclim_current)

# \dontshow{
myExtent <- terra::ext(0,30,45,70)
myExpl <- terra::crop(myExpl, myExtent)
# }

# ---------------------------------------------------------------#
# Format Data with true absences
myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
                                     expl.var = myExpl,
                                     resp.xy = myRespXY,
                                     resp.name = myRespName)

# k-fold selection
cv.k <- bm_CrossValidation(bm.format = myBiomodData,
                           strategy = 'kfold',
                           nb.rep = 2,
                           k = 3)


# ---------------------------------------------------------------#
allModels <- c('ANN', 'CTA', 'FDA', 'GAM', 'GBM', 'GLM'
               , 'MARS', 'MAXENT', 'MAXNET', 'RF', 'SRE', 'XGBOOST')

# default parameters
opt.d <- bm_ModelingOptions(data.type = 'binary',
                            models = allModels,
                            strategy = 'default')

# providing formated data
opt.df <- bm_ModelingOptions(data.type = 'binary',
                             models = allModels,
                             strategy = 'default',
                             bm.format = myBiomodData,
                             calib.lines = cv.k)

opt.d
opt.d@models
opt.d@options$ANN.binary.nnet.nnet
names(opt.d@options$[e-mail])

opt.df@options$ANN.binary.nnet.nnet
names(opt.df@options$[e-mail])


# ---------------------------------------------------------------#
# bigboss parameters
opt.b <- bm_ModelingOptions(data.type = 'binary',
                            models = allModels,
                            strategy = 'bigboss')

# user defined parameters
user.SRE <- list('_allData_allRun' = list(quant = 0.01))
user.XGBOOST <- list('_allData_allRun' = list(nrounds = 10))
user.val <- list(SRE.binary.biomod2.bm_SRE = user.SRE
                 , XGBOOST.binary.xgboost.xgboost = user.XGBOOST)

opt.u <- bm_ModelingOptions(data.type = 'binary',
                            models = c('SRE', 'XGBOOST'),
                            strategy = 'user.defined',
                            user.val = user.val)

opt.b
opt.u

if (FALSE) {
# tuned parameters with formated data
opt.t <- bm_ModelingOptions(data.type = 'binary',
                            models = c('SRE', 'XGBOOST'),
                            strategy = 'tuned',
                            bm.format = myBiomodData)
opt.t
}