BIOMOD_EnsembleModeling() output object class

Class returned by BIOMOD_EnsembleModeling, and used by BIOMOD_LoadModels, BIOMOD_PresenceOnly and BIOMOD_EnsembleForecasting

# S4 method for BIOMOD.ensemble.models.out
show(object)

Arguments

object

a BIOMOD.ensemble.models.out object

Slots

modeling.id

a character corresponding to the name (ID) of the simulation set

dir.name

a character corresponding to the modeling folder

sp.name

a character corresponding to the species name

expl.var.names

a vector containing names of explanatory variables

models.out

a BIOMOD.stored.models.out-class object containing informations from BIOMOD_Modeling object

em.by

a character corresponding to the way kept models have been combined to build the ensemble models, must be among PA+run, PA+algo, PA, algo, all

em.computed

a vector containing names of ensemble models

em.failed

a vector containing names of failed ensemble models

em.models_kept

a list containing single models for each ensemble model

models.evaluation

a BIOMOD.stored.data.frame-class object containing models evaluation

variables.importance

a BIOMOD.stored.data.frame-class object containing variables importance

models.prediction

a BIOMOD.stored.data.frame-class object containing models predictions

models.prediction.eval

a BIOMOD.stored.data.frame-class object containing models predictions for evaluation data

link

a character containing the file name of the saved object

See also

BIOMOD_EnsembleModeling, BIOMOD_LoadModels, BIOMOD_PresenceOnly, bm_VariablesImportance, bm_PlotEvalMean, bm_PlotEvalBoxplot, bm_PlotVarImpBoxplot, bm_PlotResponseCurves

Other Toolbox objects: BIOMOD.formated.data, BIOMOD.formated.data.PA, BIOMOD.models.options, BIOMOD.models.out, BIOMOD.options.dataset, BIOMOD.options.default, BIOMOD.projection.out, BIOMOD.stored.data, biomod2_ensemble_model, biomod2_model

Author

Damien Georges

Examples

showClass("BIOMOD.ensemble.models.out")

## ----------------------------------------------------------------------- #
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)
# }

## ----------------------------------------------------------------------- #
file.out <- paste0(myRespName, "/", myRespName, ".AllModels.models.out")
if (file.exists(file.out)) {
  myBiomodModelOut <- get(load(file.out))
} else {

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

  # Model single models
  myBiomodModelOut <- BIOMOD_Modeling(bm.format = myBiomodData,
                                      modeling.id = 'AllModels',
                                      models = c('RF', 'GLM'),
                                      CV.strategy = 'random',
                                      CV.nb.rep = 2,
                                      CV.perc = 0.8,
                                      OPT.strategy = 'bigboss',
                                      metric.eval = c('TSS','ROC'),
                                      var.import = 3,
                                      seed.val = 42)
}


## ----------------------------------------------------------------------- #
# Model ensemble models
myBiomodEM <- BIOMOD_EnsembleModeling(bm.mod = myBiomodModelOut,
                                      models.chosen = 'all',
                                      em.by = 'all',
                                      em.algo = c('EMmean', 'EMca'),
                                      metric.select = c('TSS'),
                                      metric.select.thresh = c(0.7),
                                      metric.eval = c('TSS', 'ROC'),
                                      var.import = 3,
                                      seed.val = 42)
myBiomodEM