BIOMOD_LoadModels {biomod2}

Load species distribution models built with biomod2

Description

This function loads individual models built with BIOMOD_Modeling or BIOMOD_EnsembleModeling functions.

Usage

BIOMOD_LoadModels(
  bm.out,
  full.name = NULL,
  PA = NULL,
  run = NULL,
  algo = NULL,
  merged.by.PA = NULL,
  merged.by.run = NULL,
  merged.by.algo = NULL,
  filtered.by = NULL
)

Arguments

bm.out

a BIOMOD.models.out or BIOMOD.ensemble.models.out object that can be obtained with the BIOMOD_Modeling or BIOMOD_EnsembleModeling functions

full.name

(optional, default NULL)
A vector containing model names to be kept, must be either all or a sub-selection of model names that can be obtained with the get_built_models function

PA

(optional, default NULL)
A vector containing pseudo-absence set to be loaded, must be among PA1, PA2, ..., allData

run

(optional, default NULL)
A vector containing repetition set to be loaded, must be among RUN1, RUN2, ..., allRun

algo

(optional, default NULL)
A character containing algorithm to be loaded, must be either GLM, GBM, GAM, CTA, ANN, SRE, FDA, MARS, RF, MAXENT, MAXNET, XGBOOST

merged.by.PA

(optional, default NULL)
A vector containing merged pseudo-absence set to be loaded, must be among PA1, PA2, ..., mergedData

merged.by.run

(optional, default NULL)
A vector containing merged repetition set to be loaded, must be among RUN1, RUN2, ..., mergedRun

merged.by.algo

(optional, default NULL)
A character containing merged algorithm to be loaded, must be among GLM, GBM, GAM, CTA, ANN, SRE, FDA, MARS, RF, MAXENT, MAXNET, XGBOOST, mergedAlgo

filtered.by

(optional, default NULL)
A vector containing evaluation metric selected to filter single models to build the ensemble models, must be among ROC, TSS, KAPPA, ACCURACY, BIAS, POD, FAR, POFD, SR, CSI, ETS, HK, HSS, OR, ORSS

Details

This function might be of particular use to load models and make response plot analyses.

Running the function providing only bm.out argument will load all models built by the BIOMOD_Modeling or BIOMOD_EnsembleModeling function, but a subselection of models can be done using the additional arguments (full.name, PA, run, algo, merged.by.PA, merged.by.run, merged.by.algo, filtered.by).

Value

A vector containing the names of the loaded models.

Author(s)

Damien Georges

See Also

BIOMOD_Modeling, BIOMOD_EnsembleModeling

Other Main functions: BIOMOD_EnsembleForecasting(), BIOMOD_EnsembleModeling(), BIOMOD_FormatingData(), BIOMOD_ModelingOptions(), BIOMOD_Modeling(), BIOMOD_PresenceOnly(), BIOMOD_Projection(), BIOMOD_RangeSize(), BIOMOD_Tuning()

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)



# ---------------------------------------------------------------
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)

  # Create default modeling options
  myBiomodOptions <- BIOMOD_ModelingOptions()

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


# ---------------------------------------------------------------
# Loading some models built
BIOMOD_LoadModels(bm.out = myBiomodModelOut, algo = 'RF')