BIOMOD.ensemble.models.out | R Documentation |
BIOMOD_EnsembleModeling()
output object classClass returned by BIOMOD_EnsembleModeling
, and used by
BIOMOD_LoadModels
, BIOMOD_PresenceOnly
and
BIOMOD_EnsembleForecasting
## S4 method for signature 'BIOMOD.ensemble.models.out'
show(object)
object |
a |
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
Damien Georges
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
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)
## ----------------------------------------------------------------------- #
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
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