bm_PlotVarImpBoxplot: Plot boxplot of variables importance

In biomodhub/biomod2: Ensemble Platform for Species Distribution Modeling

Plot boxplot of variables importance

Description

This function represents boxplot of variables importance of species distribution models, from BIOMOD.models.out or BIOMOD.ensemble.models.out objects that can be obtained from BIOMOD_Modeling or BIOMOD_EnsembleModeling functions. Scores are represented according to 3 grouping methods (see Details).

Usage

bm_PlotVarImpBoxplot(
  bm.out,
  group.by = c("run", "expl.var", "algo"),
  do.plot = TRUE,
  ...
)

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
group.by	a 3-length vector containing the way kept models will be represented, must be among full.name, PA, run, algo, expl.var (if bm.out is a BIOMOD.models.out object), or full.name, merged.by.PA, merged.by.run, merged.by.algo, expl.var (if bm.out is a BIOMOD.ensemble.models.out object)
do.plot	(optional, default TRUE) A logical value defining whether the plot is to be rendered or not
...	some additional arguments (see Details)

Details

... can take the following values :

• main : a character corresponding to the graphic title

Value

A list containing a data.frame with variables importance and the corresponding ggplot object representing them in boxplot.

Author(s)

Damien Georges, Maya Guéguen

See Also

BIOMOD.models.out, BIOMOD.ensemble.models.out, BIOMOD_Modeling, BIOMOD_EnsembleModeling, get_variables_importance

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

Other Plot functions: bm_PlotEvalBoxplot(), bm_PlotEvalMean(), bm_PlotRangeSize(), bm_PlotResponseCurves()

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.name = myRespName,
                                       resp.var = myResp,
                                       resp.xy = myRespXY,
                                       expl.var = myExpl)

  # 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)
}


# ---------------------------------------------------------------
# Get variables importance
get_variables_importance(myBiomodModelOut)

# Represent variables importance
bm_PlotVarImpBoxplot(bm.out = myBiomodModelOut, group.by = c('expl.var', 'algo', 'algo'))
bm_PlotVarImpBoxplot(bm.out = myBiomodModelOut, group.by = c('expl.var', 'algo', 'PA'))
bm_PlotVarImpBoxplot(bm.out = myBiomodModelOut, group.by = c('algo', 'expl.var', 'PA'))

biomodhub/biomod2 documentation built on July 4, 2025, 6:15 p.m.