biomod2 (version 4.2-5)

bm_PlotVarImpBoxplot: 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,
  ...
)

Value

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

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)

Author

Damien Georges, Maya Gueguen

Details

... can take the following values :

• main : a character corresponding to the graphic title

See Also

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

Other Secundary functions: bm_BinaryTransformation(), bm_CrossValidation(), bm_FindOptimStat(), bm_MakeFormula(), bm_ModelingOptions(), bm_PlotEvalBoxplot(), bm_PlotEvalMean(), bm_PlotRangeSize(), bm_PlotResponseCurves(), bm_PseudoAbsences(), 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)

# \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)
}


# ---------------------------------------------------------------
# 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'))