bm_PlotEvalBoxplot: Plot boxplot of evaluation scores

In biomod2: Ensemble Platform for Species Distribution Modeling

Plot boxplot of evaluation scores

Description

This function represents boxplot of evaluation scores 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 2 grouping methods (see Details).

Usage

bm_PlotEvalBoxplot(
  bm.out,
  dataset = "calibration",
  group.by = c("algo", "run"),
  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
dataset	a character corresponding to the dataset upon which evaluation metrics have been calculated and that is to be represented, must be among calibration, validation, evaluation
group.by	a 2-length vector containing the way kept models will be represented, must be among full.name, PA, run, algo (if bm.out is a BIOMOD.models.out object), or full.name, merged.by.PA, merged.by.run, merged.by.algo (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

• scales : a character corresponding to the scales argument of the facet_wrap function, must be either fixed, free_x, free_y or free

Value

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

Author(s)

Damien Georges, Maya Gueguen

See Also

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

Other Secundary functions: bm_BinaryTransformation(), bm_CVnnet(), bm_CrossValidation(), bm_FindOptimStat(), bm_MakeFormula(), bm_PlotEvalMean(), bm_PlotRangeSize(), bm_PlotResponseCurves(), bm_PlotVarImpBoxplot(), bm_PseudoAbsences(), bm_RunModelsLoop(), bm_SRE(), bm_SampleBinaryVector(), bm_SampleFactorLevels(), bm_VariablesImportance()

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

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)
}


# ---------------------------------------------------------------
# Get evaluation scores
get_evaluations(myBiomodModelOut)

# Represent evaluation scores
bm_PlotEvalBoxplot(bm.out = myBiomodModelOut, group.by = c('algo', 'run'))

biomod2 documentation built on July 9, 2023, 6:05 p.m.