bm_PlotEvalMean: Plot mean evaluation scores

In biomod2: Ensemble Platform for Species Distribution Modeling

Plot mean evaluation scores

Description

This function represents mean evaluation scores (and their standard deviation) 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 different evaluation methods, and models can be grouped (see Details).

Usage

bm_PlotEvalMean(
  bm.out,
  metric.eval = NULL,
  dataset = "calibration",
  group.by = "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
metric.eval	a vector containing evaluation metric names to be used, must be among POD, FAR, POFD, SR, ACCURACY, BIAS, ROC, TSS, KAPPA, OR, ORSS, CSI, ETS, BOYCE, MPA
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 character corresponding to the way kept models will be combined to compute mean and sd evaluation scores, 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 :

• xlim : an integer corresponding to the x maximum limit to represent

• ylim : an integer corresponding to the y maximum limit to represent

• main : a character corresponding to the graphic title

• col : a vector containing new color values

Value

A list containing a data.frame with mean and standard deviation of evaluation scores and the corresponding ggplot object representing them according to 2 different evaluation methods.

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_CrossValidation(), bm_FindOptimStat(), bm_MakeFormula(), bm_ModelingOptions(), bm_PlotEvalBoxplot(), bm_PlotRangeSize(), bm_PlotResponseCurves(), bm_PlotVarImpBoxplot(), bm_PseudoAbsences(), bm_RunModelsLoop(), bm_SRE(), bm_SampleBinaryVector(), bm_SampleFactorLevels(), bm_Tuning(), bm_VariablesImportance()

Other Plot functions: bm_PlotEvalBoxplot(), 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)

  # 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 evaluation scores
get_evaluations(myBiomodModelOut)

# Represent mean evaluation scores
bm_PlotEvalMean(bm.out = myBiomodModelOut)

biomod2 documentation built on June 22, 2024, 10:56 a.m.