biomod2 (version 4.1-2)

bm_PlotEvalMean: 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,
  group.by = "algo",
  do.plot = TRUE,
  ...
)

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.

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 ROC, TSS, KAPPA, ACCURACY, BIAS, POD, FAR, POFD, SR, CSI, ETS, HK, HSS, OR, ORSS

group.by

a character corresponding to the way kept models will be combined to compute mean and sd evaluation scores, must be among model, algo, run, dataset

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 :

• 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

See Also

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

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

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

Examples

# Load species occurrences (6 species available)
myFile <- system.file('external/species/mammals_table.csv', package = 'biomod2')
DataSpecies <- read.csv(myFile, row.names = 1)
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)
myFiles <- paste0('external/bioclim/current/bio', c(3, 4, 7, 11, 12), '.grd')
myExpl <- raster::stack(system.file(myFiles, package = 'biomod2'))

# \dontshow{
myExtent <- raster::extent(0,30,45,70)
myExpl <- raster::stack(raster::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)

  # 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,
                                      nb.rep = 2,
                                      data.split.perc = 80,
                                      metric.eval = c('TSS','ROC'),
                                      var.import = 3,
                                      do.full.models = FALSE,
                                      seed.val = 42)
}


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

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