The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Here are presented, in a full and complete example, all main functions (starting with BIOMOD_[...]) of biomod2.
library(biomod2)
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 <- rast(bioclim_current)
# Format Data with true absences
myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
expl.var = myExpl,
resp.xy = myRespXY,
resp.name = myRespName)
myBiomodData
plot(myBiomodData)
Single or multiple set of pseudo-absences can be selected with the BIOMOD_FormatingData function, which calls the bm_PseudoAbsences function to do so. More examples are presented on the Secondary functions webpage.
# # Transform true absences into potential pseudo-absences
# myResp.PA <- ifelse(myResp == 1, 1, NA)
#
# # Format Data with pseudo-absences : random method
# myBiomodData.r <- BIOMOD_FormatingData(resp.var = myResp.PA,
# expl.var = myExpl,
# resp.xy = myRespXY,
# resp.name = myRespName,
# PA.nb.rep = 4,
# PA.nb.absences = 1000,
# PA.strategy = 'random')
#
# myBiomodData.r
# plot(myBiomodData.r)
# # Select multiple sets of pseudo-absences
#
# # Transform true absences into potential pseudo-absences
# myResp.PA <- ifelse(myResp == 1, 1, NA)
#
# # Format Data with pseudo-absences : random method
# myBiomodData.multi <- BIOMOD_FormatingData(resp.var = myResp.PA,
# expl.var = myExpl,
# resp.xy = myRespXY,
# resp.name = myRespName,
# PA.nb.rep = 4,
# PA.nb.absences = c(1000, 500, 500, 200),
# PA.strategy = 'random')
# myBiomodData.multi
# summary(myBiomodData.multi)
# plot(myBiomodData.multi)
Several cross-validation methods are available and can be selected with the BIOMOD_Modeling function, which calls the bm_CrossValidation function to do so. More examples are presented on the Secondary functions webpage.
# # k-fold selection
# cv.k <- bm_CrossValidation(bm.format = myBiomodData,
# strategy = "kfold",
# nb.rep = 2,
# k = 3)
#
# # stratified selection (geographic)
# cv.s <- bm_CrossValidation(bm.format = myBiomodData,
# strategy = "strat",
# k = 2,
# balance = "presences",
# strat = "x")
# head(cv.k)
# head(cv.s)
Modeling options are automatically retrieved from selected models within the BIOMOD_Modeling function, which calls the bm_ModelingOptions function to do so. Model parameters can also be automatically tuned to a specific dataset, by calling the bm_Tuning function, however it can be quite long. More examples are presented on the Secondary functions webpage.
# # bigboss parameters
# opt.b <- bm_ModelingOptions(data.type = 'binary',
# models = c('SRE', 'XGBOOST'),
# strategy = 'bigboss')
#
# # tuned parameters with formated data
# opt.t <- bm_ModelingOptions(data.type = 'binary',
# models = c('SRE', 'XGBOOST'),
# strategy = 'tuned',
# bm.format = myBiomodData)
#
# opt.b
# opt.t
# Model single models
myBiomodModelOut <- BIOMOD_Modeling(bm.format = myBiomodData,
modeling.id = 'AllModels',
CV.strategy = 'random',
CV.nb.rep = 2,
CV.perc = 0.8,
OPT.strategy = 'bigboss',
var.import = 3,
metric.eval = c('TSS','ROC'))
# seed.val = 123)
# nb.cpu = 8)
myBiomodModelOut
# Get evaluation scores & variables importance
get_evaluations(myBiomodModelOut)
get_variables_importance(myBiomodModelOut)
# Represent evaluation scores & variables importance
bm_PlotEvalMean(bm.out = myBiomodModelOut)
bm_PlotEvalBoxplot(bm.out = myBiomodModelOut, group.by = c('algo', 'algo'))
bm_PlotEvalBoxplot(bm.out = myBiomodModelOut, group.by = c('algo', 'run'))
bm_PlotVarImpBoxplot(bm.out = myBiomodModelOut, group.by = c('expl.var', 'algo', 'algo'))
bm_PlotVarImpBoxplot(bm.out = myBiomodModelOut, group.by = c('expl.var', 'algo', 'run'))
bm_PlotVarImpBoxplot(bm.out = myBiomodModelOut, group.by = c('algo', 'expl.var', 'run'))
# Represent response curves
bm_PlotResponseCurves(bm.out = myBiomodModelOut,
models.chosen = get_built_models(myBiomodModelOut)[c(1:3, 12:14)],
fixed.var = 'median')
bm_PlotResponseCurves(bm.out = myBiomodModelOut,
models.chosen = get_built_models(myBiomodModelOut)[c(1:3, 12:14)],
fixed.var = 'min')
bm_PlotResponseCurves(bm.out = myBiomodModelOut,
models.chosen = get_built_models(myBiomodModelOut)[3],
fixed.var = 'median',
do.bivariate = TRUE)
# Model ensemble models
myBiomodEM <- BIOMOD_EnsembleModeling(bm.mod = myBiomodModelOut,
models.chosen = 'all',
em.by = 'all',
em.algo = c('EMmean', 'EMcv', 'EMci', 'EMmedian', 'EMca', 'EMwmean'),
metric.select = c('TSS'),
metric.select.thresh = c(0.7),
metric.eval = c('TSS', 'ROC'),
var.import = 3,
EMci.alpha = 0.05,
EMwmean.decay = 'proportional')
myBiomodEM
# Get evaluation scores & variables importance
get_evaluations(myBiomodEM)
get_variables_importance(myBiomodEM)
# Represent evaluation scores & variables importance
bm_PlotEvalMean(bm.out = myBiomodEM, group.by = 'full.name')
bm_PlotEvalBoxplot(bm.out = myBiomodEM, group.by = c('full.name', 'full.name'))
bm_PlotVarImpBoxplot(bm.out = myBiomodEM, group.by = c('expl.var', 'full.name', 'full.name'))
bm_PlotVarImpBoxplot(bm.out = myBiomodEM, group.by = c('expl.var', 'algo', 'merged.by.run'))
bm_PlotVarImpBoxplot(bm.out = myBiomodEM, group.by = c('algo', 'expl.var', 'merged.by.run'))
# Represent response curves
bm_PlotResponseCurves(bm.out = myBiomodEM,
models.chosen = get_built_models(myBiomodEM)[c(1, 6, 7)],
fixed.var = 'median')
bm_PlotResponseCurves(bm.out = myBiomodEM,
models.chosen = get_built_models(myBiomodEM)[c(1, 6, 7)],
fixed.var = 'min')
bm_PlotResponseCurves(bm.out = myBiomodEM,
models.chosen = get_built_models(myBiomodEM)[7],
fixed.var = 'median',
do.bivariate = TRUE)
# Project single models
myBiomodProj <- BIOMOD_Projection(bm.mod = myBiomodModelOut,
proj.name = 'Current',
new.env = myExpl,
models.chosen = 'all',
metric.binary = 'all',
metric.filter = 'all',
build.clamping.mask = TRUE)
myBiomodProj
plot(myBiomodProj)
# Project ensemble models (from single projections)
myBiomodEMProj <- BIOMOD_EnsembleForecasting(bm.em = myBiomodEM,
bm.proj = myBiomodProj,
models.chosen = 'all',
metric.binary = 'all',
metric.filter = 'all')
# Project ensemble models (building single projections)
myBiomodEMProj <- BIOMOD_EnsembleForecasting(bm.em = myBiomodEM,
proj.name = 'CurrentEM',
new.env = myExpl,
models.chosen = 'all',
metric.binary = 'all',
metric.filter = 'all')
myBiomodEMProj
plot(myBiomodEMProj)
# Load environmental variables extracted from BIOCLIM (bio_3, bio_4, bio_7, bio_11 & bio_12)
data("bioclim_future")
myExplFuture = rast(bioclim_future)
# Project onto future conditions
myBiomodProjectionFuture <- BIOMOD_Projection(bm.mod = myBiomodModelOut,
proj.name = 'Future',
new.env = myExplFuture,
models.chosen = 'all',
metric.binary = 'TSS',
build.clamping.mask = TRUE)
# Load current and future binary projections
CurrentProj <- get_predictions(myBiomodProj, metric.binary = "TSS")
FutureProj <- get_predictions(myBiomodProjectionFuture, metric.binary = "TSS")
# Compute differences
myBiomodRangeSize <- BIOMOD_RangeSize(proj.current = CurrentProj,
proj.future = FutureProj)
myBiomodRangeSize$Compt.By.Models
plot(myBiomodRangeSize$Diff.By.Pixel)
# Represent main results
gg = bm_PlotRangeSize(bm.range = myBiomodRangeSize,
do.count = TRUE,
do.perc = TRUE,
do.maps = TRUE,
do.mean = TRUE,
do.plot = TRUE,
row.names = c("Species", "Dataset", "Run", "Algo"))
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.