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)
# # 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')
#
# # Format Data with pseudo-absences : disk method
# myBiomodData.d <- BIOMOD_FormatingData(resp.var = myResp.PA,
# expl.var = myExpl,
# resp.xy = myRespXY,
# resp.name = myRespName,
# PA.nb.rep = 4,
# PA.nb.absences = 500,
# PA.strategy = 'disk',
# PA.dist.min = 5,
# PA.dist.max = 35)
#
# # Format Data with pseudo-absences : SRE method
# myBiomodData.s <- 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 = 'sre',
# PA.sre.quant = 0.025)
#
# # Format Data with pseudo-absences : user.defined method
# myPAtable <- data.frame(PA1 = ifelse(myResp == 1, TRUE, FALSE),
# PA2 = ifelse(myResp == 1, TRUE, FALSE))
# for (i in 1:ncol(myPAtable)) myPAtable[sample(which(myPAtable[, i] == FALSE), 500), i] = TRUE
# myBiomodData.u <- BIOMOD_FormatingData(resp.var = myResp.PA,
# expl.var = myExpl,
# resp.xy = myRespXY,
# resp.name = myRespName,
# PA.strategy = 'user.defined',
# PA.user.table = myPAtable)
#
# myBiomodData.r
# myBiomodData.d
# myBiomodData.s
# myBiomodData.u
# plot(myBiomodData.r)
# plot(myBiomodData.d)
# plot(myBiomodData.s)
# plot(myBiomodData.u)
# Print default modeling options
bm_DefaultModelingOptions()
# Create default modeling options
myBiomodOptions <- BIOMOD_ModelingOptions()
myBiomodOptions
# # Part (or totality) of the print can be copied and customized
# # Below is an example to compute quadratic GLM and select best model with 'BIC' criterium
# myBiomodOptions <- BIOMOD_ModelingOptions(
# GLM = list(type = 'quadratic',
# interaction.level = 0,
# myFormula = NULL,
# test = 'BIC',
# family = 'binomial',
# control = glm.control(epsilon = 1e-08,
# maxit = 1000,
# trace = FALSE)))
# myBiomodOptions
#
# # It is also possible to give a specific GLM formula
# myForm <- 'Sp277 ~ bio3 + log(bio10) + poly(bio16, 2) + bio19 + bio3:bio19'
# myBiomodOptions <- BIOMOD_ModelingOptions(GLM = list(myFormula = formula(myForm)))
# myBiomodOptions
### Model parameters can also be automatically tuned for your specific
### dataset with an optimization algorithm. The tuning can however
### be quite long. Duration for tuning all models sequentially
### with default optimization settings :
### on 1 x 2.5 GHz processor: approx. 45 min tuning all models
### on 8 x 2.5 GHz processor: approx. 15 min tuning all models
#
# # library(doParallel)
# # cl <- makeCluster(8)
# # doParallel::registerDoParallel(cl)
#
# time.seq <- system.time(
# bm.tuning <- BIOMOD_Tuning(bm.format = myBiomodData, ME.env = myExpl, ME.n.bg = ncell(myExpl))
# )
#
# # stopCluster(cl)
#
# plot(bm.tuning$tune.CTA.rpart)
# plot(bm.tuning$tune.CTA.rpart2)
# plot(bm.tuning$tune.RF)
# plot(bm.tuning$tune.ANN)
# plot(bm.tuning$tune.MARS)
# plot(bm.tuning$tune.FDA)
# plot(bm.tuning$tune.GBM)
# plot(bm.tuning$tune.GAM)
#
# # Get tuned modeling options
# myBiomodOptions <- bm.tuning$models.options
# Create the different validation datasets
myBiomodCV <- BIOMOD_CrossValidation(bm.format = myBiomodData)
head(myBiomodCV)
# # Several validation strategies can be combined
# DataSplitTable.b <- BIOMOD_CrossValidation(bm.format = myBiomodData,
# k = 5,
# nb.rep = 2,
# do.full.models = FALSE)
# DataSplitTable.y <- BIOMOD_CrossValidation(bm.format = myBiomodData,
# k = 2,
# do.stratification = TRUE,
# method = "y")
# colnames(DataSplitTable.y)[1:2] <- c("RUN11", "RUN12")
# myBiomodCV <- cbind(DataSplitTable.b, DataSplitTable.y)
# head(myBiomodCV)
# Model single models
myBiomodModelOut <- BIOMOD_Modeling(bm.format = myBiomodData,
bm.options = myBiomodOptions,
modeling.id = 'AllModels',
nb.rep = 2,
data.split.perc = 80,
# data.split.table = myBiomodCV,
var.import = 3,
metric.eval = c('TSS','ROC'),
do.full.models = FALSE)
# seed.val = 123)
# nb.cpu = 8)
myBiomodModelOut
# Get evaluation scores & variables importance
get_evaluations(myBiomodModelOut)
get_variables_importance(myBiomodModelOut, as.data.frame = TRUE)
# 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', 'dataset'))
bm_PlotVarImpBoxplot(bm.out = myBiomodModelOut, group.by = c('algo', 'expl.var', 'dataset'))
# 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',
metric.select = c('TSS'),
metric.select.thresh = c(0.7),
var.import = 3,
metric.eval = c('TSS', 'ROC'),
prob.mean = TRUE,
prob.median = TRUE,
prob.cv = TRUE,
prob.ci = TRUE,
prob.ci.alpha = 0.05,
committee.averaging = TRUE,
prob.mean.weight = TRUE,
prob.mean.weight.decay = 'proportional')
myBiomodEM
# Get evaluation scores & variables importance
get_evaluations(myBiomodEM, as.data.frame = TRUE)
get_variables_importance(myBiomodEM, as.data.frame = TRUE)
# Represent evaluation scores & variables importance
bm_PlotEvalMean(bm.out = myBiomodEM, group.by = 'model')
bm_PlotEvalBoxplot(bm.out = myBiomodEM, group.by = c('model', 'model'))
bm_PlotVarImpBoxplot(bm.out = myBiomodEM, group.by = c('expl.var', 'model', 'model'))
bm_PlotVarImpBoxplot(bm.out = myBiomodEM, group.by = c('expl.var', 'model', 'dataset'))
bm_PlotVarImpBoxplot(bm.out = myBiomodEM, group.by = c('model', 'expl.var', 'dataset'))
# 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)
# Evaluate models with Boyce index and MPA
myBiomodPO <- BIOMOD_PresenceOnly(bm.mod = myBiomodModelOut,
bm.em = myBiomodEM)
myBiomodPO
# Evaluate models with Boyce index and MPA (using background data)
myBiomodPO <- BIOMOD_PresenceOnly(bm.mod = myBiomodModelOut,
bm.em = myBiomodEM,
bg.env = values(myExpl))
myBiomodPO
# 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 <- rast("GuloGulo/proj_Current/proj_Current_GuloGulo_TSSbin.grd")
FutureProj <- rast("GuloGulo/proj_Future/proj_Future_GuloGulo_TSSbin.grd")
# 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"))