R/BIOMOD_EnsembleForecasting.R
BIOMOD_EnsembleForecasting.RdThis function allows to project ensemble models built with the
BIOMOD_EnsembleModeling function onto new environmental data
(which can represent new areas, resolution or time scales for example).
BIOMOD_EnsembleForecasting(
bm.em,
bm.proj = NULL,
proj.name = NULL,
new.env = NULL,
new.env.xy = NULL,
models.chosen = "all",
metric.binary = NULL,
metric.filter = NULL,
na.rm = TRUE,
nb.cpu = 1,
...
)a BIOMOD.ensemble.models.out object returned by the
BIOMOD_EnsembleModeling function
a BIOMOD.projection.out object returned by the
BIOMOD_Projection function
(optional, default NULL)
If bm.proj = NULL, a character corresponding to the name (ID) of the
projection set (a new folder will be created within the simulation folder with this
name)
(optional, default NULL)
If bm.proj = NULL, a matrix, data.frame or
SpatRaster object containing the new
explanatory variables (in columns or layers, with names matching the
variables names given to the BIOMOD_FormatingData function to build
bm.mod) that will be used to project the species distribution model(s)
Note that old format from raster are still supported such as
RasterStack objects.
(optional, default NULL)
If new.env is a matrix or a data.frame, a 2-columns matrix or
data.frame containing the corresponding X and Y coordinates that will
be used to project the ensemble species distribution model(s)
a vector containing model names to be kept, must be either
all or a sub-selection of model names that can be obtained with the
get_built_models function applied to bm.mod
(optional, default NULL)
A vector containing evaluation metric names to be used to transform prediction values
into binary values based on models evaluation scores obtained with the
BIOMOD_EnsembleModeling function. Must be among all (same evaluation
metrics than those of bm.mod) or POD, FAR, POFD, SR,
ACCURACY, BIAS, ROC, TSS, KAPPA, OR, ORSS,
CSI, ETS, BOYCE, MPA
Note that this is for binary data only.
(optional, default NULL)
A vector containing evaluation metric names to be used to transform prediction values
into filtered values based on models evaluation scores obtained with the
BIOMOD_EnsembleModeling function. Must be among all (same evaluation
metrics than those of bm.mod) or POD, FAR, POFD, SR,
ACCURACY, BIAS, ROC, TSS, KAPPA, OR, ORSS,
CSI, ETS, BOYCE, MPA
Note that this is for binary data only.
(optional, default TRUE)
A logical value defining whether ensemble model projection should ignore missing values
in single model projections or not (ignored by EMwmean algorithm)
(optional, default 1)
An integer value corresponding to the number of computing resources to be used to
parallelize the single models computatio
(optional, see Details)
A BIOMOD.projection.out object containing models projections, or links to saved
outputs.
Models projections are stored out of R (for memory storage reasons) in
proj.name folder created in the current working directory :
the output is a data.frame if new.env is a matrix or a
data.frame
it is a SpatRaster if new.env is a
SpatRaster (or several SpatRaster
objects, if new.env is too large)
raw projections, as well as binary and filtered projections (if asked), are saved in
the proj.name folder
... can take the following values :
(optional, default 0) :
an integer value corresponding to the number of digits of the predictions
(optional, default TRUE) :
a logical value defining whether 0 - 1 probabilities are to be converted to
0 - 1000 scale to save memory on backup
(optional, default TRUE) :
a logical value defining whether all projections are to be kept loaded at once in
memory, or only links pointing to hard drive are to be returned
(optional, default TRUE) :
a logical value defining whether all projections are to be saved as one
SpatRaster object or several SpatRaster
files (the default if projections are too heavy to be all loaded at once in memory)
(optional, default .RData or .tif) :
a character value corresponding to the projections saving format on hard drive, must
be either .grd, .img, .tif or .RData (the default if
new.env is given as matrix or data.frame)
(optional, default TRUE) :
a logical or a character value defining whether and how objects should be
compressed when saved on hard drive. Must be either TRUE, FALSE, gzip
(for Windows OS) or xz (for other OS)
BIOMOD_FormatingData, bm_ModelingOptions,
BIOMOD_Modeling, BIOMOD_EnsembleModeling,
BIOMOD_RangeSize
Other Main functions:
BIOMOD_EnsembleModeling(),
BIOMOD_FormatingData(),
BIOMOD_LoadModels(),
BIOMOD_Modeling(),
BIOMOD_Projection(),
BIOMOD_RangeSize()
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)
DONTSHOW({
myExtent <- terra::ext(0,30,45,70)
myExpl <- terra::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.name = myRespName,
resp.var = myResp,
resp.xy = myRespXY,
expl.var = myExpl)
# 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)
}
file.proj <- paste0(myRespName, "/proj_Current/", myRespName, ".Current.projection.out")
if (file.exists(file.proj)) {
myBiomodProj <- get(load(file.proj))
} else {
# Project single models
myBiomodProj <- BIOMOD_Projection(bm.mod = myBiomodModelOut,
proj.name = 'Current',
new.env = myExpl,
models.chosen = 'all',
build.clamping.mask = TRUE)
}
file.EM <- paste0(myRespName, "/", myRespName, ".AllModels.ensemble.models.out")
if (file.exists(file.EM)) {
myBiomodEM <- get(load(file.EM))
} else {
# Model ensemble models
myBiomodEM <- BIOMOD_EnsembleModeling(bm.mod = myBiomodModelOut,
models.chosen = 'all',
em.by = 'all',
em.algo = c('EMmean', 'EMca'),
metric.select = c('TSS'),
metric.select.thresh = c(0.7),
metric.eval = c('TSS', 'ROC'),
var.import = 3,
seed.val = 42)
}
# --------------------------------------------------------------- #
# 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)