R/BIOMOD_Projection.R
BIOMOD_Projection.RdThis function allows to project a range of models built with the
BIOMOD_Modeling function onto new environmental data (which can
represent new areas, resolution or time scales for example).
BIOMOD_Projection(
bm.mod,
proj.name,
new.env,
new.env.xy = NULL,
models.chosen = "all",
metric.binary = NULL,
metric.filter = NULL,
compress = TRUE,
build.clamping.mask = TRUE,
nb.cpu = 1,
seed.val = NULL,
...
)a BIOMOD.models.out object returned by the
BIOMOD_Modeling function
a character corresponding to the name (ID) of the projection set
(a new folder will be created within the simulation folder with this name)
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 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
(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_Modeling 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
(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_Modeling 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
(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, xz or
gzip (see Details)
(optional, default TRUE)
A logical value defining whether a clamping mask should be built and saved on hard
drive or not (see Details)
(optional, default 1)
An integer value corresponding to the number of computing resources to be used to
parallelize the single models computation
(optional, default NULL)
An integer value corresponding to the new seed value to be set
(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
If models.chosen = 'all', projections are done for all calibration and pseudo absences
runs if applicable.
These projections may be used later by the
BIOMOD_EnsembleForecasting function.
If build.clamping.mask = TRUE, a raster file will be saved within the projection folder.
This mask values will correspond to the number of variables in each pixel that are out of their
calibration / validation range, identifying locations where predictions are uncertain.
... can take the following values :
omit.na : a logical value defining whether all not fully referenced
environmental points will get NA as predictions or not
on_0_1000 : a logical value defining whether 0 - 1 probabilities
are to be converted to 0 - 1000 scale to save memory on backup
do.stack : 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)
keep.in.memory : 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
output.format : 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)
BIOMOD_Modeling, BIOMOD_EnsembleModeling,
BIOMOD_RangeSize
Other Main functions:
BIOMOD_EnsembleForecasting(),
BIOMOD_EnsembleModeling(),
BIOMOD_FormatingData(),
BIOMOD_LoadModels(),
BIOMOD_Modeling(),
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.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)
}
# ---------------------------------------------------------------#
# Project single models
file.proj <- paste0(myRespName, "/proj_Current/", myRespName, ".Current.projection.out")
if (file.exists(file.proj)) {
myBiomodProj <- get(load(file.proj))
} else {
myBiomodProj <- BIOMOD_Projection(bm.mod = myBiomodModelOut,
proj.name = 'Current',
new.env = myExpl,
models.chosen = 'all')
}
myBiomodProj
plot(myBiomodProj)