BIOMOD_FormatingData() output object class (with pseudo-absences)

Class returned by BIOMOD_FormatingData, and used by bm_Tuning, bm_CrossValidation and BIOMOD_Modeling

# S4 method for class 'numeric,data.frame'
BIOMOD.formated.data.PA(
  sp,
  env,
  xy = NULL,
  dir.name = ".",
  sp.name = NULL,
  eval.sp = NULL,
  eval.env = NULL,
  eval.xy = NULL,
  PA.nb.rep = 1,
  PA.strategy = "random",
  PA.nb.absences = NULL,
  PA.dist.min = 0,
  PA.dist.max = NULL,
  PA.sre.quant = 0.025,
  PA.fact.aggr = NULL,
  PA.user.table = NULL,
  na.rm = TRUE,
  filter.raster = FALSE,
  seed.val = NULL
)

# S4 method for class 'numeric,SpatRaster'
BIOMOD.formated.data.PA(
  sp,
  env,
  xy = NULL,
  dir.name = ".",
  sp.name = NULL,
  eval.sp = NULL,
  eval.env = NULL,
  eval.xy = NULL,
  PA.nb.rep = 1,
  PA.strategy = "random",
  PA.nb.absences = NULL,
  PA.dist.min = 0,
  PA.dist.max = NULL,
  PA.sre.quant = 0.025,
  PA.fact.aggr = NULL,
  PA.user.table = NULL,
  na.rm = TRUE,
  filter.raster = FALSE,
  seed.val = NULL
)

Arguments

sp: a vector, a SpatVector without associated data (if presence-only), or a SpatVector object containing binary data (0 : absence, 1 : presence, NA : indeterminate) for a single species that will be used to build the species distribution model(s)
Note that old format from sp are still supported such as SpatialPoints (if presence-only) or SpatialPointsDataFrame object containing binary data.
env: a matrix, data.frame, SpatVector or SpatRaster object containing the explanatory variables (in columns or layers) that will be used to build the species distribution model(s).
Note that old format from raster and sp are still supported such as RasterStack and SpatialPointsDataFrame objects.
xy: (optional, default NULL)
If resp.var is a vector, a 2-columns matrix or data.frame containing the corresponding X and Y coordinates that will be used to build the species distribution model(s)
dir.name: a character corresponding to the modeling folder
sp.name: a character corresponding to the species name
eval.sp: (optional, default NULL)
A vector, a SpatVector without associated data (if presence-only), or a SpatVector object containing binary data (0 : absence, 1 : presence, NA : indeterminate) for a single species that will be used to evaluate the species distribution model(s) with independent data
Note that old format from sp are still supported such as SpatialPoints (if presence-only) or SpatialPointsDataFrame object containing binary data.
eval.env: (optional, default NULL)
A matrix, data.frame, SpatVector or SpatRaster object containing the explanatory variables (in columns or layers) that will be used to evaluate the species distribution model(s) with independent data
Note that old format from raster and sp are still supported such as RasterStack and SpatialPointsDataFrame objects.
eval.xy: (optional, default NULL)
If resp.var is a vector, a 2-columns matrix or data.frame containing the corresponding X and Y coordinates that will be used to evaluate the species distribution model(s) with independent data
PA.nb.rep: (optional, default 0)
If pseudo-absence selection, an integer corresponding to the number of sets (repetitions) of pseudo-absence points that will be drawn
PA.strategy: (optional, default NULL)
If pseudo-absence selection, a character defining the strategy that will be used to select the pseudo-absence points. Must be random, sre, disk or user.defined (see Details)
PA.nb.absences: (optional, default 0)
If pseudo-absence selection, and PA.strategy = 'random' or PA.strategy = 'sre' or PA.strategy = 'disk', an integer (or a vector of integer the same size as PA.nb.rep) corresponding to the number of pseudo-absence points that will be selected for each pseudo-absence repetition (true absences included)
PA.dist.min: (optional, default 0)
If pseudo-absence selection and PA.strategy = 'disk', a numeric defining the minimal distance to presence points used to make the disk pseudo-absence selection (in the same projection system units as coord, see Details)
PA.dist.max: (optional, default 0)
If pseudo-absence selection and PA.strategy = 'disk', a numeric defining the maximal distance to presence points used to make the disk pseudo-absence selection (in the same projection system units as coord, see Details)
PA.sre.quant: (optional, default 0)
If pseudo-absence selection and PA.strategy = 'sre', a numeric between 0 and 0.5 defining the half-quantile used to make the sre pseudo-absence selection (see Details)
PA.fact.aggr: (optional, default NULL)
If strategy = 'random' or strategy = 'disk', a integer defining the factor of aggregation to reduce the resolution
PA.user.table: (optional, default NULL)
If pseudo-absence selection and PA.strategy = 'user.defined', a matrix or data.frame with as many rows as resp.var values, as many columns as PA.nb.rep, and containing TRUE or FALSE values defining which points will be used to build the species distribution model(s) for each repetition (see Details)
na.rm: (optional, default TRUE)
A logical value defining whether points having one or several missing values for explanatory variables should be removed from the analysis or not
filter.raster: (optional, default FALSE)
If env is of raster type, a logical value defining whether sp is to be filtered when several points occur in the same raster cell
seed.val: (optional, default NULL)
An integer value corresponding to the new seed value to be set

Slots

dir.name: a character corresponding to the modeling folder
sp.name: a character corresponding to the species name
coord: a 2-columns data.frame containing the corresponding X and Y coordinates
data.species: a vector containing the species observations (0, 1 or NA)
data.env.var: a data.frame containing explanatory variables
data.mask: a SpatRaster object containing the mask of the studied area
has.data.eval: a logical value defining whether evaluation data is given
eval.coord: (optional, default NULL)
A 2-columns data.frame containing the corresponding X and Y coordinates for evaluation data
eval.data.species: (optional, default NULL)
A vector containing the species observations (0, 1 or NA) for evaluation data
eval.data.env.var: (optional, default NULL)
A data.frame containing explanatory variables for evaluation data
PA.strategy: a character corresponding to the pseudo-absence selection strategy
PA.table: a data.frame containing the corresponding table of selected pseudo-absences (indicated by TRUE or FALSE) from the pa.tab list element returned by the bm_PseudoAbsences function

Author

Damien Georges

Examples


showClass("BIOMOD.formated.data.PA")

## ----------------------------------------------------------------------- #
library(terra)

# Load species occurrences (6 species available)
data(DataSpecies)
head(DataSpecies)

# Select the name of the studied species
myRespName <- 'GuloGulo'

# Keep only presence informations
DataSpecies <- DataSpecies[which(DataSpecies[, myRespName] == 1), ]

# 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)
})

## ----------------------------------------------------------------------- #
# Format Data with pseudo-absences : random method
myBiomodData <- BIOMOD_FormatingData(resp.name = myRespName,
                                     resp.var = myResp,
                                     resp.xy = myRespXY,
                                     expl.var = myExpl,
                                     PA.nb.rep = 4,
                                     PA.strategy = 'random',
                                     PA.nb.absences = 1000)
myBiomodData
plot(myBiomodData)

`BIOMOD_FormatingData()` output object class (with pseudo-absences)

Arguments

Slots

See also

Author

Examples