BIOMOD.formated.data.PA: `BIOMOD_FormatingData()` output object class (with pseudo-absences)

Description

Class returned by BIOMOD_FormatingData, and used by BIOMOD_Tuning, BIOMOD_CrossValidation and BIOMOD_Modeling

Usage

# S4 method for 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.user.table = NULL,
  na.rm = TRUE
)
# S4 method for numeric,RasterStack
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.user.table = NULL,
  na.rm = TRUE
)
# S4 method for BIOMOD.formated.data.PA,missing
plot(x, coord = NULL, col = NULL)
# S4 method for BIOMOD.formated.data.PA
show(object)

Arguments

sp: a vector, SpatialPoints (if presence-only) or SpatialPointsDataFrame 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)
env: a matrix, data.frame, SpatialPointsDataFrame or RasterStack object containing the explanatory variables (in columns or layers) that will be used to build the species distribution model(s)
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, SpatialPoints (if presence-only) or SpatialPointsDataFrame 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
eval.env: (optional, default NULL)
A matrix, data.frame, SpatialPointsDataFrame or RasterStack object containing the explanatory variables (in columns or layers) that will be used to evaluate the species distribution model(s) with independent data
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 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 meters, 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 meters, 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.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
x: a BIOMOD.formated.data.PA object
coord: a 2-columns data.frame containing X and Y coordinates for plot
col: a vector containing colors for plot (default : c('green', 'red', 'orange', 'grey'))
object: a BIOMOD.formated.data.PA object

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 RasterStack 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

Run this code


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

## ----------------------------------------------------------------------- #

# Load species occurrences (6 species available)
myFile <- system.file('external/species/mammals_table.csv', package = 'biomod2')
DataSpecies <- read.csv(myFile, row.names = 1)
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)
myFiles <- paste0('external/bioclim/current/bio', c(3, 4, 7, 11, 12), '.grd')
myExpl <- raster::stack(system.file(myFiles, package = 'biomod2'))

# \dontshow{
myExtent <- raster::extent(0,30,45,70)
myExpl <- raster::stack(raster::crop(myExpl, myExtent))
# }

## ----------------------------------------------------------------------- #
# Format Data with pseudo-absences : random method
myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
                                     expl.var = myExpl,
                                     resp.xy = myRespXY,
                                     resp.name = myRespName,
                                     PA.nb.rep = 0,
                                     PA.strategy = 'random',
                                     PA.nb.absences = 1000)
myBiomodData
plot(myBiomodData)