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R/bm_PseudoAbsences.R
In biomod2: Ensemble Platform for Species Distribution Modeling
Defines functions
.add_pa_rownames
.get_nb_available_pa_cells
.get_nb_true_abs
.bm_PseudoAbsences.check.args
bm_PseudoAbsences
Documented in
bm_PseudoAbsences
# bm_PseudoAbsences doc ---------------------------------------------------------
##' @name bm_PseudoAbsences
##' @aliases bm_PseudoAbsences
##' @aliases bm_PseudoAbsences_random
##' @aliases bm_PseudoAbsences_sre
##' @aliases bm_PseudoAbsences_disk
##' @aliases bm_PseudoAbsences_user.defined
##' @author Wilfried Thuiller, Damien Georges
##'
##' @title Select pseudo-absences
##'
##' @description This internal \pkg{biomod2} function allows to select pseudo-absences according
##' to 4 different methods : \code{random}, \code{sre}, \code{disk} or \code{user.defined} (see Details).
##'
##'
##' @param resp.var a \code{vector}, \code{\link[sp]{SpatialPoints}} or
##' \code{\link[sp]{SpatialPointsDataFrame}} object containing binary data (\code{0} : absence,
##' \code{1} : presence, \code{NA} : indeterminate) for a single species that will be used to
##' find the pseudo-absences
##' @param expl.var a \code{matrix}, \code{data.frame}, \code{\link[sp]{SpatialPointsDataFrame}}
##' or \code{\link[terra:rast]{SpatRaster}} object containing the explanatory variables (in
##' columns or layers) that will be used to find the pseudo-absences
##' @param \ldots (\emph{optional, one or several of the following arguments depending on the selected
##' method})
##'
##' @param nb.rep an \code{integer} corresponding to the number of sets (repetitions) of
##' pseudo-absence points that will be drawn
##' @param strategy a \code{character} corresponding to the pseudo-absence selection strategy,
##' must be among \code{random}, \code{sre}, \code{disk} or \code{user.defined}
##' @param nb.absences (\emph{optional, default} \code{NULL}) \cr
##' If \code{strategy = 'random'} or \code{strategy = 'sre'} or \code{strategy = 'disk'}, an
##' \code{integer} corresponding to the number of pseudo-absence points that will be selected for
##' each pseudo-absence repetition (true absences included)
##' @param sre.quant (\emph{optional, default} \code{0}) \cr
##' If \code{strategy = 'sre'}, a \code{numeric} between \code{0} and \code{0.5} defining the
##' half-quantile used to make the \code{sre} pseudo-absence selection (see \code{\link{bm_SRE}})
##' @param dist.min (\emph{optional, default} \code{0}) \cr
##' If \code{strategy = 'disk'}, a \code{numeric} defining the minimal distance to presence points
##' used to make the \code{disk} pseudo-absence selection (in meters)
##' @param dist.max (\emph{optional, default} \code{NULL}) \cr
##' If \code{strategy = 'disk'}, a \code{numeric} defining the maximal distance to presence points
##' used to make the \code{disk} pseudo-absence selection (in meters)
##' @param user.table (\emph{optional, default} \code{NULL}) \cr
##' If \code{strategy = 'user.defined'}, a \code{matrix} or \code{data.frame} with as many rows as
##' \code{resp.var} values, as many columns as \code{nb.rep}, and containing \code{TRUE} or
##' \code{FALSE} values defining which points will be used to build the species distribution
##' model(s) for each repetition
##'
##'
##' @return
##'
##' A \code{list} containing the following elements :
##' \itemize{
##' \item{\code{xy} : }{the coordinates of the species observations}
##' \item{\code{sp} : }{the values of the species observations (\code{0}, \code{1} or \code{NA})}
##' \item{\code{env} : }{the explanatory variables}
##' \item{\code{pa.tab} : }{the corresponding table of selected pseudo-absences (indicated by
##' \code{TRUE} or \code{FALSE})}
##' }
##'
##'
##' @details
##'
##' \bold{Concerning random selection :}
##'
##' The idea is to select pseudo-absences randomly in spatial locations where the species has not
##' been sampled. This method is the simplest one and the most appropriate if lacking information
##' about the presence sampling (non-exhaustive, biased sampling, etc). \cr \cr
##'
##' \bold{Concerning SRE selection :}
##'
##' The idea is to select pseudo-absences in spatial locations whose environmental conditions are
##' different from those of the presence points. This method is appropriate when most of the
##' environmental space of the species has been sampled. \cr \cr
##'
##' \bold{Concerning disk selection :}
##'
##' The idea is to select pseudo-absences, not too close from presence points, but not too far
##' away either. This method is appropriate when most of the spatial range of the species has
##' been sampled. \cr \cr
##'
##' \bold{Concerning user defined selection :}
##'
##' The user can provide pseudo-absences locations through a table containing spatial locations
##' in rows, pseudo-absences repetitions in columns, and \code{TRUE/FALSE} values indicating
##' whether each point is to be considered as pseudo-absence or not for each dataset.
##'
##'
##' @keywords pseudo-absence random SRE disk
##'
##'
##' @seealso \code{\link{BIOMOD.formated.data.PA}}, \code{\link{BIOMOD_FormatingData}}
##' @family Secundary functions
##'
##'
##' @examples
##'
##' 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)
##' }
##'
##'
##' # --------------------------------------------------------------- #
##' # Create the different pseudo-absence datasets
##'
##' # Transform true absences into potential pseudo-absences
##' myResp.PA <- ifelse(myResp == 1, 1, NA)
##' myResp.PA.vect <- vect(cbind(myRespXY, myResp.PA), geom = c("X_WGS84","Y_WGS84"))
##'
##' # random method
##' PA.r <- bm_PseudoAbsences(resp.var = myResp.PA,
##' expl.var = myExpl,
##' nb.rep = 4,
##' nb.absences = 1000,
##' strategy = 'random')
##'
##' # disk method
##' PA.d <- bm_PseudoAbsences(resp.var = myResp.PA,
##' expl.var = myExpl,
##' nb.rep = 4,
##' nb.absences = 500,
##' strategy = 'disk',
##' dist.min = 5,
##' dist.max = 35)
##'
##' # SRE method
##' PA.s <- bm_PseudoAbsences(resp.var = myResp.PA,
##' expl.var = myExpl,
##' nb.rep = 4,
##' nb.absences = 1000,
##' strategy = 'sre',
##' sre.quant = 0.025)
##'
##' # 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
##' PA.u <- bm_PseudoAbsences(resp.var = myResp.PA,
##' expl.var = myExpl,
##' strategy = 'user.defined',
##' user.table = myPAtable)
##'
##' str(PA.r)
##' head(PA.r$pa.tab)
##' apply(PA.r$pa.tab, 2, table)
##'
##' head(PA.d$pa.tab)
##' apply(PA.d$pa.tab, 2, table)
##'
##' head(PA.s$pa.tab)
##' apply(PA.s$pa.tab, 2, table)
##'
##' tail(PA.u$pa.tab)
##' apply(PA.u$pa.tab, 2, table)
##'
##'
##' # random method : different number of PA
##' PA.r_mult <- bm_PseudoAbsences(resp.var = myResp.PA.vect,
##' expl.var = myExpl,
##' nb.rep = 4,
##' nb.absences = c(1000, 500, 500, 200),
##' strategy = 'random')
##'
##' str(PA.r_mult)
##' head(PA.r_mult$pa.tab)
##' apply(PA.r_mult$pa.tab, 2, table)
##'
##'
##' @importFrom terra rast vect freq spatSample values extract
##' @importFrom utils packageVersion
##'
##' @export
##'
##'
## --------------------------------------------------------------------------- #
bm_PseudoAbsences <- function(resp.var, expl.var, nb.rep = 1, strategy = 'random', nb.absences = NULL
, sre.quant = 0, dist.min = 0, dist.max = NULL, user.table = NULL)
{
## 0. Check arguments ---------------------------------------------------------------------------
args <- .bm_PseudoAbsences.check.args(resp.var, expl.var, nb.rep, strategy, nb.absences
, sre.quant, dist.min, dist.max, user.table)
for (argi in names(args)) { assign(x = argi, value = args[[argi]]) }
rm(args)
## 1. Create output object ----------------------------------------------------------------------
if ((nb.rep == 0 || any(nb.absences <= 0)) & strategy != 'user.defined') {
out <- NULL
} else {
if (length(nb.absences) == 1) {
out <- switch(strategy,
user.defined = bm_PseudoAbsences_user.defined(resp.var, expl.var, user.table),
random = bm_PseudoAbsences_random(resp.var, expl.var, nb.absences, nb.rep),
sre = bm_PseudoAbsences_sre(resp.var, expl.var, sre.quant, nb.absences, nb.rep),
disk = bm_PseudoAbsences_disk(resp.var, expl.var, dist.min, dist.max, nb.absences, nb.rep))
} else if (length(nb.absences) == nb.rep) {
out.list = foreach(i.abs = unique(nb.absences)) %do%
{
i.rep = which(nb.absences == i.abs)
cat("\n > Set ", paste0(i.rep, collapse = ", "), " (", i.abs, " pseudo absences wanted)", sep = "")
out <- switch(strategy,
user.defined = bm_PseudoAbsences_user.defined(resp.var, expl.var, user.table),
random = bm_PseudoAbsences_random(resp.var, expl.var, i.abs, length(i.rep)),
sre = bm_PseudoAbsences_sre(resp.var, expl.var, sre.quant, i.abs, length(i.rep)),
disk = bm_PseudoAbsences_disk(resp.var, expl.var, dist.min, dist.max, i.abs, length(i.rep)))
## CASE where all available cells have been selected :
## give back only one dataset, even if several were asked
if (ncol(out$pa.tab) == 1 && length(i.rep) > 1) {
col1 <- out$pa.tab
for (j in 2:length(i.rep)) {
out$pa.tab <- cbind(out$pa.tab, col1)
}
# i.rep = i.rep[1] ## NOT working with summary and plot functions
}
colnames(out$pa.tab) = paste0("PA", i.rep)
return(out)
}
## GET XY -----------------------------------------------------
## Get coordinates of presences
ind.pres = which(out.list[[1]][["sp"]] == 1)
xy.pres = out.list[[1]][["xy"]][ind.pres, ]
nb.pres = length(ind.pres)
## Get coordinates of pseudo-absences
out.xy = foreach(i = 1:length(out.list)) %do%
{
ind.keep = 1:nrow(out.list[[i]][["xy"]])
ind.keep = ind.keep[-which(ind.keep %in% ind.pres)]
res = out.list[[i]][["xy"]][ind.keep, ]
res = cbind(res, ind.keep)
return(res)
}
## Merge all coordinates of pseudo-absences (may be duplicates)
out.xy = Reduce(function(x, y) merge(x, y, by = c("x", "y"), all = TRUE), out.xy)
## Get indexes of merged PA coordinates for each set
## To be used to rebuild env and pa.tab
out.index = out.xy[, -which(colnames(out.xy) %in% c("x", "y"))]
out.order = !is.na(out.index)
out.order = t(apply(out.order, 1, cumsum))
## Keep presences + pseudo-absences coordinates
out.xy = out.xy[, c("x", "y")]
out.xy = rbind(xy.pres, out.xy)
out.sp = c(rep(1, nb.pres), rep(NA, nrow(out.xy) - nb.pres))
## GET ENV & PA.TAB -------------------------------------------
## Initialize env matrix
out.env = matrix(NA, nrow = nrow(out.xy), ncol = ncol(out.list[[1]][["env"]]))
out.env = as.data.frame(out.env)
colnames(out.env) = colnames(out.list[[1]][["env"]])
out.env[1:nb.pres, ] = out.list[[1]][["env"]][1:nb.pres, ]
## Initialize pa.tab matrix
out.pa.tab = matrix(NA, nrow = nrow(out.xy), ncol = nb.rep)
out.pa.tab = as.data.frame(out.pa.tab)
colnames(out.pa.tab) = paste0("PA", 1:nb.rep)
out.pa.tab[1:nb.pres, ] = TRUE
ind.start = 1
ind.end = ncol(out.list[[1]][["pa.tab"]])
## Fill first column
ind = which(out.order[, 1] == 1)
out.env[ind + nb.pres, ] = out.list[[1]][["env"]][out.index[ind, 1], ]
out.pa.tab[ind + nb.pres, ind.start:ind.end] = out.list[[1]][["pa.tab"]][out.index[ind, 1], ]
## Fill all other columns
for (j in 2:ncol(out.order)) {
ind = which(out.order[, j] != out.order[, j-1])
if (length(ind) > 0) {
## For env
out.env[ind + nb.pres, ] = out.list[[j]][["env"]][out.index[ind, j], ]
## For pa.tab
ind.start = ind.end + 1
ind.end = ind.start + ncol(out.list[[j]][["pa.tab"]]) - 1
out.pa.tab[ind + nb.pres, ind.start:ind.end] = out.list[[j]][["pa.tab"]][out.index[ind, j], ]
}
}
## GET everything ---------------------------------------------
out = list(xy = out.xy,
sp = out.sp,
env = out.env,
pa.tab = out.pa.tab)
}
}
cat("\n")
return(out)
}
# Argument Check --------------------------------------------------------------
.bm_PseudoAbsences.check.args <- function(resp.var, expl.var, nb.rep, strategy, nb.absences, sre.quant, dist.min, dist.max, user.table)
{
cat('\n\nChecking Pseudo-absence selection arguments...\n')
## 1. Check resp.var argument -----------------------------------------------
if (is.vector(resp.var)) {
resp.var <- vect(data.frame(x = 0,
y = 0,
resp = resp.var),
geom = c("x","y"))
if (!is.null(nb.absences) && length(nb.absences) > 1) {
stop("Selection of multiple number of pseudo-absences depends on coordinates. Please provide some.")
}
}
.fun_testIfInherits(TRUE, "resp.var", resp.var, "SpatVector")
## 2. Check expl.var argument -----------------------------------------------
if (inherits(expl.var, c("matrix","data.frame"))) {
if (nrow(expl.var) != nrow(resp.var)) {
stop("Species and Explanatory must have same dimensions")
}
# transform expl.var into SpatVector
rownames(expl.var) <- NULL
expl.var <- vect(
cbind(data.frame(x = crds(resp.var)[,1],
y = crds(resp.var)[,2]),
as.data.frame(expl.var)
),
geom = c("x","y")
)
}
.fun_testIfInherits(TRUE, "expl.var", expl.var, c("SpatVector", "SpatRaster"))
args <- .BIOMOD.formated.data.check.args(sp = resp.var, env = expl.var)
for (argi in names(args)) { assign(x = argi, value = args[[argi]]) }
rm(args)
## 3. Check strategy argument -----------------------------------------------
availableStrategies <- c("random", "sre", "disk", "user.defined")
if (is.null(strategy) || !(strategy %in% availableStrategies) ||
(strategy != 'user.defined' && all(crds(resp.var) == 0))) {
# no coordinates or unknown strategy
strategy <- "random"
cat("\n ! Random strategy was automatically selected (that can be due to points coordinates lack or unavailable strategy choosen)")
}
## 4. Check sre.quant argument ----------------------------------------------
if (strategy == 'SRE' && (sre.quant >= 0.5 || sre.quant < 0)) {
stop("\n ! SRE Quant should be a value between 0 and 0.5 ")
}
## 5. Check dist.min and dist.max arguments ---------------------------------
if (strategy == 'disk') {
if (!is.null(dist.min) && dist.min < 0) {
dist.min <- 0
}
if (!is.null(dist.max) && dist.max < 0) {
dist.max <- NULL
}
if (!is.null(dist.max) && !is.null(dist.min) && dist.min >= dist.max) {
stop("dist.min >= dist.max")
}
}
## 6. Check nb.absences argument --------------------------------------------
if (strategy != "user.defined") {
if (is.null(nb.absences)) {
stop("You must give the number of pseudo absences you want")
} else {
if (length(nb.absences) > 1) {
if (length(nb.absences) != nb.rep) {
stop("You must give one value for pseudo absences, or as many as the number of repetitions")
} else if (length(unique(nb.absences)) == 1) {
nb.absences = unique(nb.absences)
}
}
nbTrueAbs <- .get_nb_true_abs(resp.var)
if (nbTrueAbs) {
stop("Your dataset contains true absences. This should not be mixed with pseudo absences selection")
}
}
}
## 7. Check user.table argument --------------------------------------------
if (strategy == "user.defined") {
if (is.null(user.table)) {
stop("You must give a table defining the pseudo absences you want")
} else {
if (!(is.matrix(user.table) | is.data.frame(user.table))) {
stop("\n PA.user.table must be a matrix or a data.frame")
}
if (nrow(user.table) != length(resp.var)) {
stop("\n PA.user.table must have as many row than the number of observation of your response variable")
}
colnames(user.table) <- paste0("PA", 1:ncol(user.table))
nb.absences <- nrow(user.table)
}
}
return(list(resp.var = resp.var,
expl.var = expl.var,
nb.rep = nb.rep,
strategy = strategy,
nb.absences = nb.absences,
sre.quant = sre.quant,
dist.min = dist.min,
dist.max = dist.max,
user.table = user.table))
}
# Additionnal tools ------------------------------------------------------------
.get_nb_true_abs <- function(sp)
{
if (is.vector(sp)) {
return(length(which(sp == 0)))
} else if (inherits(sp, 'SpatVector')) {
return(length(which(values(sp)[,1] == 0)))
} else if (inherits(sp, 'SpatRaster')) {
return(freq(sp, value = 0)$count)
}
}
.get_nb_available_pa_cells <- function(data, PA.flag = NA)
{
if (is.vector(data) || inherits(data, c("matrix","data.frame"))) {
return(
ifelse(is.na(PA.flag),
length(which(is.na(data))),
length(which(data == PA.flag)))
)
} else if (inherits(data, 'SpatVector')) {
return(
ifelse(is.na(PA.flag),
length(which(is.na(as.data.frame(data)[,1]))),
length(which(values(data)[,1] == PA.flag)))
)
} else if (inherits(data, 'SpatRaster')) {
return(
freq(data, value = PA.flag)$count
)
}
}
.add_pa_rownames <- function(xy)
{
rn <- row.names(xy)
# if (length(rn) == 0) {
# rn = rep("", nrow(xy))
# }
missing_rn <- which(rn == "")
if (length(missing_rn) > 0) {
rn[missing_rn] <- paste0("pa", 1:length(missing_rn))
}
rownames(xy) <- rn
return(xy)
}
# bm_PseudoAbsences user-defined methods --------------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##'
setGeneric("bm_PseudoAbsences_user.defined",
def = function(resp.var, expl.var, ...) {
standardGeneric( "bm_PseudoAbsences_user.defined")
})
## bm_PseudoAbsences user-defined SpatVector methods --------------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##'
setMethod('bm_PseudoAbsences_user.defined', signature(expl.var = "SpatVector"),
function(resp.var, expl.var, user.table) {
cat("\n > User defined pseudo absences selection")
return(list(xy = crds(resp.var),
sp = as.numeric(unlist(values(resp.var), use.names = FALSE)),
env = as.data.frame(expl.var),
pa.tab = user.table))
})
## bm_PseudoAbsences user-defined SpatRaster methods --------------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##'
setMethod('bm_PseudoAbsences_user.defined', signature(expl.var = "SpatRaster"),
function(resp.var, expl.var, user.table) {
cat("\n > User defined pseudo absences selection")
expl.var <- extract(expl.var, resp.var, ID = FALSE)
return(list(xy = crds(resp.var),
sp = as.numeric(unlist(values(resp.var), use.names = FALSE)),
env = expl.var,
pa.tab = user.table))
})
# bm_PseudoAbsences random methods --------------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##'
setGeneric("bm_PseudoAbsences_random",
def = function(resp.var, expl.var, ...) {
standardGeneric( "bm_PseudoAbsences_random")
})
## bm_PseudoAbsences random SPDF methods --------------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##'
setMethod('bm_PseudoAbsences_random', signature(expl.var = "SpatVector"),
function(resp.var, expl.var, nb.absences, nb.rep)
{
cat("\n > random pseudo absences selection")
# 1. Check if NA are present in resp.var observations or not to determine which dataset to use
nb.cells <- .get_nb_available_pa_cells(resp.var)
if (nb.cells > 0) { # PA will be taken into response variable
# 2. If nb NA < nb.absences, select all NA cells
if (nb.cells <= nb.absences) {
nb.rep <- 1
nb.absences <- nb.cells
cat("\n > All available cells have been selected (", nb.absences, "cells )")
}
# 3. Select always the presences and the true absences
pa.tab <- matrix(FALSE, ncol = nb.rep, nrow = nrow(resp.var))
colnames(pa.tab) <- paste0("PA", 1:nb.rep)
pa.tab[c(which(values(resp.var)[, 1] == 1),
which(values(resp.var)[, 1] == 0)),] <- TRUE
# 4. For each repetition, select among NA cells
cand.cells <- which(is.na(values(resp.var)[, 1]))
for (j in 1:ncol(pa.tab)) {
## force to get at least one value of each factorial variable
fact.level.cells <- bm_SampleFactorLevels(expl.var = as.data.frame(expl.var),
mask.out = pa.tab[, j, drop = FALSE])
if (length(fact.level.cells) > 0) {
pa.tab[fact.level.cells, j] <- TRUE
cand.cells <- setdiff(cand.cells, fact.level.cells)
}
pa.tab[sample(x = cand.cells,
size = nb.absences - length(fact.level.cells),
replace = FALSE), j] <- TRUE
}
return(list(xy = crds(resp.var),
sp = as.numeric(unlist(values(resp.var), use.names = FALSE)),
env = as.data.frame(expl.var),
pa.tab = pa.tab))
} else {
cat("\nUnsupported case yet!")
return(NULL)
}
})
## bm_PseudoAbsences random SpatRaster methods --------------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##' @importFrom terra rast xyFromCell cellFromXY classify crds extract subset
##'
setMethod('bm_PseudoAbsences_random', signature(expl.var = "SpatRaster"),
function(resp.var, expl.var, nb.absences, nb.rep)
{
cat("\n > random pseudo absences selection")
# 1. Check if NA are present in resp.var observations or not to determine which dataset to use
nb.cells <- .get_nb_available_pa_cells(resp.var)
if (nb.cells > 0) { # PA will be taken into response variable
# 2. If nb NA < nb.absences, select all NA cells
if (nb.cells <= nb.absences) {
nb.rep <- 1
nb.absences <- nb.cells
cat("\n > All available cells have been selected (", nb.absences, "cells )")
}
# 3. Select always the presences and the true absences
pa.tab <- matrix(FALSE, ncol = nb.rep, nrow = nrow(resp.var))
colnames(pa.tab) <- paste0("PA", 1:nb.rep)
pa.tab[c(which(values(resp.var)[, 1] == 1),
which(values(resp.var)[, 1] == 0)),] <- TRUE
# 4. For each repetition, select among NA cells
cand.cells <- which(is.na(values(resp.var)[, 1]))
for (j in 1:ncol(pa.tab)) {
pa.tab[sample(x = cand.cells, size = nb.absences, replace = FALSE), j] <- TRUE
}
expl.var <- extract(expl.var, resp.var, ID = FALSE)
return(list(xy = crds(resp.var),
sp = as.numeric(unlist(values(resp.var), use.names = FALSE)),
env = as.data.frame(expl.var),
pa.tab = as.data.frame(pa.tab)))
} else {
cat("\n > Pseudo absences are selected in explanatory variables")
# create a mask containing all not already sampled points (presences and absences)
## the area we want to sample
mask.out <- mask.env <- .get_data_mask(expl.var, value.out = -1)
# add presences and true absences in our mask
in.cells <- cellFromXY(mask.env, crds(resp.var))
mask.env[in.cells] <- NA
mask.out[in.cells] <- 1
# checking of nb candidates
nb.cells <- .get_nb_available_pa_cells(mask.env, PA.flag = -1)
# 2. If nb NA < nb.absences, select all NA cells
if (nb.cells <= nb.absences) {
nb.rep <- 1
nb.absences <- nb.cells
cat("\n > All available cells have been selected (", nb.absences, "cells )")
}
if (nb.absences == 0) {
cat("\n > No cells are available (0 pseudo absences selected )")
return(NULL)
} else {
# 4. For each repetition, select among raster cells
pa.tab.tmp <- matrix(NA, ncol = nb.rep, nrow = nb.absences)
for (j in 1:ncol(pa.tab.tmp)) {
SR <- NULL ## initialise the vector of sample cells
mask.env.tmp <- mask.env ## define a copy of the sampling mask
## force to get at least one value of each factorial variable
fact.level.cells <- bm_SampleFactorLevels(expl.var = expl.var, mask.out = mask.out)
if (length(fact.level.cells) > 0) {
SR <- c(SR, fact.level.cells)
mask.env.tmp[SR] <- NA ## update the mask by removing already selected cells
}
## repeat sampling until having the right number of points
## spatSample with na.rm = TRUE may return less points than asked
while(length(SR) < nb.absences){
SR <- c(SR, spatSample(x = mask.env.tmp,
method = "random",
size = nb.absences - length(SR),
cells = TRUE,
na.rm = TRUE)[, "cell", drop = TRUE])
mask.env.tmp[SR] <- NA ## update the mask by removing already selected cells
}
pa.tab.tmp[, j] <- SR
}
# putting cells in good format
selected.cells <- sort(unique(as.vector(pa.tab.tmp)))
pa.tab <- matrix(FALSE, ncol = nb.rep, nrow = length(selected.cells))
colnames(pa.tab) <- paste0("PA", 1:nb.rep)
for (j in 1:ncol(pa.tab)) {
pa.tab[selected.cells %in% pa.tab.tmp[,j], j] <- TRUE
}
# putting presences, true absences and pseudo absences together
xy = crds(resp.var)
rownames(xy) = paste0("pres", 1:nrow(xy))
xy <- rbind(xy, xyFromCell(mask.env, selected.cells))
xy <- .add_pa_rownames(xy)
resp.var <- as.numeric(unlist(c(values(resp.var)[, 1],
rep(NA, length(selected.cells))),
use.names = FALSE))
expl.var <- extract(expl.var, xy)
pa.tab <- rbind(matrix(TRUE,
nrow = (nrow(xy) - length(selected.cells)),
ncol = ncol(pa.tab)),
pa.tab)
return(list(xy = xy,
sp = resp.var,
env = as.data.frame(expl.var),
pa.tab = as.data.frame(pa.tab)))
}
}
})
# bm_PseudoAbsences SRE methods --------------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##'
setGeneric("bm_PseudoAbsences_sre",
def = function(resp.var, expl.var, ...) {
standardGeneric("bm_PseudoAbsences_sre")
})
## bm_PseudoAbsences SRE SpatVector methods ----------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##'
setMethod('bm_PseudoAbsences_sre', signature(expl.var = "SpatVector"),
function(resp.var, expl.var, sre.quant, nb.absences, nb.rep)
{
cat("\n > SRE pseudo absences selection")
# 0. calculate SRE to determine available pixels
mask.in <- bm_SRE(resp.var = resp.var, expl.var = expl.var, new.env = values(expl.var), quant = sre.quant)
mask.in <- data.frame(mask.in = !as.logical(mask.in)) ## revert the mask to sample PA out of SRE
mask.in$mask.in[c(which(values(resp.var)[, 1] == 1)
, which(values(resp.var)[, 1] == 0))] <- FALSE
# 1. Check if NA are present in resp.var observations or not to determine which dataset to use
nb.cells <- .get_nb_available_pa_cells(mask.in$mask.in, PA.flag = TRUE)
# 2. If nb NA < nb.absences, select all NA cells
if (nb.cells <= nb.absences) {
nb.rep <- 1
nb.absences <- nb.cells
cat("\n > All available cells have been selected (", nb.absences, "cells )")
}
# 3. Select always the presences and the true absences
pa.tab <- matrix(FALSE, ncol = nb.rep, nrow = nrow(resp.var))
colnames(pa.tab) <- paste0("PA", 1:nb.rep)
pa.tab[c(which(values(resp.var)[, 1] == 1),
which(values(resp.var)[, 1] == 0)),] <- TRUE
# 4. For each repetition, select among NA cells
cand.cells <- which(mask.in$mask.in == TRUE)
for (j in 1:ncol(pa.tab)) {
## force to get at least one value of each factorial variable
fact.level.cells <- bm_SampleFactorLevels(expl.var = as.data.frame(expl.var),
mask.out = pa.tab[, j, drop = FALSE],
mask.in = mask.in)
pa.tab[c(fact.level.cells,
sample(x = setdiff(cand.cells, fact.level.cells),
size = nb.absences - length(fact.level.cells),
replace = FALSE)), j] <- TRUE
}
return(list(xy = crds(resp.var),
sp = as.numeric(unlist(values(resp.var), use.names = FALSE)),
env = values(expl.var),
pa.tab = pa.tab))
})
## bm_PseudoAbsences SRE SpatRaster methods -----------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##'
setMethod('bm_PseudoAbsences_sre', signature(expl.var = "SpatRaster"),
function(resp.var, expl.var, sre.quant, nb.absences, nb.rep)
{
cat("\n > SRE pseudo absences selection")
# 0. calculate SRE to determine available pixels
mask.in <- bm_SRE(resp.var = resp.var, expl.var = expl.var, new.env = expl.var, quant = sre.quant)
mask.in[mask.in[] > 0] <- NA ## remove points that are in SRE
mask.in[cellFromXY(mask.in, crds(resp.var)[which(!is.na(values(resp.var)[, 1])), ])] <- NA
## mask of already sampled points (presences/absences)
mask.out <- subset(expl.var, 1)
mask.out[] <- NA
# 1. Check if NA are present in resp.var observations or not to determine which dataset to use
nb.cells <- .get_nb_available_pa_cells(mask.in, PA.flag = 0)
# 2. If nb NA < nb.absences, select all NA cells
if (nb.cells <= nb.absences) {
nb.rep <- 1
nb.absences <- nb.cells
cat("\n > All available cells have been selected (", nb.absences, "cells )")
}
# 4. For each repetition, select among raster cells
pa.tab.tmp <- matrix(NA, ncol = nb.rep, nrow = nb.absences)
for (j in 1:ncol(pa.tab.tmp)) {
SR <- NULL ## initialise the vector of sample cells
mask.in.tmp <- mask.in ## define a copy of the sampling mask
## repeat sampling until having the right number of points
while (length(SR) < nb.absences) {
SR <- c(SR, spatSample(x = mask.in.tmp,
method = "random",
size = nb.absences - length(SR),
cells = TRUE,
na.rm = TRUE)[, "cell", drop = TRUE])
mask.in.tmp[SR] <- NA ## update the mask by removing already selected cells
}
pa.tab.tmp[, j] <- SR
}
# putting cells in good format
selected.cells <- sort(unique(as.vector(pa.tab.tmp)))
pa.tab <- matrix(FALSE, ncol = nb.rep, nrow = length(selected.cells))
colnames(pa.tab) <- paste0("PA", 1:nb.rep)
for (j in 1:ncol(pa.tab)) {
pa.tab[selected.cells %in% pa.tab.tmp[,j], j] <- TRUE
}
# putting presences, true absences and pseudo absences together
xy <- rbind(crds(resp.var)[which(!is.na(values(resp.var)[, 1])), ],
xyFromCell(mask.in, selected.cells))
xy <- .add_pa_rownames(xy)
resp.var <- as.numeric(unlist(c(na.omit(values(resp.var)[, 1]),
rep(NA, length(selected.cells))),
use.names = FALSE))
expl.var <- extract(expl.var, xy)
pa.tab <- rbind(matrix(TRUE, nrow = (nrow(xy) - length(selected.cells)),
ncol = ncol(pa.tab)), pa.tab)
return(list(xy = xy,
sp = resp.var,
env = as.data.frame(expl.var),
pa.tab = as.data.frame(pa.tab)))
})
# bm_PseudoAbsences disk methods --------------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##'
setGeneric("bm_PseudoAbsences_disk",
def = function(resp.var, expl.var, ...) {
standardGeneric("bm_PseudoAbsences_disk")
})
## bm_PseudoAbsences disk SpatVector methods --------------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##'
setMethod('bm_PseudoAbsences_disk', signature(expl.var = "SpatVector"),
function(resp.var, expl.var, dist.min, dist.max, nb.absences, nb.rep) {
cat("\n > Disk pseudo absences selection")
# 1. determining area which can be selected
coor <- crds(resp.var)
pres <- which(values(resp.var)[, 1] == 1)
true.abs <- which(values(resp.var)[, 1] == 0)
tmp.abs <- which(is.na(values(resp.var)[, 1]))
outside <- rep(0, length(abs))
inside <- rep(0, length(abs))
for (i in 1:length(pres)) {
# removing points too close from presences
inside <- inside + (sqrt((coor[tmp.abs, 1] - coor[pres[i], 1]) ^ 2 +
(coor[tmp.abs, 2] - coor[pres[i], 2])^2) > dist.min)
# keeping points not to far from presences
if (!is.null(dist.max)) {
outside <- outside + (sqrt((coor[tmp.abs, 1] - coor[pres[i], 1]) ^ 2 +
(coor[tmp.abs, 2] - coor[pres[i], 2]) ^ 2) < dist.max )
}
}
if (is.null(dist.max)) { # no cells are too far
outside <- outside + 1
}
selected.abs <- tmp.abs[(inside == length(pres)) & (outside > 0)]
# 2. adding presences and true absences and selecting randomly pseudo absences
return(bm_PseudoAbsences_random(resp.var[c(pres, true.abs, selected.abs), ],
expl.var[c(pres, true.abs, selected.abs), ],
nb.absences, nb.rep))
})
## bm_PseudoAbsences disk SpatRaster methods --------------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##' @importFrom terra rast distance subset mask crds cellFromXY extract
##'
setMethod('bm_PseudoAbsences_disk', signature(expl.var = "SpatRaster"),
function(resp.var, expl.var, dist.min, dist.max, nb.absences, nb.rep)
{
cat("\n > Disk pseudo absences selection")
# 1. Check if NA are present in resp.var observations or not to determine which dataset to use
nb.cells <- .get_nb_available_pa_cells(resp.var)
if (nb.cells > 0) { # PA will be taken into response variable
expl.var.tmp <- extract(expl.var, resp.var[,-1], bind = TRUE, ID = FALSE)
return(
bm_PseudoAbsences_disk(resp.var, expl.var.tmp,
dist.min, dist.max,
nb.absences, nb.rep)
)
} else {
cat("\n > Pseudo absences are selected in explanatory variables")
# create a mask
dist.mask <- subset(expl.var, 1)
dist.mask[] <- NA
pres.xy <- crds(resp.var[which(values(resp.var)[, 1] == 1), ])
dist.mask[cellFromXY(dist.mask, pres.xy)] <- 1
dist.mask <- distance(dist.mask)
dist.mask <- mask(dist.mask, subset(expl.var, 1))
if (is.null(dist.max)) {
dist.max <- Inf
}
mask.in <- classify(dist.mask,
matrix(c(-Inf, dist.min, NA,
dist.min, dist.max, 1,
dist.max, Inf, NA),
ncol = 3, byrow = TRUE))
mask.in[cellFromXY(mask.in, pres.xy)] <- 1
mask.in = expl.var * mask.in
names(mask.in) = names(expl.var)
# 2. selecting randomly pseudo absences
return(bm_PseudoAbsences_random(resp.var, expl.var = mask.in, nb.absences, nb.rep))
}
})
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Defines functions .add_pa_rownames .get_nb_available_pa_cells .get_nb_true_abs .bm_PseudoAbsences.check.args bm_PseudoAbsences
Documented in bm_PseudoAbsences
# bm_PseudoAbsences doc ---------------------------------------------------------
##' @name bm_PseudoAbsences
##' @aliases bm_PseudoAbsences
##' @aliases bm_PseudoAbsences_random
##' @aliases bm_PseudoAbsences_sre
##' @aliases bm_PseudoAbsences_disk
##' @aliases bm_PseudoAbsences_user.defined
##' @author Wilfried Thuiller, Damien Georges
##'
##' @title Select pseudo-absences
##'
##' @description This internal \pkg{biomod2} function allows to select pseudo-absences according
##' to 4 different methods : \code{random}, \code{sre}, \code{disk} or \code{user.defined} (see Details).
##'
##'
##' @param resp.var a \code{vector}, \code{\link[sp]{SpatialPoints}} or
##' \code{\link[sp]{SpatialPointsDataFrame}} object containing binary data (\code{0} : absence,
##' \code{1} : presence, \code{NA} : indeterminate) for a single species that will be used to
##' find the pseudo-absences
##' @param expl.var a \code{matrix}, \code{data.frame}, \code{\link[sp]{SpatialPointsDataFrame}}
##' or \code{\link[terra:rast]{SpatRaster}} object containing the explanatory variables (in
##' columns or layers) that will be used to find the pseudo-absences
##' @param \ldots (\emph{optional, one or several of the following arguments depending on the selected
##' method})
##'
##' @param nb.rep an \code{integer} corresponding to the number of sets (repetitions) of
##' pseudo-absence points that will be drawn
##' @param strategy a \code{character} corresponding to the pseudo-absence selection strategy,
##' must be among \code{random}, \code{sre}, \code{disk} or \code{user.defined}
##' @param nb.absences (\emph{optional, default} \code{NULL}) \cr
##' If \code{strategy = 'random'} or \code{strategy = 'sre'} or \code{strategy = 'disk'}, an
##' \code{integer} corresponding to the number of pseudo-absence points that will be selected for
##' each pseudo-absence repetition (true absences included)
##' @param sre.quant (\emph{optional, default} \code{0}) \cr
##' If \code{strategy = 'sre'}, a \code{numeric} between \code{0} and \code{0.5} defining the
##' half-quantile used to make the \code{sre} pseudo-absence selection (see \code{\link{bm_SRE}})
##' @param dist.min (\emph{optional, default} \code{0}) \cr
##' If \code{strategy = 'disk'}, a \code{numeric} defining the minimal distance to presence points
##' used to make the \code{disk} pseudo-absence selection (in meters)
##' @param dist.max (\emph{optional, default} \code{NULL}) \cr
##' If \code{strategy = 'disk'}, a \code{numeric} defining the maximal distance to presence points
##' used to make the \code{disk} pseudo-absence selection (in meters)
##' @param user.table (\emph{optional, default} \code{NULL}) \cr
##' If \code{strategy = 'user.defined'}, a \code{matrix} or \code{data.frame} with as many rows as
##' \code{resp.var} values, as many columns as \code{nb.rep}, and containing \code{TRUE} or
##' \code{FALSE} values defining which points will be used to build the species distribution
##' model(s) for each repetition
##'
##'
##' @return
##'
##' A \code{list} containing the following elements :
##' \itemize{
##' \item{\code{xy} : }{the coordinates of the species observations}
##' \item{\code{sp} : }{the values of the species observations (\code{0}, \code{1} or \code{NA})}
##' \item{\code{env} : }{the explanatory variables}
##' \item{\code{pa.tab} : }{the corresponding table of selected pseudo-absences (indicated by
##' \code{TRUE} or \code{FALSE})}
##' }
##'
##'
##' @details
##'
##' \bold{Concerning random selection :}
##'
##' The idea is to select pseudo-absences randomly in spatial locations where the species has not
##' been sampled. This method is the simplest one and the most appropriate if lacking information
##' about the presence sampling (non-exhaustive, biased sampling, etc). \cr \cr
##'
##' \bold{Concerning SRE selection :}
##'
##' The idea is to select pseudo-absences in spatial locations whose environmental conditions are
##' different from those of the presence points. This method is appropriate when most of the
##' environmental space of the species has been sampled. \cr \cr
##'
##' \bold{Concerning disk selection :}
##'
##' The idea is to select pseudo-absences, not too close from presence points, but not too far
##' away either. This method is appropriate when most of the spatial range of the species has
##' been sampled. \cr \cr
##'
##' \bold{Concerning user defined selection :}
##'
##' The user can provide pseudo-absences locations through a table containing spatial locations
##' in rows, pseudo-absences repetitions in columns, and \code{TRUE/FALSE} values indicating
##' whether each point is to be considered as pseudo-absence or not for each dataset.
##'
##'
##' @keywords pseudo-absence random SRE disk
##'
##'
##' @seealso \code{\link{BIOMOD.formated.data.PA}}, \code{\link{BIOMOD_FormatingData}}
##' @family Secundary functions
##'
##'
##' @examples
##'
##' 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)
##' }
##'
##'
##' # --------------------------------------------------------------- #
##' # Create the different pseudo-absence datasets
##'
##' # Transform true absences into potential pseudo-absences
##' myResp.PA <- ifelse(myResp == 1, 1, NA)
##' myResp.PA.vect <- vect(cbind(myRespXY, myResp.PA), geom = c("X_WGS84","Y_WGS84"))
##'
##' # random method
##' PA.r <- bm_PseudoAbsences(resp.var = myResp.PA,
##' expl.var = myExpl,
##' nb.rep = 4,
##' nb.absences = 1000,
##' strategy = 'random')
##'
##' # disk method
##' PA.d <- bm_PseudoAbsences(resp.var = myResp.PA,
##' expl.var = myExpl,
##' nb.rep = 4,
##' nb.absences = 500,
##' strategy = 'disk',
##' dist.min = 5,
##' dist.max = 35)
##'
##' # SRE method
##' PA.s <- bm_PseudoAbsences(resp.var = myResp.PA,
##' expl.var = myExpl,
##' nb.rep = 4,
##' nb.absences = 1000,
##' strategy = 'sre',
##' sre.quant = 0.025)
##'
##' # 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
##' PA.u <- bm_PseudoAbsences(resp.var = myResp.PA,
##' expl.var = myExpl,
##' strategy = 'user.defined',
##' user.table = myPAtable)
##'
##' str(PA.r)
##' head(PA.r$pa.tab)
##' apply(PA.r$pa.tab, 2, table)
##'
##' head(PA.d$pa.tab)
##' apply(PA.d$pa.tab, 2, table)
##'
##' head(PA.s$pa.tab)
##' apply(PA.s$pa.tab, 2, table)
##'
##' tail(PA.u$pa.tab)
##' apply(PA.u$pa.tab, 2, table)
##'
##'
##' # random method : different number of PA
##' PA.r_mult <- bm_PseudoAbsences(resp.var = myResp.PA.vect,
##' expl.var = myExpl,
##' nb.rep = 4,
##' nb.absences = c(1000, 500, 500, 200),
##' strategy = 'random')
##'
##' str(PA.r_mult)
##' head(PA.r_mult$pa.tab)
##' apply(PA.r_mult$pa.tab, 2, table)
##'
##'
##' @importFrom terra rast vect freq spatSample values extract
##' @importFrom utils packageVersion
##'
##' @export
##'
##'
## --------------------------------------------------------------------------- #
bm_PseudoAbsences <- function(resp.var, expl.var, nb.rep = 1, strategy = 'random', nb.absences = NULL
, sre.quant = 0, dist.min = 0, dist.max = NULL, user.table = NULL)
{
## 0. Check arguments ---------------------------------------------------------------------------
args <- .bm_PseudoAbsences.check.args(resp.var, expl.var, nb.rep, strategy, nb.absences
, sre.quant, dist.min, dist.max, user.table)
for (argi in names(args)) { assign(x = argi, value = args[[argi]]) }
rm(args)
## 1. Create output object ----------------------------------------------------------------------
if ((nb.rep == 0 || any(nb.absences <= 0)) & strategy != 'user.defined') {
out <- NULL
} else {
if (length(nb.absences) == 1) {
out <- switch(strategy,
user.defined = bm_PseudoAbsences_user.defined(resp.var, expl.var, user.table),
random = bm_PseudoAbsences_random(resp.var, expl.var, nb.absences, nb.rep),
sre = bm_PseudoAbsences_sre(resp.var, expl.var, sre.quant, nb.absences, nb.rep),
disk = bm_PseudoAbsences_disk(resp.var, expl.var, dist.min, dist.max, nb.absences, nb.rep))
} else if (length(nb.absences) == nb.rep) {
out.list = foreach(i.abs = unique(nb.absences)) %do%
{
i.rep = which(nb.absences == i.abs)
cat("\n > Set ", paste0(i.rep, collapse = ", "), " (", i.abs, " pseudo absences wanted)", sep = "")
out <- switch(strategy,
user.defined = bm_PseudoAbsences_user.defined(resp.var, expl.var, user.table),
random = bm_PseudoAbsences_random(resp.var, expl.var, i.abs, length(i.rep)),
sre = bm_PseudoAbsences_sre(resp.var, expl.var, sre.quant, i.abs, length(i.rep)),
disk = bm_PseudoAbsences_disk(resp.var, expl.var, dist.min, dist.max, i.abs, length(i.rep)))
## CASE where all available cells have been selected :
## give back only one dataset, even if several were asked
if (ncol(out$pa.tab) == 1 && length(i.rep) > 1) {
col1 <- out$pa.tab
for (j in 2:length(i.rep)) {
out$pa.tab <- cbind(out$pa.tab, col1)
}
# i.rep = i.rep[1] ## NOT working with summary and plot functions
}
colnames(out$pa.tab) = paste0("PA", i.rep)
return(out)
}
## GET XY -----------------------------------------------------
## Get coordinates of presences
ind.pres = which(out.list[[1]][["sp"]] == 1)
xy.pres = out.list[[1]][["xy"]][ind.pres, ]
nb.pres = length(ind.pres)
## Get coordinates of pseudo-absences
out.xy = foreach(i = 1:length(out.list)) %do%
{
ind.keep = 1:nrow(out.list[[i]][["xy"]])
ind.keep = ind.keep[-which(ind.keep %in% ind.pres)]
res = out.list[[i]][["xy"]][ind.keep, ]
res = cbind(res, ind.keep)
return(res)
}
## Merge all coordinates of pseudo-absences (may be duplicates)
out.xy = Reduce(function(x, y) merge(x, y, by = c("x", "y"), all = TRUE), out.xy)
## Get indexes of merged PA coordinates for each set
## To be used to rebuild env and pa.tab
out.index = out.xy[, -which(colnames(out.xy) %in% c("x", "y"))]
out.order = !is.na(out.index)
out.order = t(apply(out.order, 1, cumsum))
## Keep presences + pseudo-absences coordinates
out.xy = out.xy[, c("x", "y")]
out.xy = rbind(xy.pres, out.xy)
out.sp = c(rep(1, nb.pres), rep(NA, nrow(out.xy) - nb.pres))
## GET ENV & PA.TAB -------------------------------------------
## Initialize env matrix
out.env = matrix(NA, nrow = nrow(out.xy), ncol = ncol(out.list[[1]][["env"]]))
out.env = as.data.frame(out.env)
colnames(out.env) = colnames(out.list[[1]][["env"]])
out.env[1:nb.pres, ] = out.list[[1]][["env"]][1:nb.pres, ]
## Initialize pa.tab matrix
out.pa.tab = matrix(NA, nrow = nrow(out.xy), ncol = nb.rep)
out.pa.tab = as.data.frame(out.pa.tab)
colnames(out.pa.tab) = paste0("PA", 1:nb.rep)
out.pa.tab[1:nb.pres, ] = TRUE
ind.start = 1
ind.end = ncol(out.list[[1]][["pa.tab"]])
## Fill first column
ind = which(out.order[, 1] == 1)
out.env[ind + nb.pres, ] = out.list[[1]][["env"]][out.index[ind, 1], ]
out.pa.tab[ind + nb.pres, ind.start:ind.end] = out.list[[1]][["pa.tab"]][out.index[ind, 1], ]
## Fill all other columns
for (j in 2:ncol(out.order)) {
ind = which(out.order[, j] != out.order[, j-1])
if (length(ind) > 0) {
## For env
out.env[ind + nb.pres, ] = out.list[[j]][["env"]][out.index[ind, j], ]
## For pa.tab
ind.start = ind.end + 1
ind.end = ind.start + ncol(out.list[[j]][["pa.tab"]]) - 1
out.pa.tab[ind + nb.pres, ind.start:ind.end] = out.list[[j]][["pa.tab"]][out.index[ind, j], ]
}
}
## GET everything ---------------------------------------------
out = list(xy = out.xy,
sp = out.sp,
env = out.env,
pa.tab = out.pa.tab)
}
}
cat("\n")
return(out)
}
# Argument Check --------------------------------------------------------------
.bm_PseudoAbsences.check.args <- function(resp.var, expl.var, nb.rep, strategy, nb.absences, sre.quant, dist.min, dist.max, user.table)
{
cat('\n\nChecking Pseudo-absence selection arguments...\n')
## 1. Check resp.var argument -----------------------------------------------
if (is.vector(resp.var)) {
resp.var <- vect(data.frame(x = 0,
y = 0,
resp = resp.var),
geom = c("x","y"))
if (!is.null(nb.absences) && length(nb.absences) > 1) {
stop("Selection of multiple number of pseudo-absences depends on coordinates. Please provide some.")
}
}
.fun_testIfInherits(TRUE, "resp.var", resp.var, "SpatVector")
## 2. Check expl.var argument -----------------------------------------------
if (inherits(expl.var, c("matrix","data.frame"))) {
if (nrow(expl.var) != nrow(resp.var)) {
stop("Species and Explanatory must have same dimensions")
}
# transform expl.var into SpatVector
rownames(expl.var) <- NULL
expl.var <- vect(
cbind(data.frame(x = crds(resp.var)[,1],
y = crds(resp.var)[,2]),
as.data.frame(expl.var)
),
geom = c("x","y")
)
}
.fun_testIfInherits(TRUE, "expl.var", expl.var, c("SpatVector", "SpatRaster"))
args <- .BIOMOD.formated.data.check.args(sp = resp.var, env = expl.var)
for (argi in names(args)) { assign(x = argi, value = args[[argi]]) }
rm(args)
## 3. Check strategy argument -----------------------------------------------
availableStrategies <- c("random", "sre", "disk", "user.defined")
if (is.null(strategy) || !(strategy %in% availableStrategies) ||
(strategy != 'user.defined' && all(crds(resp.var) == 0))) {
# no coordinates or unknown strategy
strategy <- "random"
cat("\n ! Random strategy was automatically selected (that can be due to points coordinates lack or unavailable strategy choosen)")
}
## 4. Check sre.quant argument ----------------------------------------------
if (strategy == 'SRE' && (sre.quant >= 0.5 || sre.quant < 0)) {
stop("\n ! SRE Quant should be a value between 0 and 0.5 ")
}
## 5. Check dist.min and dist.max arguments ---------------------------------
if (strategy == 'disk') {
if (!is.null(dist.min) && dist.min < 0) {
dist.min <- 0
}
if (!is.null(dist.max) && dist.max < 0) {
dist.max <- NULL
}
if (!is.null(dist.max) && !is.null(dist.min) && dist.min >= dist.max) {
stop("dist.min >= dist.max")
}
}
## 6. Check nb.absences argument --------------------------------------------
if (strategy != "user.defined") {
if (is.null(nb.absences)) {
stop("You must give the number of pseudo absences you want")
} else {
if (length(nb.absences) > 1) {
if (length(nb.absences) != nb.rep) {
stop("You must give one value for pseudo absences, or as many as the number of repetitions")
} else if (length(unique(nb.absences)) == 1) {
nb.absences = unique(nb.absences)
}
}
nbTrueAbs <- .get_nb_true_abs(resp.var)
if (nbTrueAbs) {
stop("Your dataset contains true absences. This should not be mixed with pseudo absences selection")
}
}
}
## 7. Check user.table argument --------------------------------------------
if (strategy == "user.defined") {
if (is.null(user.table)) {
stop("You must give a table defining the pseudo absences you want")
} else {
if (!(is.matrix(user.table) | is.data.frame(user.table))) {
stop("\n PA.user.table must be a matrix or a data.frame")
}
if (nrow(user.table) != length(resp.var)) {
stop("\n PA.user.table must have as many row than the number of observation of your response variable")
}
colnames(user.table) <- paste0("PA", 1:ncol(user.table))
nb.absences <- nrow(user.table)
}
}
return(list(resp.var = resp.var,
expl.var = expl.var,
nb.rep = nb.rep,
strategy = strategy,
nb.absences = nb.absences,
sre.quant = sre.quant,
dist.min = dist.min,
dist.max = dist.max,
user.table = user.table))
}
# Additionnal tools ------------------------------------------------------------
.get_nb_true_abs <- function(sp)
{
if (is.vector(sp)) {
return(length(which(sp == 0)))
} else if (inherits(sp, 'SpatVector')) {
return(length(which(values(sp)[,1] == 0)))
} else if (inherits(sp, 'SpatRaster')) {
return(freq(sp, value = 0)$count)
}
}
.get_nb_available_pa_cells <- function(data, PA.flag = NA)
{
if (is.vector(data) || inherits(data, c("matrix","data.frame"))) {
return(
ifelse(is.na(PA.flag),
length(which(is.na(data))),
length(which(data == PA.flag)))
)
} else if (inherits(data, 'SpatVector')) {
return(
ifelse(is.na(PA.flag),
length(which(is.na(as.data.frame(data)[,1]))),
length(which(values(data)[,1] == PA.flag)))
)
} else if (inherits(data, 'SpatRaster')) {
return(
freq(data, value = PA.flag)$count
)
}
}
.add_pa_rownames <- function(xy)
{
rn <- row.names(xy)
# if (length(rn) == 0) {
# rn = rep("", nrow(xy))
# }
missing_rn <- which(rn == "")
if (length(missing_rn) > 0) {
rn[missing_rn] <- paste0("pa", 1:length(missing_rn))
}
rownames(xy) <- rn
return(xy)
}
# bm_PseudoAbsences user-defined methods --------------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##'
setGeneric("bm_PseudoAbsences_user.defined",
def = function(resp.var, expl.var, ...) {
standardGeneric( "bm_PseudoAbsences_user.defined")
})
## bm_PseudoAbsences user-defined SpatVector methods --------------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##'
setMethod('bm_PseudoAbsences_user.defined', signature(expl.var = "SpatVector"),
function(resp.var, expl.var, user.table) {
cat("\n > User defined pseudo absences selection")
return(list(xy = crds(resp.var),
sp = as.numeric(unlist(values(resp.var), use.names = FALSE)),
env = as.data.frame(expl.var),
pa.tab = user.table))
})
## bm_PseudoAbsences user-defined SpatRaster methods --------------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##'
setMethod('bm_PseudoAbsences_user.defined', signature(expl.var = "SpatRaster"),
function(resp.var, expl.var, user.table) {
cat("\n > User defined pseudo absences selection")
expl.var <- extract(expl.var, resp.var, ID = FALSE)
return(list(xy = crds(resp.var),
sp = as.numeric(unlist(values(resp.var), use.names = FALSE)),
env = expl.var,
pa.tab = user.table))
})
# bm_PseudoAbsences random methods --------------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##'
setGeneric("bm_PseudoAbsences_random",
def = function(resp.var, expl.var, ...) {
standardGeneric( "bm_PseudoAbsences_random")
})
## bm_PseudoAbsences random SPDF methods --------------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##'
setMethod('bm_PseudoAbsences_random', signature(expl.var = "SpatVector"),
function(resp.var, expl.var, nb.absences, nb.rep)
{
cat("\n > random pseudo absences selection")
# 1. Check if NA are present in resp.var observations or not to determine which dataset to use
nb.cells <- .get_nb_available_pa_cells(resp.var)
if (nb.cells > 0) { # PA will be taken into response variable
# 2. If nb NA < nb.absences, select all NA cells
if (nb.cells <= nb.absences) {
nb.rep <- 1
nb.absences <- nb.cells
cat("\n > All available cells have been selected (", nb.absences, "cells )")
}
# 3. Select always the presences and the true absences
pa.tab <- matrix(FALSE, ncol = nb.rep, nrow = nrow(resp.var))
colnames(pa.tab) <- paste0("PA", 1:nb.rep)
pa.tab[c(which(values(resp.var)[, 1] == 1),
which(values(resp.var)[, 1] == 0)),] <- TRUE
# 4. For each repetition, select among NA cells
cand.cells <- which(is.na(values(resp.var)[, 1]))
for (j in 1:ncol(pa.tab)) {
## force to get at least one value of each factorial variable
fact.level.cells <- bm_SampleFactorLevels(expl.var = as.data.frame(expl.var),
mask.out = pa.tab[, j, drop = FALSE])
if (length(fact.level.cells) > 0) {
pa.tab[fact.level.cells, j] <- TRUE
cand.cells <- setdiff(cand.cells, fact.level.cells)
}
pa.tab[sample(x = cand.cells,
size = nb.absences - length(fact.level.cells),
replace = FALSE), j] <- TRUE
}
return(list(xy = crds(resp.var),
sp = as.numeric(unlist(values(resp.var), use.names = FALSE)),
env = as.data.frame(expl.var),
pa.tab = pa.tab))
} else {
cat("\nUnsupported case yet!")
return(NULL)
}
})
## bm_PseudoAbsences random SpatRaster methods --------------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##' @importFrom terra rast xyFromCell cellFromXY classify crds extract subset
##'
setMethod('bm_PseudoAbsences_random', signature(expl.var = "SpatRaster"),
function(resp.var, expl.var, nb.absences, nb.rep)
{
cat("\n > random pseudo absences selection")
# 1. Check if NA are present in resp.var observations or not to determine which dataset to use
nb.cells <- .get_nb_available_pa_cells(resp.var)
if (nb.cells > 0) { # PA will be taken into response variable
# 2. If nb NA < nb.absences, select all NA cells
if (nb.cells <= nb.absences) {
nb.rep <- 1
nb.absences <- nb.cells
cat("\n > All available cells have been selected (", nb.absences, "cells )")
}
# 3. Select always the presences and the true absences
pa.tab <- matrix(FALSE, ncol = nb.rep, nrow = nrow(resp.var))
colnames(pa.tab) <- paste0("PA", 1:nb.rep)
pa.tab[c(which(values(resp.var)[, 1] == 1),
which(values(resp.var)[, 1] == 0)),] <- TRUE
# 4. For each repetition, select among NA cells
cand.cells <- which(is.na(values(resp.var)[, 1]))
for (j in 1:ncol(pa.tab)) {
pa.tab[sample(x = cand.cells, size = nb.absences, replace = FALSE), j] <- TRUE
}
expl.var <- extract(expl.var, resp.var, ID = FALSE)
return(list(xy = crds(resp.var),
sp = as.numeric(unlist(values(resp.var), use.names = FALSE)),
env = as.data.frame(expl.var),
pa.tab = as.data.frame(pa.tab)))
} else {
cat("\n > Pseudo absences are selected in explanatory variables")
# create a mask containing all not already sampled points (presences and absences)
## the area we want to sample
mask.out <- mask.env <- .get_data_mask(expl.var, value.out = -1)
# add presences and true absences in our mask
in.cells <- cellFromXY(mask.env, crds(resp.var))
mask.env[in.cells] <- NA
mask.out[in.cells] <- 1
# checking of nb candidates
nb.cells <- .get_nb_available_pa_cells(mask.env, PA.flag = -1)
# 2. If nb NA < nb.absences, select all NA cells
if (nb.cells <= nb.absences) {
nb.rep <- 1
nb.absences <- nb.cells
cat("\n > All available cells have been selected (", nb.absences, "cells )")
}
if (nb.absences == 0) {
cat("\n > No cells are available (0 pseudo absences selected )")
return(NULL)
} else {
# 4. For each repetition, select among raster cells
pa.tab.tmp <- matrix(NA, ncol = nb.rep, nrow = nb.absences)
for (j in 1:ncol(pa.tab.tmp)) {
SR <- NULL ## initialise the vector of sample cells
mask.env.tmp <- mask.env ## define a copy of the sampling mask
## force to get at least one value of each factorial variable
fact.level.cells <- bm_SampleFactorLevels(expl.var = expl.var, mask.out = mask.out)
if (length(fact.level.cells) > 0) {
SR <- c(SR, fact.level.cells)
mask.env.tmp[SR] <- NA ## update the mask by removing already selected cells
}
## repeat sampling until having the right number of points
## spatSample with na.rm = TRUE may return less points than asked
while(length(SR) < nb.absences){
SR <- c(SR, spatSample(x = mask.env.tmp,
method = "random",
size = nb.absences - length(SR),
cells = TRUE,
na.rm = TRUE)[, "cell", drop = TRUE])
mask.env.tmp[SR] <- NA ## update the mask by removing already selected cells
}
pa.tab.tmp[, j] <- SR
}
# putting cells in good format
selected.cells <- sort(unique(as.vector(pa.tab.tmp)))
pa.tab <- matrix(FALSE, ncol = nb.rep, nrow = length(selected.cells))
colnames(pa.tab) <- paste0("PA", 1:nb.rep)
for (j in 1:ncol(pa.tab)) {
pa.tab[selected.cells %in% pa.tab.tmp[,j], j] <- TRUE
}
# putting presences, true absences and pseudo absences together
xy = crds(resp.var)
rownames(xy) = paste0("pres", 1:nrow(xy))
xy <- rbind(xy, xyFromCell(mask.env, selected.cells))
xy <- .add_pa_rownames(xy)
resp.var <- as.numeric(unlist(c(values(resp.var)[, 1],
rep(NA, length(selected.cells))),
use.names = FALSE))
expl.var <- extract(expl.var, xy)
pa.tab <- rbind(matrix(TRUE,
nrow = (nrow(xy) - length(selected.cells)),
ncol = ncol(pa.tab)),
pa.tab)
return(list(xy = xy,
sp = resp.var,
env = as.data.frame(expl.var),
pa.tab = as.data.frame(pa.tab)))
}
}
})
# bm_PseudoAbsences SRE methods --------------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##'
setGeneric("bm_PseudoAbsences_sre",
def = function(resp.var, expl.var, ...) {
standardGeneric("bm_PseudoAbsences_sre")
})
## bm_PseudoAbsences SRE SpatVector methods ----------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##'
setMethod('bm_PseudoAbsences_sre', signature(expl.var = "SpatVector"),
function(resp.var, expl.var, sre.quant, nb.absences, nb.rep)
{
cat("\n > SRE pseudo absences selection")
# 0. calculate SRE to determine available pixels
mask.in <- bm_SRE(resp.var = resp.var, expl.var = expl.var, new.env = values(expl.var), quant = sre.quant)
mask.in <- data.frame(mask.in = !as.logical(mask.in)) ## revert the mask to sample PA out of SRE
mask.in$mask.in[c(which(values(resp.var)[, 1] == 1)
, which(values(resp.var)[, 1] == 0))] <- FALSE
# 1. Check if NA are present in resp.var observations or not to determine which dataset to use
nb.cells <- .get_nb_available_pa_cells(mask.in$mask.in, PA.flag = TRUE)
# 2. If nb NA < nb.absences, select all NA cells
if (nb.cells <= nb.absences) {
nb.rep <- 1
nb.absences <- nb.cells
cat("\n > All available cells have been selected (", nb.absences, "cells )")
}
# 3. Select always the presences and the true absences
pa.tab <- matrix(FALSE, ncol = nb.rep, nrow = nrow(resp.var))
colnames(pa.tab) <- paste0("PA", 1:nb.rep)
pa.tab[c(which(values(resp.var)[, 1] == 1),
which(values(resp.var)[, 1] == 0)),] <- TRUE
# 4. For each repetition, select among NA cells
cand.cells <- which(mask.in$mask.in == TRUE)
for (j in 1:ncol(pa.tab)) {
## force to get at least one value of each factorial variable
fact.level.cells <- bm_SampleFactorLevels(expl.var = as.data.frame(expl.var),
mask.out = pa.tab[, j, drop = FALSE],
mask.in = mask.in)
pa.tab[c(fact.level.cells,
sample(x = setdiff(cand.cells, fact.level.cells),
size = nb.absences - length(fact.level.cells),
replace = FALSE)), j] <- TRUE
}
return(list(xy = crds(resp.var),
sp = as.numeric(unlist(values(resp.var), use.names = FALSE)),
env = values(expl.var),
pa.tab = pa.tab))
})
## bm_PseudoAbsences SRE SpatRaster methods -----------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##'
setMethod('bm_PseudoAbsences_sre', signature(expl.var = "SpatRaster"),
function(resp.var, expl.var, sre.quant, nb.absences, nb.rep)
{
cat("\n > SRE pseudo absences selection")
# 0. calculate SRE to determine available pixels
mask.in <- bm_SRE(resp.var = resp.var, expl.var = expl.var, new.env = expl.var, quant = sre.quant)
mask.in[mask.in[] > 0] <- NA ## remove points that are in SRE
mask.in[cellFromXY(mask.in, crds(resp.var)[which(!is.na(values(resp.var)[, 1])), ])] <- NA
## mask of already sampled points (presences/absences)
mask.out <- subset(expl.var, 1)
mask.out[] <- NA
# 1. Check if NA are present in resp.var observations or not to determine which dataset to use
nb.cells <- .get_nb_available_pa_cells(mask.in, PA.flag = 0)
# 2. If nb NA < nb.absences, select all NA cells
if (nb.cells <= nb.absences) {
nb.rep <- 1
nb.absences <- nb.cells
cat("\n > All available cells have been selected (", nb.absences, "cells )")
}
# 4. For each repetition, select among raster cells
pa.tab.tmp <- matrix(NA, ncol = nb.rep, nrow = nb.absences)
for (j in 1:ncol(pa.tab.tmp)) {
SR <- NULL ## initialise the vector of sample cells
mask.in.tmp <- mask.in ## define a copy of the sampling mask
## repeat sampling until having the right number of points
while (length(SR) < nb.absences) {
SR <- c(SR, spatSample(x = mask.in.tmp,
method = "random",
size = nb.absences - length(SR),
cells = TRUE,
na.rm = TRUE)[, "cell", drop = TRUE])
mask.in.tmp[SR] <- NA ## update the mask by removing already selected cells
}
pa.tab.tmp[, j] <- SR
}
# putting cells in good format
selected.cells <- sort(unique(as.vector(pa.tab.tmp)))
pa.tab <- matrix(FALSE, ncol = nb.rep, nrow = length(selected.cells))
colnames(pa.tab) <- paste0("PA", 1:nb.rep)
for (j in 1:ncol(pa.tab)) {
pa.tab[selected.cells %in% pa.tab.tmp[,j], j] <- TRUE
}
# putting presences, true absences and pseudo absences together
xy <- rbind(crds(resp.var)[which(!is.na(values(resp.var)[, 1])), ],
xyFromCell(mask.in, selected.cells))
xy <- .add_pa_rownames(xy)
resp.var <- as.numeric(unlist(c(na.omit(values(resp.var)[, 1]),
rep(NA, length(selected.cells))),
use.names = FALSE))
expl.var <- extract(expl.var, xy)
pa.tab <- rbind(matrix(TRUE, nrow = (nrow(xy) - length(selected.cells)),
ncol = ncol(pa.tab)), pa.tab)
return(list(xy = xy,
sp = resp.var,
env = as.data.frame(expl.var),
pa.tab = as.data.frame(pa.tab)))
})
# bm_PseudoAbsences disk methods --------------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##'
setGeneric("bm_PseudoAbsences_disk",
def = function(resp.var, expl.var, ...) {
standardGeneric("bm_PseudoAbsences_disk")
})
## bm_PseudoAbsences disk SpatVector methods --------------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##'
setMethod('bm_PseudoAbsences_disk', signature(expl.var = "SpatVector"),
function(resp.var, expl.var, dist.min, dist.max, nb.absences, nb.rep) {
cat("\n > Disk pseudo absences selection")
# 1. determining area which can be selected
coor <- crds(resp.var)
pres <- which(values(resp.var)[, 1] == 1)
true.abs <- which(values(resp.var)[, 1] == 0)
tmp.abs <- which(is.na(values(resp.var)[, 1]))
outside <- rep(0, length(abs))
inside <- rep(0, length(abs))
for (i in 1:length(pres)) {
# removing points too close from presences
inside <- inside + (sqrt((coor[tmp.abs, 1] - coor[pres[i], 1]) ^ 2 +
(coor[tmp.abs, 2] - coor[pres[i], 2])^2) > dist.min)
# keeping points not to far from presences
if (!is.null(dist.max)) {
outside <- outside + (sqrt((coor[tmp.abs, 1] - coor[pres[i], 1]) ^ 2 +
(coor[tmp.abs, 2] - coor[pres[i], 2]) ^ 2) < dist.max )
}
}
if (is.null(dist.max)) { # no cells are too far
outside <- outside + 1
}
selected.abs <- tmp.abs[(inside == length(pres)) & (outside > 0)]
# 2. adding presences and true absences and selecting randomly pseudo absences
return(bm_PseudoAbsences_random(resp.var[c(pres, true.abs, selected.abs), ],
expl.var[c(pres, true.abs, selected.abs), ],
nb.absences, nb.rep))
})
## bm_PseudoAbsences disk SpatRaster methods --------------------------------------
##'
##' @rdname bm_PseudoAbsences
##' @export
##' @importFrom terra rast distance subset mask crds cellFromXY extract
##'
setMethod('bm_PseudoAbsences_disk', signature(expl.var = "SpatRaster"),
function(resp.var, expl.var, dist.min, dist.max, nb.absences, nb.rep)
{
cat("\n > Disk pseudo absences selection")
# 1. Check if NA are present in resp.var observations or not to determine which dataset to use
nb.cells <- .get_nb_available_pa_cells(resp.var)
if (nb.cells > 0) { # PA will be taken into response variable
expl.var.tmp <- extract(expl.var, resp.var[,-1], bind = TRUE, ID = FALSE)
return(
bm_PseudoAbsences_disk(resp.var, expl.var.tmp,
dist.min, dist.max,
nb.absences, nb.rep)
)
} else {
cat("\n > Pseudo absences are selected in explanatory variables")
# create a mask
dist.mask <- subset(expl.var, 1)
dist.mask[] <- NA
pres.xy <- crds(resp.var[which(values(resp.var)[, 1] == 1), ])
dist.mask[cellFromXY(dist.mask, pres.xy)] <- 1
dist.mask <- distance(dist.mask)
dist.mask <- mask(dist.mask, subset(expl.var, 1))
if (is.null(dist.max)) {
dist.max <- Inf
}
mask.in <- classify(dist.mask,
matrix(c(-Inf, dist.min, NA,
dist.min, dist.max, 1,
dist.max, Inf, NA),
ncol = 3, byrow = TRUE))
mask.in[cellFromXY(mask.in, pres.xy)] <- 1
mask.in = expl.var * mask.in
names(mask.in) = names(expl.var)
# 2. selecting randomly pseudo absences
return(bm_PseudoAbsences_random(resp.var, expl.var = mask.in, nb.absences, nb.rep))
}
})
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