randPointsBatch: Iterate "randPointsRespecting..." functions.

In adamlilith/enmSdm: Tools for Modeling Niches and Distributions of Species

Iterate "randPointsRespecting..." functions.

Description

This function is a wrapper function for any of the randPointsRespecting~ functions. It is useful for calling the function multiple times using the same arguments. The output is a list with one element per call.

Usage

randPointsBatch(
  randFunct,
  iterations = 100,
  ...,
  rast,
  distFunct = NULL,
  keepData = FALSE,
  verbose = TRUE,
  verboseEach = FALSE
)

Arguments

randFunct	Character, any of: 'randPointsRespectingSelf', 'randPointsRespectingSelfOther1', or 'randPointsRespectingSelfOther2'.
iterations	Positive integer, number of times to call the function.
...	Arguments to pass to randPointsRespectingSelf, randPointsRespectingSelfOther1, or randPointsRespectingSelfOther2.
rast	Raster, RasterStack, or RasterBrick used to locate presences randomly. If this is a RasterStack or a RasterBrick then the first layer will be used (i.e., so cells with NA will not have points located within them).
distFunct	Either a function or NULL. If NULL then distGeo is used to calculate distances. Other "dist" functions (e.g., distGeo) can be used. Alternatively, a custom function can be used so long as its first argument is a 2-column numeric matrix with one row for the x- and y-coordinates of a single point and its second argument is a two-column numeric matrix with one or more rows of other points.
keepData	Logical, if TRUE then the original data in x (i.e., columns that do not represent coordinates) will be retained in the output but the coordinates will be shuffled. If FALSE (default) then the returned value will have just shuffled coordinates.
verbose	Logical, if TRUE (default) show progress along iterations.
verboseEach	Logical, if FALSE (default), then suppress progress indicators for each call of the randPointsRespecting~ function.

Details

Note that if you use the randPointsRespectingSelfOther2 function and intend to summarize across iterations using subsequent functions (e.g., randPointsBatchSampled), it is highly advisable to ensure that the x1 and x2 arguments to that function have the same class (matrix, data frame, SpatialPoints, or SpatialPointsDataFrame). You should also either set the argument keepData to FALSE or ensure that the column names of x1 and x2 are exactly the same (which then allows you to use keepData = TRUE).

Value

A list with iterations elements.

See Also

randPointsRespectingSelf, randPointsRespectingSelfOther1, randPointsRespectingSelfOther2, randPointsBatchSampled, randPointsBatchExtract, randPointsBatchNicheOverlap

Examples

library(dismo)
library(raster)

data(lemurs, package='enmSdm')
longLat <- c('decimalLongitude', 'decimalLatitude')

mad <- raster::getData('GADM', country='MDG', level=0)
elev <- raster::getData('alt', country='MDG', mask=TRUE, res=2.5)

# plot data as-is
plot(mad)
species <- sort(unique(lemurs$species))

for (i in seq_along(species)) {

	thisLemur <- lemurs[lemurs$species == species[i], longLat]
	points(thisLemur, pch=i, col=i)

}

legend('bottomleft', legend=species, pch=seq_along(species), col=seq_along(species))

# geographically thin presences of each species
thinLemurs <- data.frame()

for (i in seq_along(species)) {

	thisLemur <- lemurs[lemurs$species == species[i], ]
	thinned <- geoThin(thisLemur, minDist=10000, longLat=longLat)
	thinLemurs <- rbind(thinLemurs, thinned)

}

# plot geographically thinned data
plot(mad)

for (i in seq_along(species)) {

	thisLemur <- thinLemurs[thinLemurs$species == species[i], longLat]
	points(thisLemur, pch=i, col=i)

}

legend('bottomleft', legend=species, pch=seq_along(species), col=seq_along(species))

# randomize one species with respect to itself
x <- thinLemurs[thinLemurs$species == 'Eulemur fulvus', longLat]

set.seed(123)
x1rand <- randPointsRespectingSelf(x=x, rast=elev, tol=24000, verbose=TRUE)

# plot observed and randomized occurrences
plot(mad)
points(x, pch=16)
points(x1rand, col='red')

# randomize two species with respect to selves and others
species1 <- species[1]
species2 <- species[3]

x1 <- thinLemurs[thinLemurs$species == species1, longLat]
x2 <- thinLemurs[thinLemurs$species == species2, longLat]

set.seed(123)
tol1 <- tol2 <- tol12 <- 16000
x12rand <- randPointsRespectingSelfOther2(x1=x1, x2=x2, rast=elev,
	tol1=tol1, tol2=tol2, tol12=tol12, verbose=TRUE)

# plot geographically thinned data
plot(mad)
points(x1, pch=21, bg='cornflowerblue')
points(x2, pch=24, bg='cornflowerblue')
points(x12rand$x1rand, pch=1, col='red')
points(x12rand$x2rand, pch=2, col='red')

legend('bottomleft', legend=c(species1, species2,
	legend=paste('rand', species1), paste('rand', species2)),
	pch=c(21, 24, 1, 2), col=c('black', 'black', 'red', 'red'),
	pt.bg=c('cornflowerblue', 'cornflowerblue', NA, NA))

### batch mode
## Not run: 

# download climate data
clim <- raster::getData('worldclim', var='bio', res=2.5)

# lemur data
data(lemurs, package='enmSdm')
longLat <- c('decimalLongitude', 'decimalLatitude')

# geographically thin presences of each species
thinLemurs <- data.frame()

for (i in seq_along(species)) {

	thisLemur <- lemurs[lemurs$species == species[i], ]
	thinned <- geoThin(thisLemur, minDist=10000, longLat=longLat)
	thinLemurs <- rbind(thinLemurs, thinned)

}

# randomize two species with respect to selves and others
species1 <- species[1]
species2 <- species[3]

x1 <- thinLemurs[thinLemurs$species == species1, longLat]
x2 <- thinLemurs[thinLemurs$species == species2, longLat]

# create null distributions
set.seed(123)
tol1 <- tol2 <- tol12 <- 24000
iterations <- 100 # for analysis set this to 100 or more
# for testing use a small number!

x12rand <- randPointsBatch('randPointsRespectingSelfOther2', x1=x1, x2=x2,
	rast=clim[[1]], tol1=tol1, tol2=tol2, tol12=tol12, iterations=iterations,
	verbose=TRUE)

# get environment that was sampled to use as background
bg <- randPointsBatchSampled(x12rand)
bgEnv <- raster::extract(clim, bg)

# create PCA of environmental space
vars <- paste0('bio', 1:19)
bgPca <- princomp(bgEnv[ , vars], cor=TRUE)

x1env <- raster::extract(clim, x1)
x2env <- raster::extract(clim, x2)

nas1 <- omnibus::naRows(x1env)
nas2 <- omnibus::naRows(x2env)

if (length(nas1) > 0) x1env <- x1env[-nas1, ]
if (length(nas2) > 0) x2env <- x2env[-nas2, ]

# observed niche overlap
obsOverlap <- enmSdm::nicheOverlap(
	x1=x1env,
	x2=x2env,
	env=bgPca,
	vars=vars,
	bins=100,
	cor=TRUE
)

# extract climate at randomized sites
x12rand <- randPointsBatchExtract(x12rand, clim, verbose=TRUE)

# null niche overlap
nullOverlap <- randPointsBatchNicheOverlap(
	rands=x12rand,
	env=bgPca,
	vars=vars,
	bins=100,
	cor=TRUE
)

hist(nullOverlap$d, 20, main='Niche Overlap',
	xlab='Schoener\'s D', xlim=c(0, 1))
abline(v=obsOverlap[['d']], col='blue', lwd=3)
legend('topright', legend='Observed', lwd=3, col='blue')

## End(Not run)

adamlilith/enmSdm documentation built on Jan. 6, 2023, 11 a.m.