## ----sdm15a-------------------------------------------------------------------
library(predicts)
# get the predictors filename  
f1 <- system.file("ex/bio.tif", package="predicts")
f2 <- system.file("ex/biome.tif", package="predicts")

r <- rast(c(f1, f2))

# select 5000 random points
# set seed to assure that the examples will always
# have the same random sample.
set.seed(1963)
bg <- spatSample(r, 5000, "random", na.rm=TRUE, as.points=TRUE)


## ----sdm15, fig.width=9, fig.height=6-----------------------------------------
plot(r, 1)
points(bg, cex=0.5)


## -----------------------------------------------------------------------------
e <- ext(-80, -53, -39, -22)
bg2 <- spatSample(r, 500, "random", na.rm=TRUE, as.points=TRUE, ext=e)
plot(r, 1)
lines(e, col="red", lwd=2)
points(bg2, cex=0.5)


## ----sdm16a-------------------------------------------------------------------
acfile <- file.path(system.file(package="predicts"), "ex/acaule.rds")
ac <- readRDS(acfile)


## ----sdm17--------------------------------------------------------------------
# circles with a radius of 50 km
x <- buffer(ac, 50000)
pol <- aggregate(x)


## ----sdm19A-------------------------------------------------------------------
# sample randomly from all circles
set.seed(999)
samp1 <- spatSample(pol, 500, "random")


## ----sdm19B-------------------------------------------------------------------
pcells <- cells(r, samp1)
# remote duplicates
pcells <- unique(pcells[,2])
# back to coordinates
xy <- xyFromCell(r, pcells)


## ----sdm20, fig.width=9, fig.height=6-----------------------------------------
plot(pol, axes=TRUE)
points(samp1, pch="+", cex=.5)
points(xy, cex=0.75, pch=20, col='blue')


## ----sdm21a-------------------------------------------------------------------
spxy <- vect(xy, crs="+proj=longlat +datum=WGS84")
xyInside <- intersect(spxy, x)


## ----sdm21b, fig.width=9, fig.height=6----------------------------------------
# make a new, empty, smaller raster
m <- crop(rast(r[[1]]), ext(x)+1)
# extract cell numbers for the circles
v <- cells(m, x)

# get unique cell numbers from which you could sample
v <- unique(v[,"cell"])
head(v)

# to display the results
m[v] <- 1
plot(m)
lines(x)