## ----sdm22a-------------------------------------------------------------------
library(predicts)
f <- system.file("ex/bio.tif", package="predicts")
predictors <- rast(f)
predictors
names(predictors)
plot(predictors)


## ----sdm23a-------------------------------------------------------------------
library(geodata)
wrld <- world(path=".")
file <- paste(system.file(package="predicts"), "/ex/bradypus.csv", sep="")
bradypus <- read.table(file,  header=TRUE,  sep=',')
# we do not need the first column
bradypus  <- bradypus[,-1]


## ----sdm23b-------------------------------------------------------------------
# first layer of the SpatRaster
plot(predictors, 1)
lines(wrld)
points(bradypus, col='blue')


## ----sdm24a-------------------------------------------------------------------
presvals <- extract(predictors, bradypus)
# remove the ID variable
presvals <- presvals[,-1]
# setting random seed to always create the same
# random set of points for this example
set.seed(0)
backgr <- spatSample(predictors, 500, "random", as.points=TRUE, na.rm=TRUE)
absvals <- values(backgr)

pb <- c(rep(1, nrow(presvals)), rep(0, nrow(absvals)))
sdmdata <- data.frame(cbind(pb, rbind(presvals, absvals)))
head(sdmdata)
tail(sdmdata)
summary(sdmdata)


## ----sdm24b-------------------------------------------------------------------
pairs(sdmdata[,2:5], cex=0.1)


## ----sdm24b2------------------------------------------------------------------
saveRDS(sdmdata, "sdm.Rds")
saveRDS(presvals, "pvals.Rds")