itsdm

install.packages("itsdm")

# install.packages("remotes")
remotes::install_github("LLeiSong/itsdm")

library(itsdm)
library(dplyr)
library(stars)
library(ggplot2)

# Using a pseudo presence-only occurrence dataset of
# virtual species provided in this package
data("occ_virtual_species")

# Split to training and test
occ_virtual_species <- occ_virtual_species %>%
  mutate(id = row_number())
set.seed(11)
occ <- occ_virtual_species %>% sample_frac(0.7)
occ_test <- occ_virtual_species %>% filter(! id %in% occ$id)
occ <- occ %>% select(-id)
occ_test <- occ_test %>% select(-id)

# Get environmental variables
env_vars <- system.file(
  'extdata/bioclim_tanzania_10min.tif',
  package = 'itsdm') %>% read_stars() %>%
  slice('band', c(1, 6, 12, 15))

# Train the model
mod <- isotree_po(
  occ = occ, occ_test = occ_test,
  variables = env_vars, ntrees = 200,
  sample_size = 0.8, ndim = 2L,
  seed = 123L)

# Check results
## Suitability
ggplot() +
  geom_stars(data = mod$prediction) +
  scale_fill_viridis_c('Predicted suitability',
                       na.value = 'transparent') +
  coord_equal() +
  theme_linedraw()

## Plot independent response curves
plot(mod$independent_responses, 
  target_var = c('bio1', 'bio12'))