Display the equation and parameters of a PAConversion object.
# S3 method for PAConversion
print(x, ...)(PAConversion) A PAConversion object to be messaged.
It could be the return of function convert_to_pa.
Not used.
The same object that was passed as input.
# \donttest{
# Using a pseudo presence-only occurrence dataset of
# virtual species provided in this package
library(dplyr)
library(sf)
library(stars)
library(itsdm)
# Prepare data
data("occ_virtual_species")
obs_df <- occ_virtual_species %>% filter(usage == "train")
eval_df <- occ_virtual_species %>% filter(usage == "eval")
x_col <- "x"
y_col <- "y"
obs_col <- "observation"
# Format the observations
obs_train_eval <- format_observation(
obs_df = obs_df, eval_df = eval_df,
x_col = x_col, y_col = y_col, obs_col = obs_col,
obs_type = "presence_only")
env_vars <- system.file(
'extdata/bioclim_tanzania_10min.tif',
package = 'itsdm') %>% read_stars() %>%
slice('band', c(1, 5, 12, 16))
# With imperfect_presence mode,
mod <- isotree_po(
obs_mode = "imperfect_presence",
obs = obs_train_eval$obs,
obs_ind_eval = obs_train_eval$eval,
variables = env_vars, ntrees = 20,
sample_size = 0.8, ndim = 2L,
seed = 123L, response = FALSE,
spatial_response = FALSE,
check_variable = FALSE)
# Threshold conversion
pa_thred <- convert_to_pa(mod$prediction, method = 'threshold', beta = 0.5)
print(pa_thred)
# }