Print summary information from model evaluation object (POEvaluation).

Display the most general and informative characteristics of a model evaluation object.

# S3 method for POEvaluation
print(x, ...)

Arguments

x

(POEvaluation) A presence-only evaluation object to be messaged. It could be the return of function evaluate_po.

...

Not used.

Value

The same object that was passed as input.

See also

evaluate_po, plot.POEvaluation

Examples

# \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)

eval_train <- evaluate_po(mod$model,
  occ_pred = mod$pred_train$prediction,
  var_pred = na.omit(as.vector(mod$prediction[[1]])))

print(eval_train)
# }