Display informative and detailed figures of continuous Boyce index, AUC curves, and TSS curve.
# S3 method for POEvaluation
plot(x, ...)(POEvaluation) The presence-only evaluation object to plot.
It could be the return of function evaluate_po.
Not used.
A patchwork of ggplot2 figure of AUC_ratio, AUC_background and CBI.
# \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]])))
plot(eval_train)
# }