Show the response curve and the map of contribution change from
detect_envi_change.
# S3 method for EnviChange
plot(x, ...)(EnviChange) A EnviChange object to be messaged.
It could be the return of function detect_envi_change.
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))
#'
# 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 = 10,
sample_size = 0.8, ndim = 1L,
seed = 123L, response = FALSE,
spatial_response = FALSE,
check_variable = FALSE)
# Use a fixed value
bio1_changes <- detect_envi_change(
model = mod$model,
var_occ = mod$vars_train,
variables = mod$variables,
shap_nsim = 1,
target_var = "bio1",
var_future = 5)
plot(bio1_changes)
# }