plot.SpatialResponse: Display spatial variable dependence maps.

In itsdm: Isolation Forest-Based Presence-Only Species Distribution Modeling

Display spatial variable dependence maps.

Description

Plot spatial variable dependence maps using ggplot2 by optionally setting target variable(s).

Usage

## S3 method for class 'SpatialResponse'
plot(x, target_var = NA, ...)

Arguments

x	(SpatialResponse) The spatial variable dependence object to plot. It could be the return of function spatial_response.
target_var	(vector of character) The target variable to plot. It could be NA. If it is NA, all variables will be plotted.
...	Not used.

Value

ggplot2 figure of dependent maps

See Also

spatial_response

Examples

# 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)

spatial_responses <- spatial_response(
  model = mod$model,
  var_occ = mod$vars_train,
  variables = mod$variables,
  shap_nsim = 10)
plot(spatial_responses)
plot(spatial_responses, target_var = 'bio1')

itsdm documentation built on July 9, 2023, 6:45 p.m.