itsdm (version 0.2.2)

plot.SHAPSpatial: Display Shapley values-based spatial variable dependence maps.

Description

Plot Shapley values-based spatial variable dependence maps using ggplot2 by optionally setting target variable(s). This only works for SHAPSpatial even though it is part of SpatialResponse.

Usage

# S3 method for SHAPSpatial
plot(x, target_var = NA, ...)

Value

ggplot2 figure of dependent maps

Arguments

x

(SHAPSpatial) The spatial variable dependence object to plot. It could be the return of function shap_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.

See Also

spatial_response

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))

# 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)

shap_spatial <- shap_spatial_response(
 model = mod$model,
 target_vars = c("bio1", "bio12"),
 var_occ = mod$vars_train,
 variables = mod$variables,
 shap_nsim = 1)

plot(shap_spatial)
plot(shap_spatial, target_var = "bio1")
# }