plot.VariableAnalysis: Display variable importance.

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

Display variable importance.

Description

Display informative and detailed figures of variable importance.

Usage

## S3 method for class 'VariableAnalysis'
plot(x, ...)

Arguments

x	(VariableAnalysis) The variable importance object to plot. It could be the return of function variable_analysis.
...	Not used.

Value

A patchwork of ggplot2 figure of variable importance according to multiple metrics.

See Also

variable_analysis, print.VariableAnalysis

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)

var_analysis <- variable_analysis(
  model = mod$model,
  pts_occ = mod$observation,
  pts_occ_test = mod$independent_test,
  variables = mod$variables)
plot(var_analysis)

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