plot.VariableContribution: Exhibit variable contribution for target observations.

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

Exhibit variable contribution for target observations.

Description

Use ggplot2 to plot variable contribution for each target observation separately or summarize the overall variable contribution across all selected observations.

Usage

## S3 method for class 'VariableContribution'
plot(x, plot_each_obs = FALSE, num_features = 5, ...)

Arguments

x	(VariableContribution) The VariableContribution object to plot. It could be the return of function variable_contrib.
plot_each_obs	(logical) The option of plot type. If TRUE, it will plot variable contribution for every observation. Otherwise, it will plot variable contribution violin plot for all observations.
num_features	(integer) A number of most important features to plot. Just work if plot_each_obs is TRUE.
...	Not used.

Value

ggplot2 figure of Variable Contribution.

See Also

variable_contrib

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_contribution <- variable_contrib(
  model = mod$model,
  var_occ = mod$vars_train,
  var_occ_analysis = mod$vars_train %>% slice(1:10))

# Plot variable contribution to each observation
plot(var_contribution,
     plot_each_obs = TRUE,
     num_features = 3)

# Plot the summarized contribution
plot(var_contribution)

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