Exhibit variable contribution for target observations.

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

# S3 method for 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

# \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, 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)
# }