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Calculate independent responses of each variables.

Source: R/independent_response.R
independent_response.Rd

Calculate the independent responses of each variables within the model.

Usage

independent_response(model, var_occ, variables, si = 1000, visualize = FALSE)

Arguments

model

(Any predictive model). It is isolation_forest here. It could be the item model of POIsotree made by function isotree_po.

var_occ

(data.frame, tibble) The data.frame style table that include values of environmental variables at occurrence locations.

variables

(stars) The stars of environmental variables. It should have multiple attributes instead of dims. If you have raster object instead, you could use st_as_stars to convert it to stars or use read_stars directly read source data as a stars. You also could use item variables of POIsotree made by function isotree_po.

si

(integer) The number of samples to generate response curves. If it is too small, the response curves might be biased. The default value is 1000.

visualize

(logical) if TRUE, plot the response curves. The default is FALSE.

Value

(IndependentResponse) A list of

  • responses_cont (list) A list of response values of continuous variables

  • responses_cat (list) A list of response values of categorical variables

Details

The values show how each environmental variable independently affects the modeling prediction. They show how the predicted result only using this variable changes as it is varied.

References

  • Elith, Jane, et al. "The evaluation strip: a new and robust method for plotting predicted responses from species distribution models." Ecological modelling 186.3 (2005): 280-289.doi:10.1016/j.ecolmodel.2004.12.007

See also

plot.IndependentResponse

Examples

# Using a pseudo presence-only occurrence dataset of
# virtual species provided in this package
library(dplyr)
library(sf)
library(stars)
library(itsdm)

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 = 10,
  sample_size = 0.8, ndim = 2L,
  seed = 123L, nthreads = 1,
  response = FALSE,
  spatial_response = FALSE,
  check_variable = FALSE)

independent_responses <- independent_response(
  model = mod$model,
  var_occ = mod$vars_train,
  variables = mod$variables)
plot(independent_responses)