find_best_model_ntbox: Function to find the best n-dimensional ellipsoid model using...

In luismurao/hsi: "Habitat suitability index"

Description

Function to find the best n-dimensional ellipsoid model using Partial Roc as a performance criteria.

Usage

find_best_model_ntbox(
  this_species,
  cor_threshold = 0.9,
  nbg_points = 50000,
  omr_criteria = 0.1,
  ellipsoid_level = 0.975,
  nvars_to_fit = c(2, 3),
  E = 0.05,
  RandomPercent = 50,
  NoOfIteration = 1000,
  parallel = TRUE,
  n_cores = 6
)

Arguments

this_species,	Species Temporal Environment "sp.temporal.env" object see extract_by_year.
cor_threshold	Threshold valuefrom which it is considered that the correlation is high see correlation_finder.
nbg_points	Number of background points used to compute partial ROC test. See sample_envbg for more details.
omr_criteria	Omission rate used to select best models. See ellipsoid_selection for more details.
ellipsoid_level	The proportion of points to be included inside the ellipsoid see ellipsoidfit.
nvars_to_fit	Number of variables that will be used to model.
E	Amount of error admissible for Partial Roc test (by default =.05). Value should range between 0 - 1. see PartialROC
RandomPercent	Occurrence points to be sampled in randomly for the boostrap of the Partial Roc test PartialROC.
NoOfIteration	Number of iteration for the bootstrapping of the Partial Roc test PartialROC.
parallel	Logical argument to run computations in parallel. Default TRUE
n_cores	Number of cores to be used in parallelization. Default 4

Value

A "sp.temp.best.model" object with metadata of the best model given the performance of the Partial Roc test.

luismurao/hsi documentation built on Dec. 26, 2021, 9:53 a.m.