calib_results_maxnet: Calibration Results (Maxnet)

In kuenm2: Detailed Development of Ecological Niche Models

Calibration Results (Maxnet)

Description

A calibration_results object resulted from calibration() using maxnet algorithm

Usage

data("calib_results_maxnet")

Format

A calibration_results with the following elements:

species

Species names

calibration_data

A data.frame with the variables extracted to presence and background points

formula_grid

A data.frame with the ID, formulas, and regularization multipliers of each candidate model

part_data

A list with the partition data, where each element corresponds to a replicate and contains the indices of the test points for that replicate

partition_method

A character indicating the partition method

n_replicates

A numeric value indicating the number of replicates or k-folds

train_proportion

A numeric value indicating the proportion of occurrences used as train points when the partition method is 'subsample' or 'bootstrap'

data_xy

A data.frame with the coordinates of the occurrence and background points

continuous_variables

A character indicating the names of the continuous variables

categorical_variables

A character indicating the names of the categorical variables

weights

A numeric value specifying weights for the occurrence records. It's NULL, meaning it was not set weights.

pca

A prcomp object storing PCA information. Is NULL, meaning PCA was not performed

algorithm

A character indicating the algorithm (maxnet)

calibration_results

A list containing the evaluation metrics for each candidate model

omission_rate

A numeric value indicating the omission rate used to evaluate the models (10%)

addsamplestobackground

A logical value indicating whether to add to the background any presence sample that is not already there.

selected_models

A data.frame with the formulas and evaluation metrics for each selected model

summary

A list with the number and the ID of the models removed and selected during selection procedure

kuenm2 documentation built on April 21, 2026, 1:07 a.m.