A calibration_results object resulted from calibration() using maxnet algorithm
Usage
data("calib_results_glm")Format
A calibration_results with the following elements:
- species
Species names
- calibration_data
A
data.framewith the variables extracted to presence and background points- formula_grid
A
data.framewith the ID, formulas, and regularization multipliers of each candidate model- part_data
A
listwith the partition data, where each element corresponds to a replicate and contains the indices of the test points for that replicate- partition_method
A
characterindicating the partition method- n_replicates
A
numericvalue indicating the number of replicates or k-folds- train_proportion
A
numericvalue indicating the proportion of occurrences used as train points when the partition method is 'subsample' or 'boostrap'- data_xy
A
data.framewith the coordinates of the occurrence and bakground points- continuous_variables
A
characterindicating the names of the continuous variables- categorical_variables
A
characterindicating the names of the categorical variables- weights
A
numericvalue specifying weights for the occurrence records. It's NULL, meaning it was not set weights.- pca
A
prcompobject storing PCA information. Is NULL, meaning PCA was not performed- algorithm
A
characterindicanting the algorithm (glm)- calibration_results
A
listcontaining the evaluation metrics for each candidate model- omission_rate
A
numericvalue indicating the omission rate used to evaluate the models (10%)- addsamplestobackground
A
logicalvalue indicating whether to add to the background any presence sample that is not already there.- selected_models
A
data.framewith the formulas and evaluation metrics for each selected model- summary
A
listwith the number and the ID of the models removed and selected during selection procedure