Ecological Niche Modeling using Presence-Absence Data

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Documentation for package ‘enmpa’ version 0.2.1

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enmpa-package enmpa: Ecological Niche Modeling using Presence-Absence Data
aux_string_comb Get GLM formulas according to defined response types
aux_var_comb Get GLM formulas according to defined response types
calibration_glm GLM calibration with presence-absence data
cal_res Example of results obtained from GLM calibration using enmpa
enmpa enmpa: Ecological Niche Modeling using Presence-Absence Data
enmpa_calibration Constructor of S3 objects of class enmpa_calibration
enmpa_fitted_models Constructor of S3 objects of class enmpa_fitted_models
enm_data Example data used to run model calibration exercises
evaluation_stats Summary of evaluation statistics for candidate models
fit_glms Fitting selected GLMs models
fit_selected Fitting selected GLMs models
get_formulas Get GLM formulas according to defined response types
get_formulas_main Get GLM formulas according to defined response types
independent_eval01 Evaluate final models using independent data
independent_eval1 Evaluate final models using independent data
jackknife Jackkniffe test for variable contribution
kfold_partition K-fold data partitioning
model_selection Selection of best candidate models considering various criteria
model_validation Model validation options
new_enmpa_calibration Constructor of S3 objects of class enmpa_calibration
new_enmpa_fitted_models Constructor of S3 objects of class enmpa_fitted_models
niche_signal Niche Signal detection using one or multiple variables
niche_signal_permanova Niche Signal detection using one or multiple variables
niche_signal_univariate Niche Signal detection using one or multiple variables
optimize_metrics Find threshold values to produce three optimal metrics
plot_importance Plot variable importance
plot_jk Jackkniffe plot for variable contribution
plot_niche_signal Plot Niche Signal results
plot_niche_signal_permanova Plot Niche Signal results
plot_niche_signal_univariate Plot Niche Signal results
predict_glm Extension of glm predict to generate predictions of different types
predict_selected Predictions for the models selected after calibration
print Print a short version of elements in 'calibration' and 'fitted models' objects
print-method Print a short version of elements in 'calibration' and 'fitted models' objects
print.enmpa_calibration Print a short version of elements in 'calibration' and 'fitted models' objects
print.enmpa_fitted_models Print a short version of elements in 'calibration' and 'fitted models' objects
proc_enm Partial ROC calculation
resp2var Two-Way interaction response plot
response_curve Variable response curves for GLMs
sel_fit Example of selected models fitted
summary Summary of 'calibration' and 'fitted models'
summary-method Summary of 'calibration' and 'fitted models'
summary.enmpa_calibration Summary of 'calibration' and 'fitted models'
summary.enmpa_fitted_models Summary of 'calibration' and 'fitted models'
test Example data used to test models
var_importance Variable importance for GLMs