Ecological Niche Modeling using Presence-Absence Data
Find threshold values to produce three optimal metrics
Plot variable importance
Jackkniffe plot for variable contribution
Two-Way interaction response plot
Get GLM formulas according to defined response types
Evaluate final models using independent data
GLM calibration with presence-absence data
Constructor of S3 objects of class enmpa_calibration
Constructor of S3 objects of class enmpa_fitted_models
enmpa: Ecological Niche Modeling using Presence-Absence Data
Summary of evaluation statistics for candidate models
Fitting selected GLMs models
Jackkniffe test for variable contribution
K-fold data partitioning
Selection of best candidate models considering various criteria
Model validation options
Niche Signal detection using one or multiple variables
Plot Niche Signal results
Extension of glm predict to generate predictions of different types
Predictions for the models selected after calibration
Print a short version of elements in 'calibration' and 'fitted models'...
Partial ROC calculation
Variable response curves for GLMs
Summary of 'calibration' and 'fitted models'
Variable importance for GLMs
A set of tools to perform Ecological Niche Modeling with presence-absence data. It includes algorithms for data partitioning, model fitting, calibration, evaluation, selection, and prediction. Other functions help to explore signals of ecological niche using univariate and multivariate analyses, and model features such as variable response curves and variable importance. Unique characteristics of this package are the ability to exclude models with concave quadratic responses, and the option to clamp model predictions to specific variables. These tools are implemented following principles proposed in Cobos et al., (2022) <doi:10.17161/bi.v17i.15985>, Cobos et al., (2019) <doi:10.7717/peerj.6281>, and Peterson et al., (2008) <doi:10.1016/j.ecolmodel.2007.11.008>.