PresenceAbsence: An R Package for Presence Absence Analysis

By Elizabeth A. Freeman, Gretchen G. Moisen. 2008

514 citations recorded by the source corpus.

The PresenceAbsence package for R provides a set of functions useful when evaluating the results of presence-absence analysis, for example, models of species distribution or the analysis of diagnostic tests. The package provides a toolkit for selecting the optimal threshold for translating a probability surface into presence-absence maps specifically tailored to their intended use. The package includes functions for calculating threshold dependent measures such as confusion matrices, percent correctly classified (PCC), sensitivity, specificity, and Kappa, and produces plots of each measure as the threshold is varied. It also includes functions to plot the Receiver Operator Characteristic (ROC) curve and calculates the associated area under the curve (AUC), a threshold independent measure of model quality. Finally, the package computes optimal thresholds by multiple criteria, and plots these optimized thresholds on the graphs.

Subfields: Artificial Intelligence, Ecological Modeling, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/ecafc243f659a52bffdcf5df236bbbd175d8be3f

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Citations in the corpus

Comprehensiveness of conservation of useful wild plants: An operational indicator for biodiversity and sustainable development targets (2019, background): “, 2015), velox (Hunziker, 2017), PresenceAbsence (Freeman and Moisen, 2008), rJava (Urbanek, 2017), dismo (Hijmans et al.”
The effects of small sample size and sample bias on threshold selection and accuracy assessment of species distribution models (2012, methodology): “Studies thus far have relied on relatively large sample sizes, either to create the models (Liu et al. 2005), or to test their accuracy and select thresholds (Hernandez et al. 2006, Jimenez-Valverde and Lobo 2007, Freeman and Moisen 2008a).”
Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias (2014, methodology): “The calculation of AUC was performed using the R package ‘‘PresenceAbsence’’ [74], using as test points the fraction of the original dataset which was excluded to create the biased dataset.”
OptimalCutpoints: An R Package for Selecting Optimal Cutpoints in Diagnostic Tests (2014, methodology): “These include the R packages such as, PresenceAbsence which includes 12 diﬀerent criteria (Freeman and Moisen 2008), and SDMTools with 8 methods (VanDerWal, Falconi, Januchowski, Shoo, and Storlie 2012).”
Testing species assemblage predictions from stacked and joint species distribution models (2019, unknown): “…2011), gbm (Ridgeway, 2013), dismo (Hijmans, Phillips, Leathwick, & Elith, 2017), randomForest (Liaw & Wiener, 2002), boral (Hui, 2016), ncf (Bjornstad, 2016), PresenceAbsence (Freeman & Moisen, 2008), ecospat (Broennimann, Cola, & Guisan, 2016) and lme4 (Bates, Maechler, Bolker, & Walker, 2015).”
A tutorial on calibration measurements and calibration models for clinical prediction models (2020, methodology): “23 This method groups the estimates according to the H-L H-statistics and plots the average of the actual number of positive outcomes against the midpoint of each group’s interval.”
The distribution of deep-sea sponge aggregations in the North Atlantic and implications for their effective spatial management (2016, methodology): “Model assessment and metrics calculation was all done in R using the “dismo” library (Hijmans et al., 2012) and “PresenceAbsence” library (Freeman and Moisen 2008) in R (Team 2011).”
A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and Kappa (2008, background): “Freeman and Moisen (2008) provides a case study and sample code, illustrating the use of this software.”
Predicting species distributions based on incomplete survey data: the trade‐off between precision and scale (2010, methodology): “Second, we calculated the AUC on both validation datasets using the R-package PresenceAbsence (Freeman and Moisen 2008).”