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PresenceAbsence: An R Package for Presence Absence Analysis

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  • Freeman, Elizabeth A.
  • Moisen, Gretchen

Abstract

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.

Suggested Citation

  • Freeman, Elizabeth A. & Moisen, Gretchen, 2008. "PresenceAbsence: An R Package for Presence Absence Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i11).
    • Handle: RePEc:jss:jstsof:v:023:i11
    DOI: http://hdl.handle.net/10.18637/jss.v023.i11
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    1. Koo, Kyung Ah & Kong, Woo-Seok & Park, Seon Uk & Lee, Joon Ho & Kim, Jaeuk & Jung, Huicheul, 2017. "Sensitivity of Korean fir (Abies koreana Wils.), a threatened climate relict species, to increasing temperature at an island subalpine area," Ecological Modelling, Elsevier, vol. 353(C), pages 5-16.
    2. Akpoti, Komlavi & Groen, Thomas & Dossou-Yovo, Elliott & Kabo-bah, Amos T. & Zwart, Sander J., 2022. "Climate change-induced reduction in agricultural land suitability of West-Africa's inland valley landscapes," Agricultural Systems, Elsevier, vol. 200(C).
    3. Mestre, Frederico & Pita, Ricardo & Paupério, Joana & Martins, Filipa M.S. & Alves, Paulo Célio & Mira, António & Beja, Pedro, 2015. "Combining distribution modelling and non-invasive genetics to improve range shift forecasting," Ecological Modelling, Elsevier, vol. 297(C), pages 171-179.
    4. Maria, Bobrowski & Udo, Schickhoff, 2017. "Why input matters: Selection of climate data sets for modelling the potential distribution of a treeline species in the Himalayan region," Ecological Modelling, Elsevier, vol. 359(C), pages 92-102.
    5. Freeman, Elizabeth A. & Moisen, Gretchen G., 2008. "A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa," Ecological Modelling, Elsevier, vol. 217(1), pages 48-58.
    6. Amanda West & Sunil Kumar & Catherine Jarnevich, 2016. "Regional modeling of large wildfires under current and potential future climates in Colorado and Wyoming, USA," Climatic Change, Springer, vol. 134(4), pages 565-577, February.
    7. Ehara, Makoto & Matsuura, Toshiya & Gong, Hao & Sokh, Heng & Leng, Chivin & Choeung, Hong Narith & Sem, Rida & Nomura, Hisako & Tsuyama, Ikutaro & Matsui, Tetsuya & Hyakumura, Kimihiko, 2023. "Where do people vulnerable to deforestation live? Triaging forest conservation interventions for sustainable non-timber forest products," Land Use Policy, Elsevier, vol. 131(C).
    8. Simon, Alois & Katzensteiner, Klaus & Wallentin, Gudrun, 2023. "The integration of hierarchical levels of scale in tree species distribution models of silver fir (Abies alba Mill.) and European beech (Fagus sylvatica L.) in mountain forests," Ecological Modelling, Elsevier, vol. 485(C).
    9. Sillero, Neftalí & Campos, João Carlos & Arenas-Castro, Salvador & Barbosa, A.Márcia, 2023. "A curated list of R packages for ecological niche modelling," Ecological Modelling, Elsevier, vol. 476(C).
    10. Haywood, Andrew & Stone, Christine, 2011. "Mapping eucalypt forest susceptible to dieback associated with bell miners (Manorina melanophys) using laser scanning, SPOT 5 and ancillary topographical data," Ecological Modelling, Elsevier, vol. 222(5), pages 1174-1184.
    11. Vorpahl, Peter & Elsenbeer, Helmut & Märker, Michael & Schröder, Boris, 2012. "How can statistical models help to determine driving factors of landslides?," Ecological Modelling, Elsevier, vol. 239(C), pages 27-39.
    12. Amanda M. West & Sunil Kumar & Catherine S. Jarnevich, 2016. "Regional modeling of large wildfires under current and potential future climates in Colorado and Wyoming, USA," Climatic Change, Springer, vol. 134(4), pages 565-577, February.
    13. Cushman, S.A. & Kilshaw, K. & Campbell, R.D. & Kaszta, Z. & Gaywood, M. & Macdonald, D.W., 2024. "Comparing the performance of global, geographically weighted and ecologically weighted species distribution models for Scottish wildcats using GLM and Random Forest predictive modeling," Ecological Modelling, Elsevier, vol. 492(C).
    14. Pliscoff, Patricio & Luebert, Federico & Hilger, Hartmut H. & Guisan, Antoine, 2014. "Effects of alternative sets of climatic predictors on species distribution models and associated estimates of extinction risk: A test with plants in an arid environment," Ecological Modelling, Elsevier, vol. 288(C), pages 166-177.
    15. Freeman, Elizabeth A. & Moisen, Gretchen G. & Frescino, Tracey S., 2012. "Evaluating effectiveness of down-sampling for stratified designs and unbalanced prevalence in Random Forest models of tree species distributions in Nevada," Ecological Modelling, Elsevier, vol. 233(C), pages 1-10.

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