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yaImpute: An R Package for kNN Imputation

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  • Crookston, Nicholas L.
  • Finley, Andrew O.

Abstract

This article introduces yaImpute, an R package for nearest neighbor search and imputation. Although nearest neighbor imputation is used in a host of disciplines, the methods implemented in the yaImpute package are tailored to imputation-based forest attribute estimation and mapping. The impetus to writing the yaImpute is a growing interest in nearest neighbor imputation methods for spatially explicit forest inventory, and a need within this research community for software that facilitates comparison among different nearest neighbor search algorithms and subsequent imputation techniques. yaImpute provides directives for defining the search space, subsequent distance calculation, and imputation rules for a given number of nearest neighbors. Further, the package offers a suite of diagnostics for comparison among results generated from different imputation analyses and a set of functions for mapping imputation results.

Suggested Citation

  • Crookston, Nicholas L. & Finley, Andrew O., 2008. "yaImpute: An R Package for kNN Imputation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i10).
    • Handle: RePEc:jss:jstsof:v:023:i10
    DOI: http://hdl.handle.net/10.18637/jss.v023.i10
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    Cited by:

    1. Anton Kocheturov & Panos M. Pardalos & Athanasia Karakitsiou, 2019. "Massive datasets and machine learning for computational biomedicine: trends and challenges," Annals of Operations Research, Springer, vol. 276(1), pages 5-34, May.
    2. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    3. Kowarik, Alexander & Templ, Matthias, 2016. "Imputation with the R Package VIM," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i07).
    4. Agni Orfanoudaki & Emma Chesley & Christian Cadisch & Barry Stein & Amre Nouh & Mark J Alberts & Dimitris Bertsimas, 2020. "Machine learning provides evidence that stroke risk is not linear: The non-linear Framingham stroke risk score," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-20, May.
    5. Hui Peng & He Wang & Weijia Kong & Jinyan Li & Wilson Wen Bin Goh, 2024. "Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    6. Cécile C. Remy & Alisa R. Keyser & Dan J. Krofcheck & Marcy E. Litvak & Matthew D. Hurteau, 2021. "Future fire-driven landscape changes along a southwestern US elevation gradient," Climatic Change, Springer, vol. 166(3), pages 1-20, June.

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