IDEAS home Printed from https://ideas.repec.org/a/jss/jstsof/v077i06.html
   My bibliography  Save this article

Anthropometry: An R Package for Analysis of Anthropometric Data

Author

Listed:
  • Vinué, Guillermo

Abstract

The development of powerful new 3D scanning techniques has enabled the generation of large up-to-date anthropometric databases which provide highly valued data to improve the ergonomic design of products adapted to the user population. As a consequence, Ergonomics and Anthropometry are two increasingly quantitative fields, so advanced statistical methodologies and modern software tools are required to get the maximum benefit from anthropometric data. This paper presents a new R package, called Anthropometry, which is available on the Comprehensive R Archive Network. It brings together some statistical methodologies concerning clustering, statistical shape analysis, statistical archetypal analysis and the statistical concept of data depth, which have been especially developed to deal with anthropometric data. They are proposed with the aim of providing effective solutions to some common anthropometric problems, such as clothing design or workstation design (focusing on the particular case of aircraft cockpits). The utility of the package is shown by analyzing the anthropometric data obtained from a survey of the Spanish female population performed in 2006 and from the'67 United States Air Force survey. This manuscript is also contained in Anthropometry as a vignette.

Suggested Citation

  • Vinué, Guillermo, 2017. "Anthropometry: An R Package for Analysis of Anthropometric Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i06).
    • Handle: RePEc:jss:jstsof:v:077:i06
    DOI: http://hdl.handle.net/10.18637/jss.v077.i06
    as

    Download full text from publisher

    File URL: https://www.jstatsoft.org/index.php/jss/article/view/v077i06/v77i06.pdf
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v077i06/Anthropometry_1.8.tar.gz
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v077i06/v77i06.R
    Download Restriction: no
    File URL: https://libkey.io/http://hdl.handle.net/10.18637/jss.v077.i06?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. E. Weiszfeld & Frank Plastria, 2009. "On the point for which the sum of the distances to n given points is minimum," Annals of Operations Research, Springer, vol. 167(1), pages 7-41, March.
    2. Vinué, Guillermo & Epifanio, Irene & Alemany, Sandra, 2015. "Archetypoids: A new approach to define representative archetypal data," Computational Statistics & Data Analysis, Elsevier, vol. 87(C), pages 102-115.
    3. Seiler, Christian & Wohlrabe, Klaus, 2013. "Archetypal scientists," Journal of Informetrics, Elsevier, vol. 7(2), pages 345-356.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Moliner, Jesús & Epifanio, Irene, 2019. "Robust multivariate and functional archetypal analysis with application to financial time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 195-208.
    2. Sabine Gralka & Klaus Wohlrabe, 2022. "Classifying top economists using archetypoid analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 29(14), pages 1342-1346, August.
    3. Epifanio, Irene, 2016. "Functional archetype and archetypoid analysis," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 24-34.
    4. Klaus Wohlrabe & Sabine Gralka, 2020. "Using archetypoid analysis to classify institutions and faculties of economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 159-179, April.
    5. Jiwon Baik & Alan T. Murray, 2022. "Locating a facility to simultaneously address access and coverage goals," Papers in Regional Science, Wiley Blackwell, vol. 101(5), pages 1199-1217, October.
    6. Frank Plastria & Tom Blockmans, 2015. "Multidimensional Theoretic Consensus Reachability: The Impact of Distance Selection and Issue Saliences," Group Decision and Negotiation, Springer, vol. 24(1), pages 1-44, January.
    7. Amir Beck & Shoham Sabach, 2015. "Weiszfeld’s Method: Old and New Results," Journal of Optimization Theory and Applications, Springer, vol. 164(1), pages 1-40, January.
    8. Nguyen Mau Nam & Nguyen Hoang & Nguyen Thai An, 2014. "Constructions of Solutions to Generalized Sylvester and Fermat–Torricelli Problems for Euclidean Balls," Journal of Optimization Theory and Applications, Springer, vol. 160(2), pages 483-509, February.
    9. David Degras, 2021. "Sparse group fused lasso for model segmentation: a hybrid approach," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 625-671, September.
    10. Simeon Reich & Truong Minh Tuyen, 2023. "The Generalized Fermat–Torricelli Problem in Hilbert Spaces," Journal of Optimization Theory and Applications, Springer, vol. 196(1), pages 78-97, January.
    11. Zvi Drezner & Mozart B. C. Menezes, 2016. "The wisdom of voters: evaluating the Weber objective in the plane at the Condorcet solution," Annals of Operations Research, Springer, vol. 246(1), pages 205-226, November.
    12. Aleix Alcacer & Irene Epifanio & M Victoria Ibáñez & Amelia Simó & Alfredo Ballester, 2020. "A data-driven classification of 3D foot types by archetypal shapes based on landmarks," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-19, January.
    13. Vinué, Guillermo & Epifanio, Irene & Alemany, Sandra, 2015. "Archetypoids: A new approach to define representative archetypal data," Computational Statistics & Data Analysis, Elsevier, vol. 87(C), pages 102-115.
    14. Dürre, Alexander & Vogel, Daniel & Tyler, David E., 2014. "The spatial sign covariance matrix with unknown location," Journal of Multivariate Analysis, Elsevier, vol. 130(C), pages 107-117.
    15. Plastria, Frank, 2016. "How bad can the centroid be?," European Journal of Operational Research, Elsevier, vol. 252(1), pages 98-102.
    16. Schupp, Claudia & Wache, Benjamin, 2014. "Wie groß ist der Einfluss von deutschen Wirtschaftsforschungsinstituten? Ein Ranking anhand von RePEc-Daten [How large is the influence of German economic research institutes? A ranking analysis us," MPRA Paper 55519, University Library of Munich, Germany.
    17. Zvi Drezner & Carlton Scott, 2013. "Location of a distribution center for a perishable product," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 78(3), pages 301-314, December.
    18. Kyunghee Han & Pantelis Z Hadjipantelis & Jane-Ling Wang & Michael S Kramer & Seungmi Yang & Richard M Martin & Hans-Georg Müller, 2018. "Functional principal component analysis for identifying multivariate patterns and archetypes of growth, and their association with long-term cognitive development," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-18, November.
    19. Christian Seiler & Klaus Wohlrabe, 2012. "Archetypal Analysis: A New Approach to Classifying Researchers," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 65(22), pages 07-12, November.
    20. Alexandra Baumann & Klaus Wohlrabe, 2020. "Where have all the working papers gone? Evidence from four major economics working paper series," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2433-2441, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jss:jstsof:v:077:i06. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: http://www.jstatsoft.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.