IDEAS home Printed from https://ideas.repec.org/a/spr/jagbes/v21y2016i2d10.1007_s13253-015-0240-3.html
   My bibliography  Save this article

Categorising Count Data into Ordinal Responses with Application to Ecological Communities

Author

Listed:
  • D. Fernández

    (Victoria University of Wellington)

  • S. Pledger

    (Victoria University of Wellington)

Abstract

Count data sets may involve overdispersion from a set of species and underdispersion from another set which would require fitting different models (e.g. a negative binomial model for the overdispersed set and a binomial model for the underdispersed one). Additionally, many count data sets have very high counts and very low counts. Categorising these counts into ordinal categories makes the actual counts less influential in the model fitting, giving broad categories which enable us to detect major broadly based patterns of turnover or nestedness shown by groups of species. In this paper, a strategy of categorising count data into ordinal data was carried out and also we implemented measures to compare different cluster structures. The application of this categorising strategy and a comparison of clustering results between count and categorised ordinal data in two ecological community data sets are shown. A major advantage of using our ordinal approach is that it allows for the inclusion of all different levels of dispersion in the data in one methodology, without treating the data differently. This reduction of the parameters on modelling different levels of dispersion does not substantially change the results in clustering structure. In the two data sets used in this paper, we observed ordinal clustering structure up to 93.1 % similar to those from the count data approaches. This has the important implication of supporting simpler, faster data collection using ordinal scales only. Supplementary materials accompanying this paper appear on-line.

Suggested Citation

  • D. Fernández & S. Pledger, 2016. "Categorising Count Data into Ordinal Responses with Application to Ecological Communities," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(2), pages 348-362, June.
    • Handle: RePEc:spr:jagbes:v:21:y:2016:i:2:d:10.1007_s13253-015-0240-3
    DOI: 10.1007/s13253-015-0240-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13253-015-0240-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13253-015-0240-3?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pledger, Shirley & Arnold, Richard, 2014. "Multivariate methods using mixtures: Correspondence analysis, scaling and pattern-detection," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 241-261.
    2. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Daniel Fernández & Louise McMillan & Richard Arnold & Martin Spiess & Ivy Liu, 2022. "Goodness-of-Fit and Generalized Estimating Equation Methods for Ordinal Responses Based on the Stereotype Model," Stats, MDPI, vol. 5(2), pages 1-14, June.
    2. Daniel Fernández & Richard Arnold & Shirley Pledger & Ivy Liu & Roy Costilla, 2019. "Finite mixture biclustering of discrete type multivariate data," 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. 13(1), pages 117-143, March.

    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. M. P. B. Gallaugher & C. Biernacki & P. D. McNicholas, 2023. "Parameter-wise co-clustering for high-dimensional data," Computational Statistics, Springer, vol. 38(3), pages 1597-1619, September.
    2. Wu, Han-Ming & Tien, Yin-Jing & Chen, Chun-houh, 2010. "GAP: A graphical environment for matrix visualization and cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 767-778, March.
    3. José E. Chacón, 2021. "Explicit Agreement Extremes for a 2 × 2 Table with Given Marginals," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 257-263, July.
    4. Roberto Rocci & Stefano Antonio Gattone & Roberto Di Mari, 2018. "A data driven equivariant approach to constrained Gaussian mixture modeling," 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. 12(2), pages 235-260, June.
    5. Redivo, Edoardo & Nguyen, Hien D. & Gupta, Mayetri, 2020. "Bayesian clustering of skewed and multimodal data using geometric skewed normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    6. Zhu, Xuwen & Melnykov, Volodymyr, 2018. "Manly transformation in finite mixture modeling," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 190-208.
    7. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.
    8. A van Giessen & K G M Moons & G A de Wit & W M M Verschuren & J M A Boer & H Koffijberg, 2015. "Tailoring the Implementation of New Biomarkers Based on Their Added Predictive Value in Subgroups of Individuals," PLOS ONE, Public Library of Science, vol. 10(1), pages 1-14, January.
    9. Yaeji Lim & Hee-Seok Oh & Ying Kuen Cheung, 2019. "Multiscale Clustering for Functional Data," Journal of Classification, Springer;The Classification Society, vol. 36(2), pages 368-391, July.
    10. Stefano Tonellato & Andrea Pastore, 2013. "On the comparison of model-based clustering solutions," Working Papers 2013:05, Department of Economics, University of Venice "Ca' Foscari".
    11. Elvira Pelle & Roberta Pappadà, 2021. "A clustering procedure for mixed-type data to explore ego network typologies: an application to elderly people living alone in Italy," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1507-1533, December.
    12. Renato Cordeiro Amorim, 2016. "A Survey on Feature Weighting Based K-Means Algorithms," Journal of Classification, Springer;The Classification Society, vol. 33(2), pages 210-242, July.
    13. Tom Wilderjans & Eva Ceulemans & Iven Mechelen, 2008. "The CHIC Model: A Global Model for Coupled Binary Data," Psychometrika, Springer;The Psychometric Society, vol. 73(4), pages 729-751, December.
    14. Dong Liu & Changwei Zhao & Yong He & Lei Liu & Ying Guo & Xinsheng Zhang, 2023. "Simultaneous cluster structure learning and estimation of heterogeneous graphs for matrix‐variate fMRI data," Biometrics, The International Biometric Society, vol. 79(3), pages 2246-2259, September.
    15. Yuchen Liang & Guowei Shi & Runlin Cai & Yuchen Yuan & Ziying Xie & Long Yu & Yingjian Huang & Qian Shi & Lizhe Wang & Jun Li & Zhonghui Tang, 2024. "PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    16. Jeffrey Andrews & Paul McNicholas, 2014. "Variable Selection for Clustering and Classification," Journal of Classification, Springer;The Classification Society, vol. 31(2), pages 136-153, July.
    17. Marek Obrębalski & Marek Walesiak, 2015. "Functional structure of Polish regions in the period 2004-2013 – measurement via HHI Index, Florence’s coefficient of localization and cluster analysis," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(2), pages 223-242, June.
    18. Luca Scaffidi Domianello & Giampiero M. Gallo & Edoardo Otranto, 2024. "Smooth and Abrupt Dynamics in Financial Volatility: The MS‐MEM‐MIDAS," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(1), pages 21-43, February.
    19. Paul Bastide & Mahendra Mariadassou & Stéphane Robin, 2017. "Detection of adaptive shifts on phylogenies by using shifted stochastic processes on a tree," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1067-1093, September.
    20. Douglas L. Steinley & M. J. Brusco, 2019. "Using an Iterative Reallocation Partitioning Algorithm to Verify Test Multidimensionality," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 397-413, October.

    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:spr:jagbes:v:21:y:2016:i:2:d:10.1007_s13253-015-0240-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.