IDEAS home Printed from https://ideas.repec.org/h/spr/ssdmcp/978-3-031-82279-7_9.html

fdaMocca: An R Package for Model-Based Clustering for Functional Data with Covariates

In: Quantitative Methods and Data Analysis in Applied Demography - Volume 2

Author

Listed:
  • Per Arnqvist

    (Umeå University, Department of Mathematics and Mathematical Statistics)

  • Sara Sjöstedt de Luna

    (Umeå University, Department of Mathematics and Mathematical Statistics)

  • Natalya Pya Arnqvist

    (Umeå University, Department of Mathematics and Mathematical Statistics)

Abstract

This paper presents an R package, fdaMocca, that provides routines for model-based functional cluster analysis for functional data with optional covariates. The idea is to cluster functional subjects (often called functional objects) into homogenous groups by using spline smoothers (for functional data) together with scalar covariates. The spline coefficients and the covariates are modelled as a multivariate Gaussian mixture model, where the number of mixtures corresponds to the number of clusters. The parameters of the model are estimated by maximizing the observed mixture likelihood via an EM algorithm. The usefulness of fdaMocca and its clustering methods is illustrated on a functional data set with covariates from 6400 annual seasonal patterns of varved lake sediment from Lake Kassjön (Northern Sweden). Each varve contains information about the weather the year the varve was formed and thus may be used to reconstruct past climate.

Suggested Citation

  • Per Arnqvist & Sara Sjöstedt de Luna & Natalya Pya Arnqvist, 2025. "fdaMocca: An R Package for Model-Based Clustering for Functional Data with Covariates," The Springer Series on Demographic Methods and Population Analysis, in: Christos H. Skiadas & Charilaos Skiadas (ed.), Quantitative Methods and Data Analysis in Applied Demography - Volume 2, chapter 0, pages 95-108, Springer.
    • Handle: RePEc:spr:ssdmcp:978-3-031-82279-7_9
    DOI: 10.1007/978-3-031-82279-7_9
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:spr:ssdmcp:978-3-031-82279-7_9. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.