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fdANOVA: an R software package for analysis of variance for univariate and multivariate functional data

Author

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  • Tomasz Górecki

    (Adam Mickiewicz University)

  • Łukasz Smaga

    (Adam Mickiewicz University)

Abstract

Functional data, i.e., observations represented by curves or functions, frequently arise in various fields. The theory and practice of statistical methods for such data is referred to as functional data analysis (FDA) which is one of major research fields in statistics. The practical use of FDA methods is made possible thanks to availability of specialized and usually free software. In particular, a number of R packages is devoted to these methods. In the paper, we introduce a new R package fdANOVA which provides an access to a broad range of global analysis of variance methods for univariate and multivariate functional data. The implemented testing procedures mainly for homoscedastic case are briefly overviewed and illustrated by examples on a well known functional data set. To reduce the computation time, parallel implementation is developed and its efficiency is empirically evaluated. Since some of the implemented tests have not been compared in terms of size control and power yet, appropriate simulations are also conducted. Their results can help in choosing proper testing procedures in practice.

Suggested Citation

  • Tomasz Górecki & Łukasz Smaga, 2019. "fdANOVA: an R software package for analysis of variance for univariate and multivariate functional data," Computational Statistics, Springer, vol. 34(2), pages 571-597, June.
    • Handle: RePEc:spr:compst:v:34:y:2019:i:2:d:10.1007_s00180-018-0842-7
    DOI: 10.1007/s00180-018-0842-7
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    Cited by:

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    3. Rafael Meléndez & Ramón Giraldo & Víctor Leiva, 2020. "Sign, Wilcoxon and Mann-Whitney Tests for Functional Data: An Approach Based on Random Projections," Mathematics, MDPI, vol. 9(1), pages 1-11, December.
    4. Huang, Wei-Hsueh & Huang, Li-Shan & Yang, Cheng-Tao, 2022. "Invariant tests for functional data with application to an earthquake impact study," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    5. Hlávka, Zdeněk & Hlubinka, Daniel & Koňasová, Kateřina, 2022. "Functional ANOVA based on empirical characteristic functionals," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
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    7. Germán Aneiros & Ricardo Cao & Philippe Vieu, 2019. "Editorial on the special issue on Functional Data Analysis and Related Topics," Computational Statistics, Springer, vol. 34(2), pages 447-450, June.
    8. Jiménez-Gamero, M. Dolores & Franco-Pereira, Alba M., 2021. "Testing the equality of a large number of means of functional data," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
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