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simplexreg: an R package for regression analysis of proportional data using the simplex distribution

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  • Zhang, Peng
  • Qiu, Zhenguo
  • Shi, Chengchun

Abstract

Outcomes of continuous proportions arise in many applied areas. Such data are typically measured as percentages, rates or proportions confined in the unitary interval. In this paper, the R package simplexreg which provides dispersion model fitting of the simplex distribution is introduced to model such proportional outcomes. The maximum likelihood method and generalized estimating equations techniques are available for parameter estimation in cross-sectional and longitudinal studies, respectively. This paper presents methods and algorithms implemented in the package, including parameter estimation, model checking as well as density, cumulative distribution, quantile and random number generating functions of the simplex distribution. The package is applied to real data sets for illustration.

Suggested Citation

  • Zhang, Peng & Qiu, Zhenguo & Shi, Chengchun, 2016. "simplexreg: an R package for regression analysis of proportional data using the simplex distribution," LSE Research Online Documents on Economics 102115, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:102115
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    File URL: http://eprints.lse.ac.uk/102115/
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    References listed on IDEAS

    as
    1. Zeileis, Achim & Croissant, Yves, 2010. "Extended Model Formulas in R: Multiple Parts and Multiple Responses," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i01).
    2. Stasinopoulos, D. Mikis & Rigby, Robert A., 2007. "Generalized Additive Models for Location Scale and Shape (GAMLSS) in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i07).
    3. Zhang, Peng & Qiu, Zhenguo & Shi, Chengchun, 2016. "simplexreg: An R Package for Regression Analysis of Proportional Data Using the Simplex Distribution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i11).
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    Cited by:

    1. Abdelhakim Aknouche & Stefanos Dimitrakopoulos, 2023. "Autoregressive conditional proportion: A multiplicative‐error model for (0,1)‐valued time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(4), pages 393-417, July.
    2. Aknouche, Abdelhakim & Dimitrakopoulos, Stefanos, 2021. "Autoregressive conditional proportion: A multiplicative-error model for (0,1)-valued time series," MPRA Paper 110954, University Library of Munich, Germany, revised 06 Dec 2021.
    3. Patrícia L. Espinheira & Alisson Oliveira Silva, 2020. "Residual and influence analysis to a general class of simplex regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 523-552, June.
    4. Zhang, Peng & Qiu, Zhenguo & Shi, Chengchun, 2016. "simplexreg: An R Package for Regression Analysis of Proportional Data Using the Simplex Distribution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i11).

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    More about this item

    Keywords

    dispersion models; proportional data; R; random variable generation; simplex distribution;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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