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An Object-Oriented Framework for Statistical Simulation: The R Package simFrame

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  • Alfons, Andreas
  • Templ, Matthias
  • Filzmoser, Peter

Abstract

Simulation studies are widely used by statisticians to gain insight into the quality of developed methods. Usually some guidelines regarding, e.g., simulation designs, contamination, missing data models or evaluation criteria are necessary in order to draw meaningful conclusions. The R package simFrame is an object-oriented framework for statistical simulation, which allows researchers to make use of a wide range of simulation designs with a minimal effort of programming. Its object-oriented implementation provides clear interfaces for extensions by the user. Since statistical simulation is an embarrassingly parallel process, the framework supports parallel computing to increase computational performance. Furthermore, an appropriate plot method is selected automatically depending on the structure of the simulation results. In this paper, the implementation of simFrame is discussed in great detail and the functionality of the framework is demonstrated in examples for different simulation designs.

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  • Alfons, Andreas & Templ, Matthias & Filzmoser, Peter, 2010. "An Object-Oriented Framework for Statistical Simulation: The R Package simFrame," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 37(i03).
    • Handle: RePEc:jss:jstsof:v:037:i03
    DOI: http://hdl.handle.net/10.18637/jss.v037.i03
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    References listed on IDEAS

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    1. Andreas Alfons & Stefan Kraft & Matthias Templ & Peter Filzmoser, 2011. "Simulation of close-to-reality population data for household surveys with application to EU-SILC," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(3), pages 383-407, August.
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    Cited by:

    1. Kreutzmann, Ann-Kristin & Pannier, Sören & Rojas-Perilla, Natalia & Schmid, Timo & Templ, Matthias & Tzavidis, Nikos, 2017. "The R package emdi for estimating and mapping regionally disaggregated indicators," Discussion Papers 2017/15, Free University Berlin, School of Business & Economics.
    2. Hofert, Marius & Mächler, Martin, 2016. "Parallel and Other Simulations in R Made Easy: An End-to-End Study," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(i04).
    3. Andrey Ziyatdinov & Alexandre Perera-Lluna, 2014. "Data Simulation in Machine Olfaction with the R Package Chemosensors," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-19, February.
    4. Kowarik, Alexander & Templ, Matthias, 2016. "Imputation with the R Package VIM," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i07).
    5. Alfons, Andreas & Templ, Matthias, 2013. "Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i15).
    6. Fabrizi, Enrico & Trivisano, Carlo, 2016. "Small area estimation of the Gini concentration coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 223-234.
    7. Silvia De Nicol`o & Maria Rosaria Ferrante & Silvia Pacei, 2021. "Mind the Income Gap: Bias Correction of Inequality Estimators in Small-Sized Samples," Papers 2107.08950, arXiv.org, revised May 2023.
    8. Antonio Arcos & Maria del Mar Rueda & Sara Pasadas-del-Amo, 2020. "Treating Nonresponse in Probability-Based Online Panels through Calibration: Empirical Evidence from a Survey of Political Decision-Making Procedures," Mathematics, MDPI, vol. 8(3), pages 1-16, March.
    9. Alfons, Andreas & Croux, Christophe & Gelper, Sarah, 2016. "Robust groupwise least angle regression," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 421-435.
    10. Zimmermann Thomas & Münnich Ralf Thomas, 2018. "Small Area Estimation with a Lognormal Mixed Model under Informative Sampling," Journal of Official Statistics, Sciendo, vol. 34(2), pages 523-542, June.

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