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ReGenesees: an Advanced R System for Calibration, Estimation and Sampling Error Assessment in Complex Sample Surveys

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  • Zardetto Diego

    (Istat – Italian National Institute of Statistics, Via Cesare Balbo, 16 Rome, Rome 00184, Italy)

Abstract

ReGenesees is a new software system for design-based and model-assisted analysis of complex sample surveys, based on R. As compared to traditional estimation platforms, it ensures easier and safer usage and achieves a dramatic reduction in user workload for both the calibration and the variance estimation tasks. Indeed, ReGenesees allows the specification of calibration models in a symbolic way, using R model formulae. Driven by this symbolic metadata, the system automatically and transparently generates the right values and formats for the auxiliary variables at the sample level, and assists the user in defining and calculating the corresponding population totals. Moreover, ReGenesees can handle arbitrary complex estimators, provided they can be expressed as differentiable functions of Horvitz-Thompson or calibration estimators of totals. Complex estimators can be defined in a completely free fashion: the user only needs to provide the system with the symbolic expression of the estimator as a mathematical function. ReGenesees is in fact able to automatically linearize such complex estimators, so that the estimation of their variance comes at no cost at all to the user. Remarkably, all the innovative features sketched above leverage a particular strong point of the R programming language, namely its ability to process symbolic information.

Suggested Citation

  • Zardetto Diego, 2015. "ReGenesees: an Advanced R System for Calibration, Estimation and Sampling Error Assessment in Complex Sample Surveys," Journal of Official Statistics, Sciendo, vol. 31(2), pages 177-203, June.
    • Handle: RePEc:vrs:offsta:v:31:y:2015:i:2:p:177-203:n:3
    DOI: 10.1515/jos-2015-0013
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    References listed on IDEAS

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    1. Lumley, Thomas, 2004. "Analysis of Complex Survey Samples," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 9(i08).
    2. G. N. Wilkinson & C. E. Rogers, 1973. "Symbolic Description of Factorial Models for Analysis of Variance," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 22(3), pages 392-399, November.
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