Simulation of Synthetic Complex Data: The R Package simPop. The production of synthetic datasets has been proposed as a statistical disclosure control solution to generate public use files out of protected data, and as a tool to create ”augmented datasets” to serve as input for micro-simulation models. Synthetic data have become an important instrument for ex-ante assessments of policy impact. The performance and acceptability of such a tool relies heavily on the quality of the synthetic populations, i.e., on the statistical similarity between the synthetic and the true population of interest. Multiple approaches and tools have been developed to generate synthetic data. These approaches can be categorized into three main groups: synthetic reconstruction, combinatorial optimization, and model-based generation. We provide in this paper a brief overview of these approaches, and introduce simPop, an open source data synthesizer. simPop is a user-friendly R package based on a modular object-oriented concept. It provides a highly optimized S4 class implementation of various methods, including calibration by iterative proportional fitting and simulated annealing, and modeling or data fusion by logistic regression. We demonstrate the use of simPop by creating a synthetic population of Austria, and report on the utility of the resulting data. We conclude with suggestions for further development of the package.
Keywords for this software
References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- Hoshino, Nobuaki: A firm foundation for statistical disclosure control (2020)
- Matthias Speidel, Jörg Drechsler, Shahab Jolani: The R Package hmi: A Convenient Tool for Hierarchical Multiple Imputation and Beyond (2020) not zbMATH
- Matthias Templ and Bernhard Meindl and Alexander Kowarik and Olivier Dupriez: Simulation of Synthetic Complex Data: The R Package simPop (2017) not zbMATH
- Templ, Matthias: Statistical disclosure control for microdata. Methods and applications in R (2017)
- Templ, M.; Hron, K.; Filzmoser, P.: Exploratory tools for outlier detection in compositional data with structural zeros (2017)
- Beata Nowok and Gillian Raab and Chris Dibben: synthpop: Bespoke Creation of Synthetic Data in R (2016) not zbMATH