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 <b>simFrame</b> 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 <b>simFrame</b> is discussed in great detail and the functionality of the framework is demonstrated in examples for different simulation designs.
Highlights
Due to the complexity of modern statistical methods, obtaining analytical results about their properties is often virtually impossible
A key feature is that statisticians can make use of a wide range of simulation designs with a minimal effort of programming
Statistical simulation in R (R Development Core Team 2010) is often done using bespoke use-once-and-throw-away scripts, which is perfectly fine when only a handful of simulation studies need to be done for a specific purpose such as a paper
Summary
Due to the complexity of modern statistical methods, obtaining analytical results about their properties is often virtually impossible. Simulation studies are widely used by statisticians as data-based, computer-intensive alternatives for gaining insight into the quality of developed methods. Research projects commonly involve many scientists, often from different institutions, each focusing on different aspects of the project. If these researchers use different simulation designs, the results may be incomparable, which in turn makes it impossible to draw meaningful conclusions. Its implementation follows an object-oriented approach based on S4 classes and methods (Chambers 1998, 2008). The object-oriented implementation gives maximum control over input and output, while at the same time providing clear interfaces for extensions by user-defined classes and methods
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