Speaker
Description
Simulation studies allow comparisons of performance between statistical methods to be made. Tables are traditionally used to report study results, which are usually performance measures such as bias, empirical standard error, average model standard error and coverage. In large simulation studies, these tables of results may become too large for patterns to be readily identified. This occurs when many performance measures are assessed; many combinations of simulation parameters are evaluated, and many statistical methods are evaluated.
In this talk, we will introduce the multi-performance plot, a new way of compactly visualising the results from large simulation studies for easier pattern identification. We will describe how the plot can display up to seven performance measures comparing multiple statistical methods. In R, we will demonstrate with examples the implementation of the multi-performance plot, in addition to (i) its integration with the rsimsum package for simulation studies, and (ii) a Shiny app for the multi-performance plot.
Additional Material or Paper
Shiny app released on 27 February 2026: https://poklo.shinyapps.io/MultiPerformancePlot_1-0/
Details (including guidance document) published on GitHub on 5 March 2026: https://github.com/wpoklo/Multi-Performance-Plot/tree/main
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| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | simulation studies, data visualisation, plot, Shiny |
|---|---|
| Virtual Option | This submission is for onsite presentation only |
| Material License | CC-BY 4.0 |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |