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Model selection versus traditional hypothesis testing in circular statistics: a simulation study
Lukas Landler1,2, Graeme D Ruxton3, E Pascal Malkemper4
1Institute of Zoology, Department of Integrative Biology and Biodiversity Research, University of Natural Resources and Life Sciences Vienna, Gregor-Mendel-Strasse 33, A-1180 Vienna, Austria lukas.landler@boku.ac.at.
Biology Open
|June 20, 2020
Summary
Statistical analysis of circular data, common in biology, requires specialized methods. Model-based inference shows promise comparable to traditional tests like the Rayleigh test for circular data, but requires careful error rate control.
Area of Science:
- Biology
- Statistics
- Data Science
Background:
- Biological studies frequently involve data measured on a circular scale, necessitating distinct statistical approaches compared to linear data.
- The conventional method for analyzing circular data is the Rayleigh test, which assesses uniform distribution against aggregation.
- Model-fitting approaches, standard for linear data, are increasingly advocated for circular data analysis.
Purpose of the Study:
- To evaluate the performance of model-based inference for circular data analysis.
- To compare model-fitting approaches with traditional statistical tests for circular data.
- To investigate the conditions under which model-based inference provides reliable results for circular data.
Main Methods:
- Utilized simulation data to assess statistical methods for circular data.
- Compared model-fitting approaches with established tests, such as the Rayleigh test.
- Investigated the impact of type I error rate control on model-based inference performance.
Main Results:
- Simulation data indicate that model-based inference can achieve performance similar to traditional tests for circular data.
- The effectiveness of model-based inference is contingent upon appropriate adjustments for controlling the type I error rate.
- Type I error rate control is crucial for ensuring the reliability of model-based inference in circular data analysis.
Conclusions:
- Model-based inference presents a viable alternative for analyzing circular data in biological studies.
- Careful control of the type I error rate is essential for the successful application of model-based inference to circular data.
- This study highlights the potential of model-fitting approaches to complement or replace traditional methods for circular data analysis.
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