Serrien Ben, Goossens Maggy, Baeyens Jean-Pierre
Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel, Brussels, Belgium.
Faculty of Applied Engineering, Universiteit Antwerpen, Antwerp, Belgium.
Int Biomech. 2019 Dec;6(1):9-18. doi: 10.1080/23335432.2019.1597643.
Recent developments in Statistical Parametric Mapping (SPM) for continuum data (e.g. kinematic time series) have been adopted by the biomechanics research community with great interest. The Python/MATLAB package spm1d developed by T. Pataky has introduced SPM into the biomechanical literature, adapted originally from neuroimaging. The package already allows many of the statistical analyses common in biomechanics from a frequentist perspective. In this paper, we propose an application of Bayesian analogs of SPM based on Bayes factors and posterior probability with default priors using the BayesFactor package in R. Results are provided for two typical designs (two-sample and paired sample -tests) and compared to classical SPM results, but more complex standard designs are possible in both classical and Bayesian frameworks. The advantages of Bayesian analyses in general and specifically for SPM are discussed. Scripts of the analyses are available as supplementary materials.
统计参数映射（SPM）在连续数据（如运动学时间序列）方面的最新进展引起了生物力学研究界的极大兴趣。T. Pataky开发的Python/MATLAB软件包spm1d已将SPM引入生物力学文献，该软件包最初改编自神经影像学。从频率主义的角度来看，该软件包已经可以进行生物力学中许多常见的统计分析。在本文中，我们提出了基于贝叶斯因子和具有默认先验的后验概率的SPM贝叶斯类似物的应用，使用R中的BayesFactor软件包。给出了两种典型设计（双样本和配对样本t检验）的结果，并与经典SPM结果进行了比较，但在经典和贝叶斯框架中都可以进行更复杂的标准设计。讨论了一般贝叶斯分析特别是SPM贝叶斯分析的优点。分析脚本作为补充材料提供。
