Statistical Quantification of Individual Differences (SQuID): an educational and statistical tool for understanding multilevel phenotypic data in linear mixed models
Statistical Quantification of Individual Differences (SQuID): an educational and statistical tool for understanding multilevel phenotypic data in linear mixed models
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DOI：
10.1111/2041-210x.12659
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发表时间：
2017-02-01
影响因子：
6.6
通讯作者：
Westneat, David F.
中科院分区：
文献类型：
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作者：
Allegue, Hassen;Araya-Ajoy, Yimen G.;Westneat, David F.
1.表型变异存在于生物组织的各个层面：变异存在于物种之间、种群内个体之间，以及种群内能力性状、个体内部。混合效应模型是量化性状多层次测量的理想工具，并且越来越多地用于进化生态学。混合效应模型相对复杂，两个主要问题可能阻碍其正确使用：（i）可用于教导新用户如何实施和解释它们的教育资源相对较少；（ii）缺乏确保正确估计感兴趣的统计参数的工具。在本文中，我们介绍了个体差异统计量化（SQuID），这是一种基于模拟的工具，可用于研究和教育目的。 SQuID 创建了一个虚拟世界，其中的受试者的表型是由用户定义的表型方程生成的，这可以轻松地将生物学假设转化为可量化的参数。个体差异的统计量化目前使用线性预测变量对正态分布特征进行建模，但 SQuID 还需要进一步开发，并将在未来适应处理更复杂的场景。当前的框架适合进行模拟研究，确定用户特定生物问题的最佳抽样设计，并进行基于模拟的推论以帮助解释实证研究。对于有兴趣学习或教导他人在研究引起表型变异的过程时如何实施和解释线性混合效应模型的生物学家来说，个体差异的统计量化也是一种教学工具。基于界面的模块允许用户了解这些问题。随着对采样设计效果的研究继续，新的问题将在新的模块中实现，包括非线性和非高斯数据。
1. Phenotypic variation exists in and at all levels of biological organization: variation exists among species, among-individuals within-populations, and in the case of l within-populations abile traits, within-individuals. Mixed-effects models represent ideal tools to quantify multilevel measurements of traits and are being increasingly used in evolutionary ecology. Mixed-effects models are relatively complex, and two main issues may be hampering their proper usage: (i) the relatively few educational resources available to teach new users how to implement and interpret them and (ii) the lack of tools to ensure that the statistical parameters of interest are correctly estimated. In this paper, we introduce Statistical Quantification of Individual Differences (SQuID), a simulation-based tool that can be used for research and educational purposes. SQuID creates a virtual world inhabited by subjects whose phenotypes are generated by a user-defined phenotypic equation, which allows easy translation of biological hypotheses into quantifiable parameters. Statistical Quantification of Individual Differences currently models normally distributed traits with linear predictors, but SQuID is subject to further development and will adapt to handle more complex scenarios in the future. The current framework is suitable for performing simulation studies, determining optimal sampling designs for user-specific biological problems and making simulation-based inferences to aid in the interpretation of empirical studies. Statistical Quantification of Individual Differences is also a teaching tool for biologists interested in learning, or teaching others, how to implement and interpret linear mixed-effects models when studying the processes causing phenotypic variation. Interface-based modules allow users to learn about these issues. As research on effects of sampling designs continues, new issues will be implemented in new modules, including nonlinear and non-Gaussian data.