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告别平淡的回归报告:三种可视化线性模型的森林图变化。

Say farewell to bland regression reporting: Three forest plot variations for visualizing linear models.

机构信息

Department of Developmental and Educational Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria.

出版信息

PLoS One. 2024 Feb 2;19(2):e0297033. doi: 10.1371/journal.pone.0297033. eCollection 2024.

Abstract

Regression ranks among the most popular statistical analysis methods across many research areas, including psychology. Typically, regression coefficients are displayed in tables. While this mode of presentation is information-dense, extensive tables can be cumbersome to read and difficult to interpret. Here, we introduce three novel visualizations for reporting regression results. Our methods allow researchers to arrange large numbers of regression models in a single plot. Using regression results from real-world as well as simulated data, we demonstrate the transformations which are necessary to produce the required data structure and how to subsequently plot the results. The proposed methods provide visually appealing ways to report regression results efficiently and intuitively. Potential applications range from visual screening in the model selection stage to formal reporting in research papers. The procedure is fully reproducible using the provided code and can be executed via free-of-charge, open-source software routines in R.

摘要

回归分析在包括心理学在内的许多研究领域中是最受欢迎的统计分析方法之一。通常,回归系数会在表格中显示。虽然这种表示模式信息密度很高,但大量的表格可能难以阅读和解释。在这里,我们介绍了三种用于报告回归结果的新的可视化方法。我们的方法允许研究人员在单个图中排列大量的回归模型。使用来自真实数据和模拟数据的回归结果,我们演示了产生所需数据结构所需的转换,以及如何随后绘制结果。所提出的方法提供了高效和直观地报告回归结果的视觉上吸引人的方式。潜在的应用范围从模型选择阶段的可视化筛选到研究论文中的正式报告。该过程可以使用提供的代码完全重现,并可以通过 R 中的免费开源软件例程执行。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10c5/10836698/5779457cbd35/pone.0297033.g001.jpg

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