Murray K, Heritier S, Müller S
School of Mathematics and Statistics, University of Sydney, Carslaw Building (F07), NSW 2006, Australia.
Stat Med. 2013 Nov 10;32(25):4438-51. doi: 10.1002/sim.5855. Epub 2013 May 29.
Model selection techniques have existed for many years; however, to date, simple, clear and effective methods of visualising the model building process are sparse. This article describes graphical methods that assist in the selection of models and comparison of many different selection criteria. Specifically, we describe for logistic regression, how to visualize measures of description loss and of model complexity to facilitate the model selection dilemma. We advocate the use of the bootstrap to assess the stability of selected models and to enhance our graphical tools. We demonstrate which variables are important using variable inclusion plots and show that these can be invaluable plots for the model building process. We show with two case studies how these proposed tools are useful to learn more about important variables in the data and how these tools can assist the understanding of the model building process.
模型选择技术已经存在多年;然而,迄今为止,用于可视化模型构建过程的简单、清晰且有效的方法却很少。本文介绍了有助于模型选择和多种不同选择标准比较的图形方法。具体而言,我们针对逻辑回归描述了如何可视化描述损失和模型复杂性的度量,以缓解模型选择困境。我们提倡使用自助法来评估所选模型的稳定性并增强我们的图形工具。我们使用变量包含图展示哪些变量是重要的,并表明这些图对于模型构建过程可能非常有价值。我们通过两个案例研究展示了这些提议的工具如何有助于更多地了解数据中的重要变量,以及这些工具如何辅助理解模型构建过程。