Abstract In the last two decades, the growth of computational resources has made it possible to handle generalized additive models (GAMs) that formerly were too costly for serious applications. However, the growth in model complexity has not been matched by improved visualizations for model development and results presentation. Motivated by an industrial application in electricity load forecasting, we identify the areas where the lack of modern visualization tools for GAMs is particularly severe, and we address the shortcomings of existing methods by proposing a set of visual tools that (a) are fast enough for interactive use, (b) exploit the additive structure of GAMs, (c) scale to large data sets, and (d) can be used in conjunction with a wide range of response distributions. The new visual methods proposed here are implemented by the mgcViz R package, available on the Comprehensive R Archive Network. Supplementary materials for this article are available online.

中文翻译：

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现代广义加性模型的可扩展可视化方法

摘要 在过去的二十年中，计算资源的增长使得处理以前对于严肃应用来说成本太高的广义加性模型 (GAM) 成为可能。然而，模型开发和结果呈现的改进可视化并没有与模型复杂性的增长相匹配。受电力负荷预测中的工业应用的启发，我们确定了缺乏现代 GAM 可视化工具特别严重的领域，并通过提出一组可视化工具来解决现有方法的缺点，这些工具 (a) 足够快交互式使用，(b) 利用 GAM 的加法结构，(c) 扩展到大型数据集，以及 (d) 可以与广泛的响应分布结合使用。此处提出的新视觉方法由综合 R 存档网络上的 mgcViz R 包实现。本文的补充材料可在线获取。