Several authors have recommended adopting the receiver operator characteristic (ROC) area under the curve (AUC) or mean ridit as an effect size, arguing that it measures an important and interpretable type of effect that conventional effect-size measures do not. It is base-rate insensitive, robust to outliers, and invariant under order-preserving transformations. However, applications have been limited to group comparisons, and usually just two groups, in line with the popular interpretation of the AUC as measuring the probability that a randomly chosen case from one group will score higher on the dependent variable than a randomly chosen case from another group. This tutorial article shows that the AUC can be used as an effect size for both categorical and continuous predictors in a wide variety of general linear models, whose dependent variables may be ordinal, interval, or ratio level. Thus, the AUC is a general effect-size measure. Demonstrations in this article include linear regression, ordinal logistic regression, gamma regression, and beta regression. The online supplemental materials to this tutorial provide a survey of currently available software resources in R for the AUC and ridits, along with the code and access to the data used in the examples. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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曲线下的受试者工作特征面积（或平均 Ridit）作为效应大小。

一些作者建议采用接受者操作特征（ROC）曲线下面积（AUC）或平均ridit作为效果大小，认为它测量了传统效果大小测量所不能测量的重要且可解释的效果类型。它对基本速率不敏感，对异常值具有鲁棒性，并且在保序变换下保持不变。然而，应用仅限于组比较，并且通常仅限于两组，这与 AUC 的流行解释一致，即衡量从一组中随机选择的案例在因变量上得分高于从一组中随机选择的案例的概率。另一组。本教程文章表明，AUC 可用作各种一般线性模型中分类和连续预测变量的效应大小，其因变量可以是序数、区间或比率水平。因此，AUC 是一种通用的效应大小度量。本文中的演示包括线性回归、序数逻辑回归、伽玛回归和贝塔回归。本教程的在线补充材料提供了对 R 中当前可用的 AUC 和 rodits 软件资源的调查，以及示例中使用的代码和数据访问。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。本教程的在线补充材料提供了对 R 中当前可用的 AUC 和 rodits 软件资源的调查，以及示例中使用的代码和数据访问。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。本教程的在线补充材料提供了对 R 中当前可用的 AUC 和 rodits 软件资源的调查，以及示例中使用的代码和数据访问。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。