Partial Residual Plots in Generalized Linear Models
本文探讨了偏残差图在广义线性模型中用于可视化选定预测变量曲率的结构和实用性，并对比了ceres图的表现，指出其有效性受模型、链接函数和预测变量随机行为限制。
Abstract In this article we explore the structure and usefulness of partial residual plots as tools for visualizing curvature as a function of selected predictors x 2 in a generalized linear model (GLM), where the vector of predictors x is partitioned as x T = (x T 1, x T 2). The GLM extension of ceres plots is discussed, but to a lesser extent. The usefulness of these plots for obtaining a good visual impression of curvature may be limited by the specified GLM, the link function, and the stochastic behavior of the predictors. Partial residual plots seem to work well when modeling is in a region where the conditional mean of the response given x stays well away from its extremes so that the link is essentially linear, and E(x 1 | x 2) is linear in x 2. ceres plots, however, require only the first condition. The behavior of these plots is contrasted with their behavior in additive-error models.