Measures of Fit for Logistic Regression
Measures of Fit for Logistic Regression
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发表时间：
2014
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通讯作者：
P. Allison
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作者：
P. Allison
关于逻辑回归最常见的问题之一是“我如何知道我的模型是否适合数据？”有很多方法可以回答这个问题，但它们通常分为两类：预测能力的测量（如R平方）和拟合优度检验（如皮尔逊卡方）。本演示首先介绍R方度量，认为PROC LOGISTIC报告的可选R方可能不是最佳的。McFadden和Tjur提出的措施似乎更有吸引力。至于拟合优度，流行的Hosmer和Lemeshow检验被证明有一些严重的问题。考虑了几种替代方案。
One of the most common questions about logistic regression is “How do I know if my model fits the data?” There are many approaches to answering this question, but they generally fall into two categories: measures of predictive power (like R-square) and goodness of fit tests (like the Pearson chi-square). This presentation looks first at R-square measures, arguing that the optional R-squares reported by PROC LOGISTIC might not be optimal. Measures proposed by McFadden and Tjur appear to be more attractive. As for goodness of fit, the popular Hosmer and Lemeshow test is shown to have some serious problems. Several alternatives are considered.