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当我们使用binary dependent model时，怎样对结果进行解释是很头痛的事，该教程可以告诉你怎样通过stata软件图示出自变量对因变量的主效应、边际效应等，利用此项功能，将使你的logit回归分析比一般人上几个层次！
This paper considers the role of covariates when using predicted probabilities to interpret main effects and interactions in logit models. While predicted probabilities are very intuitive for interpreting main effects and interactions, the pattern of results depends on the contribution of covariates. We introduce a concept called the covariate contribution, which reflects the aggregate contribution of all of the remaining predictors (covariates) in the model and a family of tools to help visualize the relationship between predictors and the predicted probabilities across a variety of covariate contributions. We believe this strategy and the accompanying tools can help researchers who wish to use predicted probabilities as an interpretive framework for logit models acquire and present a more comprehensive interpretation of their results. These visualization tools could be extended to other models (such as binary probit, multinomial logistic, ordinal logistic models, and other nonlinear models).
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[此贴子已经被作者于2005-5-13 23:28:11编辑过]
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