逻辑回归缺失值
我可以进行带有缺失值的逻辑回归吗？
我有很多连续属性和一些分类属性，我可以将它们设置为用户缺失吗？有用吗？
Could I have a logistic regression with missing values?
I have many continuos attributes and some categorical, could I set them as user-missing? Could it be useful?
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为了进行回归分析，您需要为每个事件测量所有变量。也许另一种技术可以处理缺失的属性，但不能处理回归。
发布问题！
顺便说一句，您应该尝试在 https://stats.stackexchange.com/ HTH
For doing a regression analysis you need all variables measured for each event. Perhaps another technique works with missing attributes, but not regression.
BTW, you should try posting the question at https://stats.stackexchange.com/
HTH!
大多数回归过程都需要完整的数据，但有多种方法可以处理缺失值。这是一个微妙的话题，所以我不会假装在这里给出完整的答案，并建议阅读一些关于该主题的文章。但简而言之：
要了解有关此主题的更多信息，请寻求有关术语“插补”的信息，尤其是“单次插补”和“多重插补”、“随机缺失”和“完全随机缺失”。
Most regression procedures require complete data, but there are a variety of methods for dealing with missing values. This is a subtle topic, so I won't pretend to give a complete answer here, and recommend doing some reading on the subject. Briefly, though:
To learn more about this subject, seek information on the terms "imputation", especially "single imputation" and "multiple imputation", "missing at random" and "missing completely at random".