老鼠:glm.fit:算法没有收敛 [英] mice: glm.fit: algorithm did not converge

问题描述

我有一个包含大约 12 个类别变量的数据集，级别范围为 2 - 10，以及其他数值变量.约 280 条记录.我正在使用 r 中的 mice 包来使用所有默认设置对缺失的数据进行插补.但是，当我尝试进行这样的插补时:

I have a dataset with about 12 categorical variables with levels ranging from 2 - 10, as well as other numerical variables. About 280 records. I'm using the mice package in r to perform imputation on the missing data with all default settings. However, when I try to do the imputation like this:

imp <- mice(df)

我继续收到此警告:

glm.fit: algorithm did not converge

我在网上找到的解决方案 这里 和这里只关注直接使用 glm 函数，但就我而言，它是从 mice 内部调用的函数.我试过设置 maxit = 50，就像这样

The solutions I found online here and here only focus on using the glm function directly, but in my case, it's a function that's called from within mice. I've tried setting maxit = 50, like this

imp <- mice(df, maxit = 50)

但最终只会收到更多相同警告的实例.知道是什么原因造成的吗?

but only ended up getting many more instances of the same warning. Any idea what could be causing this?

推荐答案

我决定发布我的问题的答案，只是为了展示我是如何解决它的，这有点不寻常，并且鉴于没有任何解决方案我可以在我的情况下找到在线工作.

I have decided to post the answer to my question just to show how I solved it, which is a bit unusual, and given that none of the solutions I could find online worked in my case.

我意识到警告实际上来自 logreg 函数(对于只有 2 个级别的分类变量)，而不是来自 polyreg.因此，鉴于 glm.fit() 函数不仅从 mice 内部调用，而且从 logreg 内部调用，我最终找到了mice 代码在 Github，复制logreg 函数，使用 maxit 的控制参数编辑 glm.fit() 调用，将其重命名为 ?mice详细信息"部分，并使用它.工作正常(经过更多调试，哈哈)，算法现在收敛了.

I realized that the warning actually comes from the logreg function (for categorical variables with only 2 levels), rather than from polyreg. So, given that the glm.fit() function is called not just from within mice, but from within logreg, I ended up finding the mice code on Github, copying the logreg function, editing the glm.fit() call with the control parameter for maxit, renaming it as specified in the ?mice 'Details' section, and using that. Worked fine (after some more debugging, lol), and the algorithm now converges.

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