R，mitools::MIcombine，没有 p 值的原因是什么？
我目前正在 R 中运行一个带有 5 个乘法插补数据集的简单线性回归模型。
例如 model <- with(imp, lm(outcome ~ Exposure))
要汇集汇总估计，我可以使用以下命令来自 mitools 包的 summary(mitools::MIcombine(model)) 。然而，这并没有给出 p 值的结果。我还可以使用 mouse 包中的命令 summary(pool(model)) ，这确实会给出 p 值的结果。
因此，我想知道 MIcombine 不产生 p 值是否有具体原因？
I am currently running a simple linear regression model with 5 multiply imputed datasets in R.
E.g. model <- with(imp, lm(outcome ~ exposure))
To pool the summary estimates I could use the command summary(mitools::MIcombine(model)) from the mitools package. However, this does not give results for p-values. I could also use the command summary(pool(model)) from the mice package and this does give results for p-values.
Because of this, I am wondering if there is a specific reason why MIcombine does not produce p-values?
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浏览完文档后，mitools 库不提供 p 值似乎没有特殊原因。尽管如此，该包的重点是插补，而不是模型结果。
但是，您不需要这些软件包中的任何一个来查看结果以及每个模型的 p 值。我开始将其写为评论，但决定包含代码。如果您不知道...您可以使用基本 R 的
摘要。我意识到mice的输出是比较性的，mitools也是如此。我认为提及这一点也很重要。如果您的调用的输出是
model，那么这将起作用。After looking through the documentation, it doesn't seem like there is a particular reason that the
mitoolslibrary doesn't provide p-values. Although, the package's focus is on imputation, not model results.However, you don't need either of these packages to see your results–along with the per model p-values. I started writing this as a comment but decided to include the code. If you weren't aware...you can use base R's
summary. I realize that the output ofmiceis comparative, as ismitools. I thought it was important enough to mention this, as well.If the output of your call is
model, then this will work.