在ggplot中可视化二进制逻辑模型的双向交互的边际效应 [英] Visualizing marginal effect of two-way interaction of binary logistic model in ggplot

问题描述

有人可以建议使用ggplot快速直观地了解glm模型/二进制logistic回归模型的双向交互吗?我对边际效应感兴趣！

Can someone suggest a quick and comprehensible way of how to visualize a two-way interaction of a glm model/ binary logistic regression model, using ggplot? I'm interested in the marginal effect!

我看了其他帖子，但并没有真正理解它们.另一个问题是由于R版本(3.4.2)，我无法使用ggpredict/gginteraction.

I have looked at other posts, but did not really understand them. Another issue is that I cannot use ggpredict/ gginteraction because of by R-version (3.4.2).

我的数据结构如下所示(简化):

My data structure looks like this (simplified):

region_AB   motive   voter_attribute  vote_for_party_XY
1           1        1                1
1           0        1                1
1           1        0                0
0           0        0                0
0           0        1                0
0           1        0                0

我声称(并实际上发现)该地区在调解给定动机对XY政党的投票产生影响.

And I'm claiming (and actually finding) that there region mediates the effect of a given motive on voting for party XY.

现在我知道这不是可复制的示例.但是也许有人可以提出一个适合所有人的解决方案(至少对于glm模型的双向交互而言).如果有必要且有帮助，也许mtcars数据集可以用作示例目的:甚至是使用此数据集的互动条件模型的示例.

Now I know this is not a reproducible example. But maybe someone can come up with a one fits all solution (at least for the case of two-way interactions of glm models). If necessary and it helps, maybe the mtcars dataset can serve examplary purposes: there's even an example for an interaction-term model using this dataset.

我希望有人对此有一个很好的解决方案.这可能是可视化双向交互的边际效应的一般指南...

I'm hoping someone has a nice and easy solution to this. This could be a general guide for visualizing marginal effects of two-way interactions...

推荐答案

您可以使用 ggeffects -package 来计算边际效应.返回值是一个数据帧，但是有一个plot()方法可以创建/返回ggplot对象.这是一个带有二进制结果的人工示例，但是您可以从上面引用的网站的文章"中找到更多详细信息.

You can use the ggeffects-package to compute marginal effects. The return value is a data frame, but there's a plot()-method that creates/returns a ggplot-object. Here'a an artificial example with binary outcome, but you can find more details in the "Articles" from the above referenced website.

library(ggeffects)
library(sjmisc) # to preserve labels
data(efc)

# prepare data, create binary outcome and make
# numeric variables categorical
efc$neg_c_7d <- dicho(efc$neg_c_7)
efc$c161sex <- to_factor(efc$c161sex)
efc$c172code <- to_factor(efc$c172code)

# fit logistic regression
m <- glm(
  neg_c_7d ~ c12hour + c161sex * c172code,
  data = efc,
  family = binomial(link = "logit")
)

# compute and plot marginal effects
ggpredict(m, c("c172code", "c161sex")) %>% plot()

请注意，我使用的数据集已标记，这就是为什么要对轴进行注释的原因带有适当"的值和变量标签.

Note that the dataset I used is labelled, that's why the axes are annotated with "proper" value and variable labels.

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