Question

我想制作一个交互图，从回归模型的结果中直观地显示分类变量（4个级别）和标准化连续变量的相互作用斜率的差异或相似性。

with(GLMModel, interaction.plot(continuous.var, categorical.var, response.var)) 不是我想要的。它产生一个图，其中斜率随连续变量的每个值而变化。我正在寻找一个具有恒定斜率的图，如下图所示：

enter image description here

有什么想法吗？

我符合fit<-glmer(resp.var ~ cont.var*cat.var + (1|rand.eff) , data = sample.data , poisson)形式的模型以下是一些示例数据：

structure(list(cat.var = structure(c(4L, 4L, 1L, 4L, 1L, 2L, 
1L, 1L, 1L, 1L, 4L, 1L, 1L, 3L, 2L, 4L, 1L, 1L, 1L, 2L, 1L, 2L, 
2L, 1L, 3L, 1L, 1L, 2L, 4L, 1L, 2L, 1L, 1L, 4L, 1L, 3L, 1L, 3L, 
3L, 4L, 3L, 4L, 1L, 3L, 3L, 1L, 2L, 3L, 4L, 3L, 4L, 2L, 1L, 1L, 
4L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 1L, 4L, 4L, 3L, 3L, 1L, 3L, 3L, 
3L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 4L, 1L, 3L, 4L, 1L, 1L, 4L, 
1L, 3L, 1L, 1L, 3L, 2L, 4L, 1L, 4L, 1L, 4L, 4L, 4L, 4L, 2L, 4L, 
4L, 1L, 2L, 1L, 4L, 3L, 1L, 1L, 3L, 2L, 4L, 4L, 1L, 4L, 1L, 3L, 
2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 4L, 1L, 
2L, 2L, 1L, 1L, 2L, 3L, 1L, 4L, 4L, 4L, 1L, 4L, 4L, 3L, 2L, 4L, 
1L, 3L, 1L, 1L, 4L, 4L, 2L, 4L, 1L, 1L, 3L, 4L, 2L, 1L, 3L, 3L, 
4L, 3L, 2L, 3L, 1L, 4L, 2L, 2L, 1L, 4L, 1L, 2L, 3L, 4L, 1L, 4L, 
2L, 1L, 3L, 3L, 3L, 4L, 1L, 1L, 1L, 3L, 1L, 3L, 4L, 2L, 1L, 4L, 
1L, 1L, 1L, 2L, 1L, 1L, 4L, 1L, 3L, 1L, 2L, 1L, 4L, 1L, 2L, 4L, 
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 3L, 4L, 1L, 4L, 3L, 
3L, 3L, 4L, 1L, 3L, 1L, 1L, 4L, 4L, 4L, 4L, 2L, 1L, 1L, 3L, 2L, 
1L, 4L, 4L, 2L, 4L, 2L, 4L, 1L, 3L, 4L, 1L, 1L, 2L, 3L, 2L, 4L, 
1L, 1L, 3L, 4L, 2L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 4L, 1L, 4L, 
2L, 4L, 3L, 4L, 2L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 1L, 4L, 4L, 1L, 
4L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 3L, 2L, 2L, 1L, 1L, 4L, 
1L, 4L, 3L, 1L, 2L, 1L, 4L, 2L, 4L, 4L, 1L, 2L, 1L, 1L, 1L, 4L, 
1L, 4L, 1L, 2L, 1L, 3L, 1L, 3L, 3L, 1L, 1L, 4L, 3L, 1L, 4L, 1L, 
2L, 4L, 1L, 1L, 3L, 3L, 2L, 4L, 4L, 1L, 1L, 2L, 2L, 1L, 2L, 4L, 
3L, 4L, 4L, 4L, 4L, 1L, 3L, 1L, 2L, 2L, 2L, 4L, 2L, 3L, 4L, 1L, 
3L, 2L, 2L, 1L, 1L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 3L, 2L, 1L, 1L, 
1L, 1L, 2L, 1L, 1L, 1L, 4L, 4L, 4L, 3L, 3L, 2L, 1L, 3L, 2L, 1L, 
1L, 1L, 4L, 1L, 1L, 2L, 3L, 1L, 1L, 2L, 4L, 3L, 2L, 4L, 3L, 2L, 
1L, 3L, 1L, 3L, 1L, 4L, 3L, 1L, 4L, 4L, 2L, 4L, 1L, 1L, 2L, 4L, 
4L, 2L, 3L, 4L, 4L, 3L, 1L, 4L, 1L, 2L, 4L, 1L, 1L, 4L, 1L, 1L, 
1L, 1L, 1L, 3L, 4L, 1L, 4L, 4L, 2L, 2L, 2L, 2L, 3L, 4L, 4L, 1L, 
1L, 4L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 4L, 2L, 
3L, 1L, 1L, 1L, 4L, 1L, 1L, 4L, 4L, 4L, 1L, 1L, 1L, 1L), .Label = c("A", 
"B", "C", "D"), class = "factor"), cont.var = c(-0.0682900527296927, 
0.546320421837542, -0.273160210918771, -0.887770685486005, 0.136580105459385, 
0.75119058002662, 0.546320421837542, -0.273160210918771, -0.682900527296927, 
0.136580105459385, 0.75119058002662, 0.75119058002662, 0.75119058002662, 
0.341450263648464, 0.75119058002662, 0.546320421837542, 0.546320421837542, 
-0.478030369107849, -0.478030369107849, -0.682900527296927, -0.682900527296927, 
0.546320421837542, -0.478030369107849, -0.0682900527296927, 0.136580105459385, 
0.136580105459385, 0.75119058002662, -0.478030369107849, 0.75119058002662, 
-0.887770685486005, 0.136580105459385, -0.478030369107849, 0.341450263648464, 
-0.682900527296927, -0.478030369107849, 0.341450263648464, -0.478030369107849, 
0.546320421837542, 0.75119058002662, -0.478030369107849, -0.273160210918771, 
0.546320421837542, -0.682900527296927, 0.75119058002662, -0.478030369107849, 
-0.887770685486005, 0.136580105459385, -0.887770685486005, -0.0682900527296927, 
-0.478030369107849, 0.546320421837542, 0.75119058002662, 0.136580105459385, 
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0.136580105459385, -0.273160210918771, -0.273160210918771, 0.136580105459385, 
0.136580105459385, 0.341450263648464, 0.136580105459385, -0.273160210918771, 
-0.273160210918771, -0.682900527296927, -0.887770685486005, -0.0682900527296927, 
0.136580105459385, -0.0682900527296927, -0.273160210918771, -0.273160210918771, 
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0.75119058002662, 0.546320421837542, 0.75119058002662, 0.75119058002662, 
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0.136580105459385, -0.0682900527296927, -0.478030369107849, -0.0682900527296927, 
-0.0682900527296927, 0.546320421837542, -0.273160210918771, 0.75119058002662, 
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-0.682900527296927, -0.478030369107849, -0.478030369107849, -0.682900527296927, 
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-0.478030369107849, 0.546320421837542, 0.546320421837542, 0.546320421837542, 
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0.136580105459385, 0.136580105459385, -0.478030369107849, -0.273160210918771, 
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-0.0682900527296927, 0.136580105459385, 0.546320421837542, -0.478030369107849, 
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-0.0682900527296927, 0.341450263648464, 0.546320421837542, -0.0682900527296927, 
0.136580105459385, -0.478030369107849, 0.75119058002662, -0.478030369107849, 
-0.682900527296927, -0.478030369107849, 0.136580105459385, -0.273160210918771, 
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0.546320421837542, -0.0682900527296927, 0.75119058002662, -0.273160210918771, 
0.546320421837542, 0.341450263648464, -0.0682900527296927, -0.0682900527296927, 
-0.0682900527296927, -0.887770685486005, 0.136580105459385, -0.273160210918771, 
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0.75119058002662, 0.546320421837542, -0.273160210918771, -0.273160210918771, 
0.75119058002662, -0.273160210918771, -0.0682900527296927, 0.136580105459385, 
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0.136580105459385, 0.75119058002662, 0.75119058002662, -0.478030369107849, 
0.136580105459385, 0.75119058002662, -0.273160210918771, -0.682900527296927, 
-0.273160210918771, 0.136580105459385, 0.546320421837542, -0.682900527296927, 
-0.478030369107849, 0.136580105459385, -0.682900527296927, -0.0682900527296927, 
-0.478030369107849, 0.136580105459385, -0.887770685486005, -0.273160210918771, 
-0.0682900527296927, -0.273160210918771, -0.887770685486005, 
0.546320421837542, 0.546320421837542, -0.478030369107849, -0.273160210918771, 
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0.341450263648464, 0.136580105459385, 0.136580105459385, 0.75119058002662, 
0.136580105459385, -0.0682900527296927, 0.546320421837542, -0.0682900527296927, 
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0.546320421837542, 0.75119058002662, -0.273160210918771, 0.341450263648464, 
-0.273160210918771, 0.136580105459385, 0.546320421837542, 0.546320421837542, 
0.136580105459385, 0.136580105459385, -0.682900527296927, 0.341450263648464, 
0.341450263648464, -0.273160210918771, -0.682900527296927, -0.0682900527296927, 
0.75119058002662, -0.887770685486005, -0.478030369107849, -0.273160210918771, 
-0.478030369107849, -0.478030369107849, 0.136580105459385, -0.478030369107849, 
0.136580105459385, -0.478030369107849, 0.136580105459385, -0.0682900527296927, 
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0.75119058002662, -0.682900527296927, 0.75119058002662, 0.75119058002662, 
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0.136580105459385, 0.136580105459385, 0.136580105459385, 0.136580105459385, 
0.546320421837542, 0.546320421837542, -0.0682900527296927, 0.75119058002662, 
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0.75119058002662, 0.546320421837542, 0.341450263648464, -0.887770685486005, 
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0.75119058002662, -0.0682900527296927, 0.136580105459385, 0.546320421837542, 
0.546320421837542, -0.478030369107849, -0.273160210918771, 0.546320421837542, 
-0.478030369107849, -0.682900527296927, 0.75119058002662, -0.0682900527296927, 
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0.136580105459385, 0.75119058002662, -0.0682900527296927, -0.682900527296927, 
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-0.478030369107849, 0.546320421837542, 0.136580105459385, -0.887770685486005, 
0.75119058002662, -0.0682900527296927, 0.75119058002662, 0.75119058002662, 
-0.273160210918771, -0.682900527296927, 0.546320421837542, 0.546320421837542, 
-0.887770685486005, 0.75119058002662, -0.273160210918771, 0.546320421837542, 
-0.0682900527296927, 0.136580105459385, 0.341450263648464, -0.478030369107849, 
0.136580105459385, 0.136580105459385, -0.273160210918771, 0.546320421837542, 
-0.273160210918771, -0.273160210918771, -0.273160210918771, 0.75119058002662, 
-0.887770685486005, -0.887770685486005, -0.0682900527296927, 
-0.478030369107849, -0.0682900527296927, 0.75119058002662, -0.273160210918771, 
0.136580105459385, -0.478030369107849, -0.273160210918771, 0.136580105459385, 
0.75119058002662, 0.546320421837542, -0.478030369107849, -0.273160210918771, 
-0.273160210918771, 0.136580105459385, -0.273160210918771, -0.0682900527296927, 
0.75119058002662, 0.136580105459385), resp.var = c(2L, 1L, 0L, 
1L, 0L, 0L, 0L, 0L, 0L, 1L, 3L, 1L, 0L, 1L, 0L, 1L, 2L, 0L, 1L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 2L, 
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 2L, 
0L, 3L, 2L, 0L, 2L, 2L, 0L, 0L, 0L, 1L, 1L, 3L, 1L, 2L, 0L, 1L, 
0L, 0L, 1L, 0L, 2L, 0L, 2L, 4L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 2L, 
3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 2L, 
0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 2L, 0L, 1L, 0L, 4L, 1L, 0L, 
1L, 1L, 0L, 0L, 0L, 1L, 3L, 0L, 2L, 0L, 0L, 2L, 1L, 0L, 0L, 2L, 
0L, 0L, 0L, 2L, 0L, 0L, 3L, 0L, 0L, 2L, 1L, 1L, 0L, 0L, 3L, 1L, 
1L, 2L, 0L, 2L, 0L, 2L, 2L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 2L, 2L, 1L, 0L, 0L, 1L, 
0L, 0L, 0L, 0L, 6L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 
1L, 0L, 0L, 1L, 3L, 1L, 0L, 2L, 3L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 
0L, 0L, 0L, 0L, 1L, 2L, 1L, 1L, 0L, 0L, 2L, 0L, 2L, 0L, 0L, 1L, 
1L, 0L, 0L, 2L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 
0L, 1L, 0L, 2L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 
0L, 3L, 0L, 0L, 3L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 2L, 1L, 1L, 0L, 2L, 2L, 0L, 2L, 1L, 0L, 2L, 0L, 0L, 0L, 0L, 
3L, 0L, 2L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 2L, 0L, 1L, 1L, 0L, 1L, 
0L, 3L, 1L, 3L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 2L, 0L, 
2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 2L, 0L, 3L, 0L, 0L, 0L, 
0L, 1L, 0L, 0L, 3L, 1L, 1L, 2L, 0L, 0L, 3L, 0L, 0L, 0L, 1L, 1L, 
0L, 1L, 3L, 0L, 2L, 0L, 0L, 1L, 3L, 1L, 0L, 0L, 4L, 3L, 0L, 2L, 
0L, 0L, 0L, 3L, 0L, 0L, 2L, 3L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 
0L, 0L, 0L, 3L, 3L, 2L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 
0L, 0L, 0L, 1L, 0L, 2L, 0L, 0L, 1L, 0L, 0L, 1L, 2L, 0L, 1L, 0L, 
2L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 3L, 1L, 0L, 0L, 0L, 0L, 0L, 
1L, 2L, 0L, 2L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 
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1L, 1L, 0L, 0L, 0L, 2L, 0L, 1L, 0L, 3L, 0L, 2L, 0L, 0L, 0L, 2L, 
0L), rand.eff = c(37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
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37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
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37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
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40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
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40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
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43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L)), .Names = c("cat.var", 
"cont.var", "resp.var", "rand.eff"), row.names = c(NA, 500L), class = "data.frame")

Answer 1

以下是各种答案（顺便说一句，您上面的数据框中有一些缺少的引号，必须手动修复...）

适合模特：

library(lme4)
fit <- glmer(resp.var ~ cont.var:cat.var + (1|rand.eff) ,
           data = sample.data , poisson)

（请注意，这是一个有点奇怪的模型规范 - 强制所有类别在cont.var==0处具有相同的值。您的意思是cont.var*cat.var吗？

library(ggplot2)
theme_update(theme_bw())  ## set white rather than gray background

快速而肮脏的线性回归：

ggplot(sample.data,aes(cont.var,resp.var,linetype=cat.var))+
    geom_smooth(method="lm",se=FALSE)

现在使用Poisson GLM（但不包含随机效应），并显示数据点：

ggplot(sample.data,aes(cont.var,resp.var,colour=cat.var))+
    stat_sum(aes(size=..n..),alpha=0.5)+
    geom_smooth(method="glm",family="poisson")

下一位需要lme4的开发（r-forge）版本，该版本具有predict方法：

设置预测数据框：

predframe <- with(sample.data,
                  expand.grid(cat.var=levels(cat.var),
                              cont.var=seq(min(cont.var),
                              max(cont.var),length=51)))

预测人口水平（REform=NA），线性预测器（logit）量表（这是你在图上获得直线的唯一方法）

predframe$pred.logit <- predict(fit,newdata=predframe,REform=NA)

minmaxvals <- range(sample.data$cont.var)

ggplot(predframe,aes(cont.var,pred.logit,linetype=cat.var))+geom_line()+
    geom_point(data=subset(predframe,cont.var %in% minmaxvals),
               aes(shape=cat.var))

enter image description here 现在在响应规模上：

predframe$pred <- predict(fit,newdata=predframe,REform=NA,type="response")
ggplot(predframe,aes(cont.var,pred,linetype=cat.var))+geom_line()+
    geom_point(data=subset(predframe,cont.var %in% minmaxvals),
               aes(shape=cat.var))

enter image description here

Answer 2

jtools包（CRAN link）可以使这种模型的绘图非常简单。我是该软件包的开发者。

我们将像Ben在答案中所做的那样适合这个模型：

library(lme4)
fit <- glmer(resp.var ~ cont.var:cat.var + (1 | rand.eff),
             data = sample.data, family = poisson)

使用jtools我们只需使用interact_plot函数：

library(jtools)
interact_plot(fit, pred = cont.var, modx = cat.var)

结果：

默认情况下，它会在响应比例上绘制，但您可以使用outcome.scale = "link"参数在线性比例上绘制（默认为"response"）。

Answer 3

effects软件包支持lme4模型，应该可以执行您想要的操作。

效果：线性，广义线性和其他模型的效果显示

图形和表格效果显示，例如，相互作用，用于具有线性预测变量的各种统计模型。

它还带有两个稍微过时的papers（您可以将它们视为小插曲）。

R：与GLMM（lme4）的连续和分类变量的交互图

3 个答案: