R:具有GLMM(lme4)的连续变量和分类变量的交互作用图 [英] R: Interaction Plot with a continuous and a categorical variable for a GLMM (lme4)

问题描述

我想制作一个交互图，以直观地显示回归模型的结果中分类变量(4个级别)和标准化连续变量的交互斜率的差异或相似性.

I would like to make an interaction plot to visually display the difference or similarity in slopes of interaction of a categorical variable (4 levels) and a standardized continuous variable from the results of a regression model.

with(GLMModel, interaction.plot(continuous.var, categorical.var, response.var)) 不是我要找的东西.它产生了一个曲线图，其中斜率针对连续变量的每个值而变化.我正在寻找一个斜率恒定的图，如下图所示:

with(GLMModel, interaction.plot(continuous.var, categorical.var, response.var)) Is not what I am looking for. It produces a plot in which the slope changes for each value of the continuous variable. I'm looking to make a plot with constant slopes as in the following plot:

有什么想法吗?

我适合以下形式的模型:fit<-glmer(resp.var ~ cont.var*cat.var + (1|rand.eff) , data = sample.data , poisson) 这是一些示例数据:

I fit a model of the form fit<-glmer(resp.var ~ cont.var*cat.var + (1|rand.eff) , data = sample.data , poisson) Here is some sample data:

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, 
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0.136580105459385, -0.0682900527296927, -0.273160210918771, -0.273160210918771, 
0.341450263648464, 0.75119058002662, -0.682900527296927, -0.0682900527296927, 
-0.273160210918771, -0.887770685486005, -0.0682900527296927, 
0.75119058002662, 0.546320421837542, 0.75119058002662, 0.75119058002662, 
-0.887770685486005, 0.341450263648464, 0.75119058002662, -0.887770685486005, 
0.136580105459385, -0.273160210918771, 0.546320421837542, 0.546320421837542, 
-0.682900527296927, 0.75119058002662, 0.136580105459385, -0.0682900527296927, 
-0.478030369107849, 0.75119058002662, -0.478030369107849, 0.341450263648464, 
0.136580105459385, -0.0682900527296927, -0.478030369107849, -0.0682900527296927, 
-0.0682900527296927, 0.546320421837542, -0.273160210918771, 0.75119058002662, 
0.341450263648464, 0.546320421837542, -0.478030369107849, 0.136580105459385, 
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-0.0682900527296927, 0.546320421837542, -0.887770685486005, -0.0682900527296927, 
-0.682900527296927, -0.478030369107849, -0.478030369107849, -0.682900527296927, 
0.75119058002662, 0.341450263648464, -0.0682900527296927, 0.341450263648464, 
-0.0682900527296927, -0.887770685486005, -0.887770685486005, 
-0.273160210918771, -0.0682900527296927, 0.546320421837542, -0.0682900527296927, 
-0.0682900527296927, 0.75119058002662, -0.0682900527296927, -0.273160210918771, 
-0.478030369107849, 0.546320421837542, 0.546320421837542, 0.546320421837542, 
0.341450263648464, 0.136580105459385, -0.478030369107849, 0.136580105459385, 
0.136580105459385, 0.136580105459385, -0.478030369107849, -0.273160210918771, 
-0.273160210918771, -0.273160210918771, 0.341450263648464, -0.273160210918771, 
-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, 
-0.0682900527296927, -0.887770685486005, -0.887770685486005, 
0.546320421837542, -0.273160210918771, 0.546320421837542, -0.478030369107849, 
0.546320421837542, -0.0682900527296927, 0.75119058002662, -0.273160210918771, 
0.546320421837542, 0.341450263648464, -0.0682900527296927, -0.0682900527296927, 
-0.0682900527296927, -0.887770685486005, 0.136580105459385, -0.273160210918771, 
-0.478030369107849, 0.75119058002662, 0.341450263648464, 0.546320421837542, 
-0.273160210918771, 0.546320421837542, 0.75119058002662, -0.273160210918771, 
0.75119058002662, 0.546320421837542, -0.273160210918771, -0.273160210918771, 
0.75119058002662, -0.273160210918771, -0.0682900527296927, 0.136580105459385, 
-0.478030369107849, 0.75119058002662, 0.75119058002662, -0.887770685486005, 
-0.887770685486005, 0.546320421837542, -0.682900527296927, -0.887770685486005, 
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, 
-0.0682900527296927, 0.136580105459385, -0.478030369107849, 0.75119058002662, 
0.341450263648464, 0.136580105459385, 0.136580105459385, 0.75119058002662, 
0.136580105459385, -0.0682900527296927, 0.546320421837542, -0.0682900527296927, 
-0.887770685486005, 0.75119058002662, 0.75119058002662, 0.546320421837542, 
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0.546320421837542, -0.273160210918771, 0.75119058002662, -0.0682900527296927, 
0.546320421837542, -0.0682900527296927, -0.273160210918771, 0.546320421837542, 
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-0.273160210918771, -0.0682900527296927, -0.478030369107849, 
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-0.273160210918771, 0.136580105459385, 0.546320421837542, 0.546320421837542, 
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0.341450263648464, -0.273160210918771, -0.682900527296927, -0.0682900527296927, 
0.75119058002662, -0.887770685486005, -0.478030369107849, -0.273160210918771, 
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-0.682900527296927, 0.75119058002662, -0.478030369107849, -0.682900527296927, 
0.341450263648464, -0.887770685486005, -0.478030369107849, 0.546320421837542, 
-0.887770685486005, -0.478030369107849, -0.478030369107849, 0.341450263648464, 
0.75119058002662, -0.682900527296927, 0.75119058002662, 0.75119058002662, 
0.341450263648464, -0.0682900527296927, 0.546320421837542, -0.0682900527296927, 
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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-0.887770685486005, 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, 
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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, 
0L, 0L, 3L, 2L, 2L, 0L, 1L, 0L, 5L, 0L, 4L, 2L, 0L, 3L, 0L, 0L, 
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, 
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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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, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 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, 
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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")

推荐答案

以下是各种答案(顺便说一句，您在上面的数据框中有一些缺少的引号，必须手动对其进行修复...)

Here's an answer of sorts (by the way, you had some missing quotation marks in your data frame above, which had to be fixed manually ...)

适合模型:

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.您是说cont.var*cat.var吗?

(Note that this is a slightly weird model specification -- forces all categories to have the same value at cont.var==0. Did you mean cont.var*cat.var?

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

快速而肮脏的线性回归:

Quick and dirty linear regressions:

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

现在具有Poisson GLM(但不包含随机效应)，并显示数据点:

Now with a Poisson GLM (but not incorporating the random effect), and showing the data points:

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:

The next bit requires the development (r-forge) version of lme4, which has a predict method:

设置用于预测的数据帧:

Set up data frame for prediction:

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

以线性预测值(对数)尺度在人口水平(REform=NA)上进行预测(这是在绘图上获得直线的唯一方法)

Predict at population level (REform=NA), on the linear predictor (logit) scale (this is the only way you will get straight lines on the plot)

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))

现在在响应规模上:

Now on the response scale:

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))

这篇关于R:具有GLMM(lme4)的连续变量和分类变量的交互作用图的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！